qualitative research for high school students

100 Qualitative Research Titles For High School Students

Are you brainstorming for excellent qualitative research titles for your high school curriculum? If yes, then this blog is for you! Academic life throws a lot of thesis and qualitative research papers and essays at you. Although thesis and essays may not be much of a hassle. However, when it comes to your research paper title, you must ensure that it is qualitative, and not quantitative. 

Qualitative research is primarily focused on obtaining data through case studies, artifacts, interviews, documentaries, and other first-hand observations. It focuses more on these natural settings rather than statistics and numbers. If you are finding it difficult to find a topic, then worry not because the high schooler has this blog post curated for you with 100 qualitative research titles that can help you get started!

Qualitative research prompts for high schoolers

Qualitative research papers are written by gathering and analyzing non-numerical data. Generally, teachers allot a list of topics that you can choose from. However, if you aren’t given the list, you need to search for a topic for yourself.

Qualitative research topics mostly deal with the happenings in society and nature. There are endless topics that you can choose from. We have curated a list of 100 qualitative research titles for you to choose from. Read on and pick the one that best aligns with your interests!

• Why is there a pressing need for wildlife conservation?
• Discuss the impacts of climate change on future generations. 
• Discuss the impact of overpopulation on sustainable resources.
• Discuss the factors considered while establishing the first 10 engineering universities in the world.
• What is the contribution of AI to emotional intelligence? Explain. 
• List out the effective methods to reduce the occurrences of fraud through cybercrimes.
• With case studies, discuss some of the greatest movements in history leading to independence. 
• Discuss real-life scenarios of gender-based discrimination. 
• Discuss disparities in income and opportunities in developing nations. 
• How to deal with those dealing with ADHD?
• Describe how life was before the invention of the air conditioner. 
• Explain the increasing applications of clinical psychology. 
• What is psychology? Explain the career opportunities it brings forth for youngsters.
• Covid lockdown: Is homeschooling the new way to school children?
• What is the role of army dogs? How are they trained for the role?
• What is feminism to you? Mention a feminist and his/her contributions to making the world a better place for women.  
• What is true leadership quality according to you? Explain with a case study of a famous personality you admire for their leadership skills. 
• Is wearing a mask effective in preventing covid-19? Explain the other practices that can help one prevent covid-19. 
• Explain how teachers play an important role in helping students with disabilities improve their learning.
• Is ‘E business’ taking over traditional methods of carrying out business?
• What are the implications of allowing high schoolers to use smartphones in classes?
• Does stress have an effect on human behavior?
• Explain the link between poverty and education. 
• With case studies, explain the political instability in developing nations.
• Are ‘reality television shows’ scripted or do they showcase reality?
• Online vs Offline teaching: which method is more effective and how?
• Does there exist an underlying correlation between education and success? Explain with case studies.
• Explain the social stigma associated with menstruation. 
• Are OTT entertainment platforms like Netflix and Amazon Prime beneficial in any other way?
• Does being physically active help reverse type 2 diabetes?
• Does pop culture influence today’s youth and their behavior?
• ‘A friend in need is a friend in deed.’ Explain with case studies of famous personalities. 
• Do books have greater importance in the lives of children from weaker economic backgrounds? Explain in detail.
• Give an overview of the rise of spoken arts. 
• Explain the problem of food insecurity in developing nations.
• How related are Windows and Apple products?
• Explore the methods used in schools to promote cultural diversity. 
• Has social media replaced the physical social engagement of children in society?
• Give an overview of allopathic medicine in treating mental disorders. 
• Explain if and how willpower plays a role in overcoming difficulties in life. 
• Are third-world countries seeing a decline in academic pursuit? Explain with real-life scenarios. 
• Can animals predict earthquakes in advance? Explain which animals have this ability and how they do it. 
• Discuss if the education system in America needs to improve. If yes, list out how this can be achieved.
• Discuss democracy as a government of the people, by the people, and for the people.’
• Discuss the increasing rate of attention deficit disorder among children.
• Explain fun games that can help boost the morale of kids with dyslexia. 
• Explain the causes of youth unemployment.
• Explain some of the ways you think might help in making differently-abled students feel inclusive in the mainstream.
• Explain in detail the challenges faced by students with special needs to feel included when it comes to accessibility to education.
• Discuss the inefficiency of the healthcare system brought about by the covid-19 pandemic. 
• Does living in hostels instill better life skills among students than those who are brought up at home? Explain in detail. 
• What is Advanced Traffic Management? Explain the success cases of countries that have deployed it.  
• Elaborate on the ethnic and socioeconomic reasons leading to poor school attendance in third-world nations.
• Do preschoolers benefit from being read to by their parents? Discuss in detail.
• What is the significance of oral learning in classrooms?
• Does computer literacy promise a brighter future? Analyze. 
• What people skills are enhanced in a high school classroom?
• Discuss in detail the education system in place of a developing nation. Highlight the measures you think are impressive and those that you think need a change. 
• Apart from the drawbacks of UV rays on the human body, explain how it has proven to be beneficial in treating diseases.  
• Discuss why or why not wearing school uniforms can make students feel included in the school environment. 
• What are the effective ways that have been proven to mitigate child labor in society? 
• Explain the contributions of arts and literature to the evolving world. 
• How do healthcare organizations cope with patients living with transmissive medical conditions?
• Why do people with special abilities still face hardships when it comes to accessibility to healthcare and education?
• What are the prevailing signs of depression in small children?
• How to identify the occurrences and onset of autism in kids below three years of age?
• Explain how SWOT and PESTLE analysis is important for a business.
• Why is it necessary to include mental health education in the school curriculum?
• What is adult learning and does it have any proven benefits?
• What is the importance of having access to libraries in high school?
• Discuss the need for including research writing in school curriculums. 
• Explain some of the greatest non-violent movements of ancient history. 
• Explain the reasons why some of the species of wildlife are critically endangered today. 
• How is the growing emission of co2 bringing an unprecedented change in the environment?
• What are the consequences of an increasing population in developing nations like India? Discuss in detail. 
• Are remote tests as effective as in-class tests? 
• Explain how sports play a vital role in schools. 
• What do you understand about social activities in academic institutions? Explain how they pose as a necessity for students. 
• Are there countries providing free healthcare? How are they faring in terms of their economy? Discuss in detail. 
• State case studies of human lives lost due to racist laws present in society.
• Discuss the effect of COVID-19 vaccines in curbing the novel coronavirus.
• State what according to you is more effective: e-learning or classroom-based educational systems.
• What changes were brought into the e-commerce industry by the COVID-19 pandemic?
• Name a personality regarded as a youth icon. Explain his or her contributions in detail.
• Discuss why more and more people are relying on freelancing as a prospective career. 
• Does virtual learning imply lesser opportunities? What is your take?
• Curbing obesity through exercise: Analyze.
• Discuss the need and importance of health outreach programs.
• Discuss in detail how the upcoming generation of youngsters can do its bit and contribute to afforestation.
• Discuss the 2020 budget allocation of the United States. 
• Discuss some of the historic ‘rags to riches’ stories.
• What according to you is the role of nurses in the healthcare industry?
• Will AI actually replace humans and eat up their jobs? Discuss your view and also explain the sector that will benefit the most from AI replacing humans. 
• Is digital media taking over print media? Explain with case studies. 
• Why is there an increasing number of senior citizens in the elderly homes? 
• Are health insurances really beneficial? 
• How important are soft skills? What role do they play in recruitment? 
• Has the keto diet been effective in weight loss? Explain the merits and demerits. 
• Is swimming a good physical activity to curb obesity? 
• Is work from home as effective as work from office? Explain your take. 

Tips to write excellent qualitative research papers

Now that you have scrolled through this section, we trust that you have picked up a topic for yourself from our list of 100 brilliant qualitative research titles for high school students. Deciding on a topic is the very first step. The next step is to figure out ways how you can ensure that your qualitative research paper can help you grab top scores. 

Once you have decided on the title, you are halfway there. However, deciding on a topic signals the next step, which is the process of writing your qualitative paper. This poses a real challenge! 

To help you with it, here are a few tips that will help you accumulate data irrespective of the topic you have chosen. Follow these four simple steps and you will be able to do justice to the topic you have chosen!

• Create an outline based on the topic. Jot down the sub-topics you would like to include. 
• Refer to as many sources as you can – documentaries, books, news articles, case studies, interviews, etc. Make a note of the facts and phrases you would like to include in your research paper. 
• Write the body. Start adding qualitative data. 
• Re-read and revise your paper. Make it comprehensible. Check for plagiarism, and proofread your research paper. Try your best and leave no scope for mistakes. 

Wrapping it up!

To wrap up, writing a qualitative research paper is almost the same as writing other research papers such as argumentative research papers , English research papers , Biology research papers , and more. Writing a paper on qualitative research titles promotes analytical and critical thinking skills among students. Moreover,  it also helps improve data interpretation and writing ability, which are essential for students going ahead.

Having a 10+ years of experience in teaching little budding learners, I am now working as a soft skills and IELTS trainers. Having spent my share of time with high schoolers, I understand their fears about the future. At the same time, my experience has helped me foster plenty of strategies that can make their 4 years of high school blissful. Furthermore, I have worked intensely on helping these young adults bloom into successful adults by training them for their dream colleges. Through my blogs, I intend to help parents, educators and students in making these years joyful and prosperous.

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Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

189+ Most Exciting Qualitative Research Topics For Students

Researchers conduct qualitative studies to gain a holistic understanding of the topic under investigation. Analyzing qualitative? Looking for the best qualitative research topics? 

If yes, you are here at the right place. We are discussing here all the topics in every field. Basically, qualitative research is the most valuable approach within the fields of social sciences, humanities, and various other fields. 

Qualitative research uses a wide array of methods such as interviews, focus groups, participant observation, content analysis, and case studies. Even among others, to gather and analyze non-numerical data. 

In this blog, we will explore the diverse, most interesting qualitative research topics, highlighting their importance. Whether you are a student, a scholar, or a practitioner in your field, these best qualitative research ideas are most helpful for you.

Must Read: 21 Ways To Get Good Grades In College

What Is Qualitative Research?

Table of Contents

Qualitative research is a systematic and exploratory approach to research that focuses on understanding and interpreting the complexities of human experiences, behaviors, and phenomena. It aims to provide in-depth insights into the “how” and “why” of various issues by examining them in their natural settings and contexts. Unlike quantitative research, which primarily deals with numerical data and statistical analysis, qualitative research relies on non-numerical data such as interviews, observations, textual analysis, and participant narratives to uncover deeper meanings and patterns.

Key Characteristics of Qualitative Research

• Purpose : Qualitative research is used to delve into new or less understood areas, aiming to generate new hypotheses and theories.
• Example : Investigating the coping mechanisms of natural disaster survivors in regions where little prior research exists.
• Purpose : It seeks to understand and interpret participants’ perspectives, emotions, and experiences.
• Example : Examining how individuals with chronic pain perceive their condition and the medical care they receive.
• Purpose : Highlights the importance of context and situational factors in shaping human behavior and experiences.
• Example : Exploring how cultural backgrounds influence parenting styles in different communities.
• Purpose : Employs adaptable and open-ended data collection methods that evolve as new insights are gained.
• Example : Conducting semi-structured interviews where the researcher can ask follow-up questions based on participants’ answers.
• Purpose : Generates detailed, narrative data that offer deep insights into the studied topic, capturing the complexity of human experiences.
• Example : Collecting and analyzing detailed personal stories to understand career development and personal growth.

These characteristics enable qualitative research to provide a comprehensive, nuanced understanding of complex issues, often revealing insights that quantitative approaches might miss.

8 Great Tips On How To Choose Good Qualitative Research Topics

Here are some tips to help you select strong qualitative research topics:

1. Personal Interest and Passion: Start by considering what genuinely interests and excites you. Your enthusiasm for the topic will sustain your motivation throughout the research process.

2. Relevance: Ensure that your chosen topic is relevant to your field of study or the discipline you are working within. It should contribute to existing knowledge or address a meaningful research gap.

3. Research Gap Identification: Review relevant literature and research to identify gaps or areas where there is limited qualitative research. Look for unanswered questions or underexplored aspects of a particular subject.

4. Feasibility: Assess whether the topic is feasible within the scope of your research project. Consider factors like available time, resources, and access to potential participants or data sources.

5. Clarity and Specificity: Your research topic should be clear, specific, and well-defined. Avoid overly broad topics that are difficult to explore in depth. Narrow it down to a manageable focus.

6. Significance: Ask yourself why your research topic matters. Consider the potential implications and applications of your findings. How might your research contribute to understanding, policy, or practice?

7. Originality: Aim for a unique angle or perspective on the topic. While you can build on existing research, strive to offer a fresh viewpoint or new insights.

8. Researchable : Ensure that your topic is researchable using qualitative methods. It should allow you to collect relevant data and answer research questions effectively.

137+ Most Exciting Qualitative Research Topics For All Students  

Qualitative research topics in health and medicine.

• Experiences of healthcare workers during the COVID-19 pandemic.
• Perceptions of alternative medicine among cancer patients.
• Coping mechanisms of individuals with chronic illnesses.
• The impact of telemedicine on patient-doctor relationships.
• Barriers to mental health treatment-seeking among minority populations.
• Qualitative analysis of patient experiences with organ transplantation.
• Decision-making processes of families regarding end-of-life care.

Qualitative Research Topics In Education

• The role of parental involvement in student academic achievement.
• Teacher perceptions of remote learning during a pandemic.
• Peer influence on academic motivation and performance.
• Exploring the experiences of homeschooling families.
• The impact of technology on the classroom environment.
• Factors influencing student dropout rates in higher education.

Qualitative Research Topics In Psychology and Mental Health

• Understanding the stigma associated with seeking therapy.
• Experiences of individuals living with anxiety disorders.
• Perceptions of body image among adolescents.
• Coping strategies of survivors of traumatic events.
• The impact of social support on mental health recovery.
• Narratives of individuals with eating disorders.

Qualitative Research Topics In Sociology and Culture

• Experiences of immigrants in adapting to a new culture.
• The role of social media in shaping cultural identities.
• Perceptions of police-community relations in marginalized communities.
• Gender dynamics in the workplace and career progression.
• Qualitative analysis of online dating experiences.
• Narratives of LGBTQ+ individuals coming out to their families.

Qualitative Research Topics In Technology and Society

• User experiences with augmented reality applications.
• Perceptions of online privacy and data security.
• The impact of social media on political activism.
• Ethical considerations in artificial intelligence development.
• Qualitative analysis of online gaming communities.
• Experiences of individuals participating in virtual reality environments.

Qualitative Research Topics In Environmental Studies

• Public perceptions of climate change and environmental policies.
• Experiences of individuals involved in sustainable living practices.
• Qualitative analysis of environmental activism movements.
• Community responses to natural disasters and climate change.
• Perspectives on wildlife conservation efforts.

Qualitative Research Topics In Business and Economics

• Qualitative analysis of consumer behavior and brand loyalty.
• Entrepreneurial experiences of women in male-dominated industries.
• Factors influencing small business success or failure.
• Corporate social responsibility and its impact on consumer trust.
• Experiences of employees in remote work settings.

Qualitative Research Topics In Politics and Governance

• Perceptions of voter suppression and electoral integrity.
• Experiences of political activists in grassroots movements.
• The role of social media in shaping political discourse.
• Narratives of individuals involved in civil rights movements.
• Qualitative analysis of government responses to crises.

Qualitative Research Topics In Family and Relationships

• Experiences of couples in long-distance relationships.
• Parenting styles and their impact on child development.
• Sibling dynamics and their influence on individual development.
• Narratives of individuals in arranged marriages.
• Experiences of single parents in raising their children.

Qualitative Research Topics In Art and Culture

• Qualitative analysis of the impact of art therapy on mental health.
• Experiences of artists in exploring social and political themes.
• Perceptions of cultural appropriation in the arts.
• Narratives of individuals involved in the hip-hop culture.
• The role of art in preserving cultural heritage.

Qualitative Research Topics In Crime and Justice

• Experiences of formerly incarcerated individuals reentering society.
• Perceptions of racial profiling and police violence.
• Qualitative analysis of restorative justice programs.
• Narratives of victims of cyberbullying.
• Perspectives on juvenile justice reform.

Qualitative Research Topics In Sports and Recreation

• Experiences of athletes in overcoming career-threatening injuries.
• The role of sports in building resilience among youth.
• Perceptions of performance-enhancing drugs in professional sports.
• Qualitative analysis of sports fandom and its impact on identity.
• Narratives of individuals involved in adaptive sports.

Qualitative Research Topics In History and Heritage

• Experiences of descendants of historical events or figures.
• Perceptions of cultural preservation and heritage conservation.
• Narratives of individuals connected to indigenous cultures.
• The impact of oral history on preserving traditions.
• Qualitative analysis of historical reenactment communities.

Qualitative Research Topics In Religion and Spirituality

• Experiences of individuals who have undergone religious conversion.
• Perceptions of spirituality and well-being.
• The role of religion in shaping moral values and ethics.
• Narratives of individuals who have left religious communities.
• Qualitative analysis of interfaith dialogue and cooperation.

Qualitative Research Topics In Travel and Tourism

• Experiences of solo travelers in foreign countries.
• Perceptions of sustainable tourism practices.
• Qualitative analysis of cultural immersion through travel.
• Narratives of individuals on pilgrimages or spiritual journeys.
• Experiences of individuals living in tourist destinations.

Qualitative Research Topics In Human Rights and Social Justice

• Narratives of human rights activists in advocating for change.
• Experiences of refugees and asylum seekers.
• Perceptions of income inequality and wealth distribution.
• Qualitative analysis of anti-discrimination campaigns.
• Perspectives on global efforts to combat human trafficking.

Qualitative Research Topics In Aging and Gerontology

• Experiences of individuals in assisted living facilities.
• Perceptions of aging and quality of life in older adults.
• Narratives of caregivers for elderly family members.
• The impact of intergenerational relationships on well-being.
• Qualitative analysis of end-of-life decisions and hospice care.

Qualitative Research Topics In Language and Communication

• Experiences of individuals learning a second language.
• Perceptions of non-verbal communication in cross-cultural interactions.
• Narratives of people who communicate primarily through sign language.
• The role of language in shaping identity and belonging.
• Qualitative analysis of online communication in virtual communities.

Qualitative Research Topics In Media and Entertainment

• Experiences of content creators in the digital media industry.
• Perceptions of representation in the film and television industry.
• The impact of music on emotional well-being and identity.
• Narratives of individuals involved in fan communities.
• Qualitative analysis of the effects of binge-watching on mental health.

Qualitative Research Topics In Ethics and Morality

• Experiences of individuals faced with ethical dilemmas.
• Perceptions of moral relativism and cultural differences.
• Narratives of whistleblowers in exposing corporate misconduct.
• The role of empathy in ethical decision-making.
• Qualitative analysis of the ethics of artificial intelligence.

Qualitative Research Topics In Technology and Education

• Experiences of teachers integrating technology in the classroom.
• Perceptions of online learning and its effectiveness.
• The impact of educational apps on student engagement.
• Narratives of students with disabilities using assistive technology.
• Qualitative analysis of the digital divide in education.

Qualitative Research Topics In Gender and Sexuality

• Experiences of transgender individuals in transitioning.
• Perceptions of gender roles and expectations.
• Narratives of individuals in same-sex relationships.
• The impact of intersectionality on experiences of gender and sexuality.
• Qualitative analysis of gender-based violence and advocacy.

Qualitative Research Topics In Migration and Diaspora

• Experiences of immigrants in maintaining cultural ties to their home country.
• Perceptions of identity among second-generation immigrants.
• Narratives of refugees resettling in new countries.
• The role of diaspora communities in supporting homeland causes.
• Qualitative analysis of immigration policies and their impact on families.

Qualitative Research Topics In Food and Nutrition

• Experiences of individuals with specific dietary restrictions.
• Perceptions of food sustainability and ethical consumption.
• Narratives of people with eating disorders seeking recovery.
• The role of food in cultural identity and traditions.
• Qualitative analysis of food insecurity and hunger relief efforts.

Qualitative Research Topics In Urban Studies and Community Development

• Experiences of residents in gentrifying neighborhoods.
• Perceptions of community engagement and empowerment.
• Narratives of individuals involved in urban farming initiatives.
• The impact of housing policies on homelessness.
• Qualitative analysis of neighborhood safety and crime prevention.

Qualitative Research Topics In Science and Technology Ethics

• Experiences of scientists in navigating ethical dilemmas.
• Perceptions of scientific responsibility in climate change research.
• Narratives of whistleblowers in scientific misconduct cases.
• The role of ethics in emerging technology development.
• Qualitative analysis of the ethics of genetic engineering.

Qualitative Research Topics In Social Media and Online Communities

• Experiences of individuals in online support groups.
• Perceptions of social media’s influence on self-esteem.
• Narratives of social media influencers and their impact.
• The role of online communities in social and political movements.
• Qualitative analysis of cyberbullying and online harassment.

Qualitative Research Topics in Daily Life

• The Impact of Social Media on Personal Relationships and Well-being.
• Exploring the Experience of Remote Work during the COVID-19 Pandemic.
• Perceptions of Sustainable Living Practices Among Urban Dwellers.
• Qualitative Analysis of Food Choices and Eating Habits in a Fast-paced Society.
• Understanding the Motivations and Barriers to Physical Activity Among Adults.

Qualitative Research Topics for Students

• Student Perceptions of Online Learning: Challenges and Opportunities.
• Peer Pressure and Decision-making Among Adolescents.
• Exploring the Transition from High School to College: Student Experiences.
• The Role of Extracurricular Activities in Student Development.
• Motivations and Challenges of Student Entrepreneurs in Starting Their Businesses.

Qualitative Research Topics for STEM Students

Here are some original qualitative research topic ideas for STEM students:

• Exploring the Ethical Implications of AI in Healthcare Decision-Making : Investigate healthcare professionals’ ethical perspectives and decision-making processes regarding the use of AI technologies in clinical settings.
• Gender Dynamics in STEM Education and Career Aspirations : Analyze how gender influences students’ educational experiences and career choices in STEM fields at the university level.
• Public Perception and Acceptance of Genome Editing Technologies : Conduct interviews and surveys to understand public attitudes and concerns about genome editing technologies such as CRISPR.
• Effectiveness of Online Interactive Tools in Teaching Middle School Mathematics : Explore how digital tools impact student learning and engagement in middle school mathematics education.
• Community Engagement and Impact of Renewable Energy Projects : Investigate community perceptions, concerns, and benefits related to local renewable energy initiatives like wind farms or solar installations.
• Challenges and Opportunities in Adopting Blockchain Technology in Supply Chain Management : Interview industry professionals to identify barriers and opportunities for integrating blockchain into supply chain operations.
• Decision-Making Processes in Software Development Methodologies : Explore how software engineers and development teams choose between different methodologies (e.g., Agile, Waterfall) and the factors influencing these decisions.
• Cross-Cultural Perspectives on Space Exploration : Analyze interviews with stakeholders from different cultural backgrounds to understand diverse perspectives on space exploration missions and collaborations.
• User Experience and Usability of Wearable Health Monitoring Devices : Conduct qualitative usability studies and interviews to evaluate user experiences with wearable health monitoring technologies.
• Impact of Virtual Reality on Engineering Design Processes : Study how virtual reality tools influence the design process, collaboration among engineering teams, and project outcomes.

These research topics for stem students qualitative to explore a wide range of social, ethical, cultural, and practical dimensions within their fields of study, providing opportunities for meaningful qualitative research.

Qualitative Research Titles Examples

• “Voices of Resilience: Narratives of Cancer Survivors.”
• “Exploring Cultural Identity Among Immigrant Communities.”
• “From Addiction to Recovery: Life Stories of Former Substance Abusers.”
• “Inside the Classroom: Student and Teacher Perspectives on Inclusive Education.”
• “Navigating Caregiving: Experiences of Family Members Caring for Alzheimer’s Patients.”

Qualitative Research Topics in Education

• Teacher Beliefs and Practices in Culturally Responsive Pedagogy.
• Qualitative Study of Bullying Incidents in Elementary Schools.
• Homeschooling: Parent and Student Perspectives on Alternative Education.
• Evaluating the Impact of Technology Integration in Classroom Learning.
• Parental Involvement in Early Childhood Education: A Qualitative Analysis.

Qualitative Research Topics for Nursing Students

• Patient Experiences of Chronic Illness Management.
• The Role of Empathy in Nursing Practice: A Qualitative Study.
• Qualitative Exploration of End-of-Life Care Decision-making.
• Perceptions of Nurse-Patient Communication in Intensive Care Units.
• Nursing Burnout: Causes, Consequences, and Coping Strategies.

Qualitative Research Topics for Human Studies

• Understanding the Impact of Climate Change on Vulnerable Communities.
• The Role of Social Support in Mental Health Recovery.
• Experiences of First-time Homebuyers in the Real Estate Market.
• Exploring the Motivations and Challenges of Volunteering.
• Narratives of Trauma Survivors: Coping and Resilience.

Qualitative Research Topics 2023

• Emerging Trends in Remote Work: Employee Perspectives.
• The Influence of Social Media on Political Engagement in the Post-COVID-19 Era.
• Qualitative Study of Mental Health Stigma Reduction Campaigns.
• Sustainability Practices in Business: Stakeholder Perceptions and Implementation.
• Narratives of Long COVID: The Lived Experience of Survivors.

Qualitative research methods such as interviews, focus groups, participant observation, and content analysis allow researchers to delve deeply into these topics, capturing rich and detailed data that can illuminate complexities, contradictions, and underlying meanings. These methods emphasize understanding context, exploring subjective experiences, and generating nuanced insights that can inform theory-building and contribute to addressing real-world challenges.

10 Major Differences Between Qualitative And Quantitative Research 

Here are the 10 best differences between qualitative and quantitative research:

Consequently, the selection of qualitative research topics is a critical phase in the journey of any researcher or student pursuing qualitative inquiry. The process of choosing the right topic involves a delicate balance of personal passion, research significance, feasibility, and ethical considerations. 

As we’ve discussed, it’s essential to choose a topic that not only resonates with your interests but also contributes to the broader academic or practical discourse. Qualitative research offers a unique lens through which to examine the complexities of human experiences, behaviors, and phenomena. 

It provides the opportunity to delve deep into the “how” and “why” of various subjects, offering nuanced insights that quantitative methods may not capture. Whether you are investigating personal narratives, cultural dynamics, educational practices, or social phenomena, qualitative research allows you to uncover the rich tapestry of human existence.

What is a good topic for qualitative research?

Self-esteem among people from low socioeconomic backgrounds. The advantages of online learning over physical learning.

What are the five topics of qualitative research?

These are biography, ethnography, phenomenology, grounded theory, and case study.

What is the easiest type of qualitative research?

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis.

What are the 4 R’s of qualitative research?

Qualitative social research, whether conducted as ethnography, participant observation, or in situ interviewing, fares poorly when examined by the criteria of representativeness, reactivity, reliability, and replicability.

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• v.20(5); 2017 Oct

A qualitative study exploring high school students’ understanding of, and attitudes towards, health information and claims

Leila cusack.

1 Centre for Research in Evidence‐Based Practice (CREBP), Bond University, Robina, QLD, Australia

Laura N Desha

Chris b del mar, tammy c hoffmann.

Exposure to health claims, particularly in the media and social media, is pervasive, and the information conveyed is often inaccurate, incomplete or misleading. Some young people of high school ages are already making decisions about using readily available health interventions (such as sports drinks and beauty products).Although previous research has assessed adults’ understanding of health claims, no research has examined this issue in young adults who are attending high school.

To explore high school students’ understanding of, and attitudes towards, concepts relevant to assessing health information and claims.

A qualitative study involving semi‐structured interviews with 27 Australian high school students. Responses were recorded, transcribed and a thematic analysis performed. Three themes emerged as follows: (i) Variability in sources of health information and claims, and general understanding of their creation and accuracy of content, (ii) The use of substitute indicators to assess health information and claims and make judgements about their trustworthiness, (iii) Uncertainty about, and literal interpretation of, the language of health claims. Despite general scepticism of health claims and admitted uncertainty of research terminology, many students were generally convinced. Students had poor understanding about how health claims are generated and tended to rely on substitute indicators, such as endorsements, when evaluating the believability of claims.

School students’ lack of awareness of basic health research processes and methods of assessing the accuracy of health information and claims makes them vulnerable to distorted and misleading health information. This restricts their ability to make informed health decisions – a skill that increases in importance as they become adults.

1. BACKGROUND

One consequence of the pervasive presence of mass media is that people are frequently exposed to health claims from varied sources (for example, from the Internet, television, radio and magazines). Many of these claims are inaccurate. 1 , 2 , 3 , 4 , 5 Basing health decisions on misinformation can be harmful to a person's health and a waste of resources of both individuals and health systems. Conversely, mistrust of reliable and accurate health information can also negatively impact upon people's health and resource use. 6 , 7 Knowing how to assess the validity of health claims can empower people to identify accurate health information upon which to base informed decisions.

1.1. Assessing health claims

Health literacy encompasses the ability to gain access to, and interact with, health information in an effective manner, such that good health is promoted and maintained. 8 Nutbeam 9 describes health literacy abilities as ranging from basic to more advanced skills; including functional, interactive and critical health literacy. 9

Most interventions developed to improve health literacy have focused on improving a person's functional health literacy, concentrating on skills such as basic numeracy and literacy, along with knowledge of medical conditions, ensuring the safe use of medications and effective navigation of the health‐care system. 9 Interactive health literacy involves the combination of advanced cognitive, literacy and social skills, to enable a person to extract information from different types of communication and be adaptable in the face of new health information and circumstances. 10 Critical health literacy comprises higher cognitive skills, enabling people to analyse health information and claims and use this information appropriately to overcome barriers to improving health and well‐being. 9 , 11 These specific skills enable people to assess the credibility of health information directly, rather than relying on other indicators of quality, such as the perceived authority of the authors, or source of the information. 12 , 13

Being able to assess claims about the effectiveness of health interventions requires underlying knowledge about the processes involved in testing health interventions and basic research concepts (such as major types of study designs; experimental vs observational studies). Furthermore, for people to be able to assess health information and claims, universally relevant key concepts 14 , 15 , 16 , 17 need to be understood, such as the need for systematic reviews; concepts such as randomization and blinding, the role of chance, placebo effects; and how to interpret results (for example, relative vs absolute risk).

1.2. Critical health literacy for students

As children grow older, parental involvement in decision making decreases and adolescents increasingly assume responsibility for making decisions about their health. However, adolescents may already be making decisions about broader health issues, such as consuming sports drinks, supplements, skin creams or other readily available products that claim to improve some aspect of the consumer's health. 18 Mass media has been cited as a source for health information for adolescents, particularly those with chronic illness (eg asthma, mental illness), 19 but without adequate preparation for interpreting information they find, students may be unable to make appropriate health decisions. While most school students have minimal interaction with the healthcare system, this will increase as they become adults. Intervening to improve critical health literacy skills, while children are at school, may foster the development of skills that are necessary for health decision making through adolescence and into adulthood. 19

1.3. Education to assess health claims

Previous studies have explored adults’ awareness of some of the key concepts involved in health claim appraisal – for example, randomization, 20 , 21 , 22 , 23 double‐blinding 23 and informed consent. 24 , 25 , 26 Some studies have explored university health students’ and health professionals’ understanding of these concepts – typically as part of evidence‐based practice. 27 , 28 , 29 Other studies have explored the way people assess health information and claims on the Internet; however, these studies specifically refer to assessing aspects of Internet site quality, rather than a direct assessment of the accuracy of health claims on the site. 30 , 31 , 32 , 33

Research specifically focusing on critical health literacy in young people (who are not yet adults) is less common. Previous studies have explored media literacy education, 34 students’ understanding of the general scientific process (without health context), 35 , 36 and ability to assess online health information. 37

Incorporating critical health literacy education into school curricula has the potential to widely disseminate information and expose as many students as possible to this learning opportunity. 19 Education programmes that are designed to teach students to understand how, and why it is necessary, to assess health claims are currently being evaluated with African school students. 38 , 39 These programmes were developed for the eldest students at primary school (ages 10‐12 years old) as the researchers considered these students mature enough to understand the material. 40 Beyond these studies, research into students’ understanding of aspects of critical health literacy, including interpretation of health claims, is lacking. Understanding of these aspects can be used to inform the development of school educational interventions, which aim to enable students to critically evaluate information about health interventions and make informed decisions.

This study aimed to explore Australian high school students’ understanding of, and attitudes towards, the concepts relevant to the assessment of health information and claims.

2.1. Recruitment and participants

We focused on students in Grades 7‐9 (in Australia, these students are approximately 12‐15 years old). Purposive sampling was used to select the schools to invite for participation, with the aim of involving public (government‐funded) and private (fee‐paying) schools, and schools across a range of socioeconomic regions. Ethical approval was provided by the Human Research Ethics Committee at Bond University on the 1st of May 2015, and approval to approach public schools was granted by the relevant government education departments.

Between July and October 2015, seven Australian schools, in two Australian states, were approached to discuss potential involvement. Three agreed to participate. Once each principal had provided consent for school participation, students were recruited via internal advertising from teachers. The research was initially described to the students by the principal or nominated teacher. Students who indicated interest in participating were provided an information sheet and consent form to take home for parental completion.

2.2. Procedure

One author (LC) performed all of the interviews using an interview script, between August and October 2015. Each school organized a meeting room and a schedule of students. The interviewer collected the signed consent form prior to commencing, explained the interview process and expected duration (15‐20 minutes). Each interview was audio‐recorded, with participants’ consent, and later transcribed.

2.3. Data collection

The semi‐structured interview questions were designed to explore students’ understanding of, and assumptions regarding, the generation of information about health interventions; the role of research; how health information/claims are interpreted and/or assessed; and the perceived meaning of, and attitudes towards, associated terms (eg “evidence‐based,” “scientifically tested” and “clinically proven”). The questions were developed based upon a recently published list of key concepts that are considered important when people are assessing health claims, 14 and a book, written with the intention of teaching and promoting critical appraisal of health interventions, particularly within the public/lay population. 15 Throughout the interview and this study, the term “health intervention” is used broadly. This can include any intervention provided by a health professional or identified by the individual; prescription or non‐prescription; drug or non‐drug; conventional, complementary or alternative; and any product making a health claim (eg health and skin products, energy drinks, and foods). Piloting of the interview script with a convenience sample of people, who were not involved with the study, enabled subsequent refinement of the questions.

2.4. Data analysis

Two authors (LC, LD) independently used the process for thematic analysis outlined by Braun and Clark, 41 whereby each familiarized themselves with the interview transcripts, and generated initial codes for overarching themes and subthemes. This process was driven by the data, and thus inductive in nature. The authors (LC, LD) iteratively compared and discussed their analyses and coding, and came to consensus on an updated coding framework, with input from another author (TH). This coding framework was independently applied by LC and LD to five randomly selected interviews before final modifications were made. After coding all of the data for interviews from three schools, LC then reviewed the coded extracts for coherency within the themes, and further refinements were made to the themes and subthemes. No further participants were recruited as data saturation was evident (no new themes emerged from analyses of the final interviews).

The study recruited a total of 27 students from three of the seven Australian schools approached; two in Victoria and one in Queensland. The majority of participants were girls (n=18, 67%) and in Grade 7 (16, 59%), with fewer in Grade 8 (2, 7%) or 9 (9, 33%). Private (52%) and public schools (48%) were equally represented.

3.1. Key themes

Analyses revealed three themes including; (i) Variability in sources of health information and claims, and general understanding of their creation and accuracy of content; (ii) The use of substitute indicators to assess health information and claims, and make judgements about their trustworthiness and (iii) Uncertainty about, and literal interpretation of, the language of health claims.

3.1.1. Theme 1: Variability in sources of health information and claims, and general understanding of their creation and accuracy of content

There was great variability in students’ access to, and understanding of health information. Approximately half of the students had searched for health information on the Internet, while others indicated that they relied on their parents to provide this type of information – “If anyone in my family ever does that [searches for health information], it's my parents” (Participant 2).

Students who had searched for health information predominantly sought it from the Internet, using the search engine Google. A few sought information from medical centres or government authorities; and some from an intervention's packaging (if a physical product) – “On the back of the packet it says all the stuff that you need to look out for…” (Participant 15).

When asked where they thought people who share health information in some way (such as journalists and website writers) obtained information from, the students offered a mixed response. About half referred either to researchers, scientists, health professionals, institutions or organizations – “Through, probably, science tests and maybe they searched with professional scientists and that” (Participant 26).

Others thought this type of information came from the public, by means such as surveys, interviews or anecdotes – “Maybe from people, I guess. They might survey people” (Participant 10), or specific groups of people – “They might get them from, like, athletes or people working with athletes, like, physiotherapists and stuff like that” (Participant 7). Other students indicated that the information may have been found from another source within the mass media – “Maybe the news or the internet, the newspaper…” (Participant 16), or sought directly from manufacturers of the health products.

About half of the students made comments during the interview, which indicated basic awareness of the role of health‐related research: they were either aware of certain aspects of the research process – “[Health information comes from] scientific evidence and evidence by past experiences and about experiments, as such, and how things work and things don't work” (Participant 20), or that research has a role in generating reliable health information – “[what makes claims true is when] there is research behind [the health information] and not them just claiming” (Participant 13). Some students mentioned terms such as “evidence”, “research”, “scientists”, “experiments” or “testing”, but only a few were able to elaborate on these concepts:

Well, if they actually tested it and stuff like that, so, yeah, they actually have and they can show that they've actually tested it, and they can't just make up figures. (Participant 18)

Some of these responses appeared to have drawn on general knowledge rather than specific knowledge of the topic or health information. For example, some students mentioned that information about interventions may change over time, with one student stating that the reader would:

…never know who is writing (the information)… it could be 50 years old and a whole new discovery was made the other day. (Participant 8)

When prompted to elaborate on responses to the question, “You said that all research is probably not true, but why do you think that?”, a couple of students inadvertently referred to the influence of bias and placebo effect – “cause some people might be biased…” (Participant 24) and “…if you believe this [treatment] will help you, then it will probably help you” (Participant 15).

When asked if health information and claims were generally true, most students acknowledged that not all is:

Well, most of the time they say it's clinically proven or something, but we don't know. (Participant 2)

They can get [information] from test participants and … or general public, people who have tried it or sometimes they might even just make them up. (Participant 14)

However, although many students expressed general scepticism, some indicated they believed that health information and claims were, “basically true most of the time” (Participant 15) and generally justifiable:

…they have to get people to check it and have to go through some before they advertise it on TV to see if it's correct… like a publisher for a book or something… (Participant 11)

Some students identified a reason that health claims on the Internet may not be legitimate and can be created by people without authority or integrity:

…people can lie pretty easily. Like, it's not too hard, especially on the internet, just write a couple of words that aren't exactly true and there you go, you've got … and this amazing statement about something that is completely false… (Participant 14)

Nearly all the students identified the existence of ulterior motives or other vested interests that can be behind health information:

…different people want you to believe different things. (Participant 24)

Most of the time they're two companies or two brands competing against each other to try and get you to be convinced about what they believe and not what you believe. They're trying to pull you into what they want. (Participant 20)

More specifically, some students felt that health information was sometimes presented as a marketing technique or a form of advertising, “…so people will buy their product” (Participant 17).

When asked about the possibility of downsides of health interventions, all students acknowledged the potential for harm – “[it]… could fix something but also bring something else on, and it just doesn't tell you that necessarily” (Participant 9).

Most interview questions referred to health interventions in general, however, a few questions asked about health interventions, which described themselves “natural”. These types of interventions were generally viewed positively by most students – “[it would help], because it has natural ingredients and it's not artificial, and it would be more careful” (Participant 18). Some students perceived that natural ingredients were less likely to harm – “…because it's natural and it doesn't have all those toxins and stuff like that” (Participant 16). Others were not sure if products which claim to be “natural” could be harmful – “Maybe, like it could [be harmful] – well everyone reacts differently to stuff, but it is natural so it shouldn't be too harmful, but it might be harmful” (Participant 27).

3.1.2. Theme 2: The use of substitute indicators to assess health information and claims, and make judgements about their trustworthiness

No students mentioned searching for, or using, any formal or validated methods of assessing health information or claims. Instead, students described the use of various substitute indicators to make their assessment, which included the following:

• personal experience of the intervention:

You obviously just buy them both and see which “one”. (Participant 8)

• corroboration – that is, for specific health products, by finding multiple sources which provide information, to check or reinforce the initial information:

…if I saw something, then I would go and research it further and if other people… like, other websites are saying the same thing as, like, what every this product is saying, then I would probably believe it. (Participant 17)

• performing “research,” which students used to refer to searching on the Internet:

… research the name… something like Google… just see what articles, if there's… reviews about it and stuff like that. (Participant 14)

• evaluating the source of the information:

See if it comes from a reliable source. (Participant 7)

• the perceived quality of its presentation (eg on the Internet, or product packaging):

…if it's on just a crappy web site… or it doesn't have a brand or it's not set up properly or the information… doesn't have good grammar and just things like that that just make it not very good quality. (Participant 13)

Like, valid packaging would have, like, not, like, massive scientific words but actually give you, like, information that you can understand and not like … and dodgy would have, like, big words that are just jumbled together to make it look more scientific and more complicated than what it is. (Participant 13)

• a detailed description of how the product works:

… if [the company of the product] have a deep understanding, I tend to believe it. (Participant 11)

Because they know what they're talking about. (Participant 27)

• presentation of balanced information was important to some, particularly if potential negative effects were mentioned:

…if it says, ‘Studies show’, and maybe talks about the studies a bit and maybe also a thing which I guess could help is if it mentions some bad things about it, so the side effects, so it's not all good, good, good, because that's not all advertising, it also shows a couple of the bad things which is also, like, even though it does cause this, this and this, it is still pretty good and, yeah. (Participant 12)

• familiarity with the intervention provider or manufacturer made about half of the students feel more comfortable when making a decision about a health product:

… companies that I have heard of or have used, I know that they do work or don't work, so yeah, if I have heard of it then I might try it, but if I haven't heard of it, I still might try it, but I might have a bit of risk. (Participant 21)

Other students, however, did not use this as a substitute indicator, with some saying that they would not assume a treatment by a known brand would be better than one by an unknown brand, while others were uncertain if being familiar with a brand influenced their belief in product claims:

‘It might be, but there might be good companies that you've just never heard of before and they can make good stuff as well, and it's better to just try new things and see if they work for you, maybe. (Participant 17)

• cost of an intervention was perceived as an indicator of quality by some – “…cause it means that they have invested more time and money into it” (Participant 24). Others did not share this belief – “they're just trying to rip you off”

(Participant 5).

Students were questioned about the influence of people (family members, friends or famous people), or organizational endorsement of health claims (such as medical or government authorities). Opinions were mixed about the endorsement by a family member or a close friend. Some felt this indicated believability:

…if a friend says that it works, then I'd more believe them because I know them and I know where they come from. (Participant 3)

Others were less trusting of such endorsements, suggesting that even if an intervention works well for one person, it may not for another:

… every person is different and it might work on someone else, but if it were tried on another person, their body is different so it won't work exactly the same. (Participant 18)

Students reported that they generally believed claims that were endorsed by “unknown” consumers or “ordinary people” – “… cause it's straight from their experiences with it, not scripting and getting told what to say” (Participant 20).

For some, a celebrity endorsement could make a claim more believable:

Well, you kind of believe it a bit more because, like, the person has high standards because they're obviously a celebrity, so they're rich and everything, so I'd probably believe it a little bit more, not as much as, like, it has to be like, completely correct, but more than just a normal person. (Participant 6)

Yet, others were suspicious of celebrity endorsements, and expressed awareness about financial incentives:

… they're probably just saying it just to get the money, and it doesn't really feel like they're actually meaning it. (Participant 18)

While in some cases, celebrity endorsement made no difference to whether students believed the claim, if it came from a celebrity health professional , some students were sceptical – “wouldn't fully believe everything” (Participant 18). However, the endorsement of health information by any health professional (not necessarily celebrities) was generally viewed positively by students:

[I'd believe it] if a doctor had said it or, yeah, probably a doctor or someone qualified enough to prove that it is ‘true’. (Participant 21)

3.1.3. Theme 3: Uncertainty about, and literal interpretation of, the language of health claims

Students associated terminology such as “‘evidence‐based,”’ “clinically proven” and “scientifically tested” with the assumption that research had been performed, or there was evidence or proof to support the claim:

So it means that the research has been done, so there's evidence that… the product is good or bad for you. (Participant 15)

Most students interpreted these terms literally – “[Evidence‐based means]… they've got evidence and it's based on what people have said, I think, yeah” (Participant 5).

A few offered a more detailed description:

… it [‘evidence‐based’] might be like they'll take the findings, they'll get like a bunch of test participants to sort of, like, test it and see if it works and sort of … or they'll give out, like, some people to do a trial of it for, like, 30 days and if they notice a difference or whatever, then they'll probably be like, yeah, ‘it works’. (Participant 14)

I guess it [‘evidence‐based’] means that they have tested it … so they haven't just tested it once or twice, they've tested it multiple times and took sort of everything into consideration or as much as they could, so a couple variables they've done. So, people with certain allergies, people without any, and yeah. (Participant 12)

However, most students acknowledged they did not understand the meaning of such terms:

Clinically proven. It's, sort of, like – I don't know about this one. Yeah. I'm not quite sure about this. (Participant 20)

Despite not fully understanding the terms, many viewed the intervention positively, stating that they would be more likely to use it:

I don't know what ‘clinically’ means, but I see [clinically proven] on everything and I'm just, like, oh, yeah, that'll be fine to use. (Participant 5)

It, sort of does to me [makes me more likely to use it], ‘cause it's one of those things that I feel ties in with a bit of the science and that behind it so it's been proven definitely. (Participant 20)

When asked how they thought new interventions compared to existing interventions, about half of the students responded that newer ones were better, with some elaborating that “newer” meant that more research, or “testing,” would have enhanced the newer one:

…there's more studies and research done and they've improved it probably. (Participant 12)

Others expressed uncertainty about whether new interventions were generally better, and a few students perceived new interventions negatively, explaining:

…some new treatments work as well, but just not as reliable as the old ones that have been used for a long time. (Participant 21)

Sometimes sticking with the old thing … is sometimes more reliable ‘cause … it's tested over years, but sometimes new ones might not be correct until a few years of testing… (Participant 20)

When asked whether, in general, new interventions have more or fewer side‐effects than older ones, responses were mixed. About half were unsure; some indicated that new interventions have more side‐effects – “… because they're newer and they don't have as much experience with the things that they're putting in there…” (Participant 1); while others believed that new interventions have less – “… [with] a lot of the new treatments, there's more side effects to start with but they sort of work out all the bugs and sort of get it good, whereas older ones generally had a lot more side effects…” (Participant 14).

4. DISCUSSION

We found that this sample of Australian school students, aged between 12‐15 years, generally had poor understanding about how health information and claims are generated and disseminated, and subsequently, how they can be assessed. Not unsurprisingly, many were largely reliant on their parents to manage any health conditions and students typically had little interaction with the health sector. However, many had already been exposed to health information and claims, and decision‐making about interventions which claim to impact upon health (for example, skin care products and sports drinks).

Many were generally sceptical about health information and claims, typically proffering concerns about conflict of interests, particularly financial, and the unregulated nature of the Internet, which allows anything to be published. Despite using terms such as “evidence” and “studies” in some of their replies, participants could not elaborate on what these terms mean or how to judge the accuracy of health claims. Instead they relied on personal experience or substitute indicators of accuracy, such as endorsements. Endorsements by friends, family or celebrities, appeared to be less consistently valued than those by health professionals and members of the public. Other substitute indicators included reading information on the product itself or associated websites, where descriptions of how the intervention works lent credibility, as did the quality of information presentation, the familiarity of the brand and the cost. An association of trust with familiar branding was noted in a study that examined health literacy challenges facing adolescents. 30 The variety of responses illustrates the diversity of approaches used and assumptions made by students.

The use of research terms, such as “evidence‐based” and “clinically proven”, in health claims has become common. While many had previously heard or seen terms like these, students’ understanding of what the terms meant was superficial. There was dissonance between students’ acknowledged lack of understanding of the meaning, yet an inclination to trust interventions that used the terms. This phenomenon of the mere presence of a health claim (regardless of its accuracy or a potential users’ understanding of it) encouraging a positive perception of the intervention, has been previously noted in studies assessing food products and cigarettes. 42 , 43 , 44 , 45

We are not aware of any studies that have explored school students’ general understanding of, and approaches to, assessing health information and claims. However, some of the findings of this current study are similar to those found in studies of adults’ health information‐seeking behaviour. Studies of adults have found that while people have easy access to health information through the Internet and may perceive their “research” skills as good, they actually have difficulty judging the trustworthiness of health information. 31 , 33 , 46 , 47 , 48 The use of the Internet to assess health information and claims in an unstructured way has also been previously found, with people often relying on search engines to identify relevant information, 12 , 31 , 33 making personal judgements about the quality of the information using factors such as the information source and presentation, 12 , 13 and not considering the evidence about intervention effectiveness when making a decision. 49 Adults searching for medicine information have also been found to search for corroborating information to reinforce a particular belief. 33

Recent reviews of interventions to improve school students’ ability to assess health claims have found limited interventions in this area, 50 , 51 leaving students likely to inadvertently rely on inaccurate information when making health decisions. 18 , 37 This study has identified specific areas requiring attention, and the findings will assist us in developing and evaluating a school educational intervention, which aims to enable students to critically evaluate information about health interventions and claims.

Some of our findings are encouraging for the potential of using education to focus on the areas in which students have low understanding and skills. For example, many students were generally sceptical about health claims, with an awareness of ulterior motives and vested interests. They were already readily using the Internet to search for information and most had an awareness of the unregulated nature of the Internet – this may serve as an incentive to develop better skills in searching for and assessing the accuracy of health information and claims. Likewise, some students based their judgement of health claims either on personal experience or on triangulated information (similar information from multiple sources which students took as reinforcement of its validity). Teaching could expand upon these assumptions (that is, of multiple sources of information vs a single experience) to include the basics of research study hierarchies, what systematic reviews and randomized trials are, and why they are more believable than anecdotes from one person when assessing health claims. Students are unlikely to otherwise learn about key concepts 14 such as these and having this knowledge has the potential to immediately influence their searching and interpretation behaviour.

Limitations of this study include possible unrepresentativeness of the sample. Boys and students from rural areas were under‐represented, and recruitment may have overrepresented middle‐ to high‐ socioeconomic status urban schools and students. Responses from participants may not accurately reflect actual behaviour, and we were unable to validate claims about their behaviours.

5. CONCLUSION

This study has provided insight into students’ understanding of issues relevant to assessing the accuracy of health information and claims and highlighted areas to incorporate into educational interventions. This sample of school students lacked understanding of basic health research processes and the knowledge or skills to assess health claims. This topic has had almost no attention in traditional school curricula, despite an increasing focus on teaching critical thinking in school subjects. 52 , 53 , 54 , 55 , 56 There is growing recognition of the role of such skills in equipping adults with critical health literacy, 10 , 11 , 57 and the school system may be an ideal place to begin teaching these skills. Until students (and adults) have this knowledge and skill set, they remain vulnerable to inaccurate and misleading health information and claims, which may result in them making ill‐informed health decisions.

CONFLICT OF INTEREST

The authors declare that they have no conflict of interests relevant to this manuscript.

ACKNOWLEDGEMENTS

We wish to express our gratitude to the principals, teachers and the students who made it possible to conduct the interviews. We would also like to acknowledge and thank Sir Iain Chalmers and Professor Andy Oxman for their much appreciated general input on the project.

Cusack L, Desha LN, Del Mar CB, Hoffmann TC. A qualitative study exploring high school students’ understanding of, and attitudes towards, health information and claims . Health Expect . 2017; 20 :1163–1171. https://doi.org/10.1111/hex.12562 [ PMC free article ] [ PubMed ] [ Google Scholar ]

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100 Interesting Research Paper Topics for High Schoolers

What’s covered:, how to pick the right research topic, elements of a strong research paper.

• Interesting Research Paper Topics

Composing a research paper can be a daunting task for first-time writers. In addition to making sure you’re using concise language and your thoughts are organized clearly, you need to find a topic that draws the reader in.

CollegeVine is here to help you brainstorm creative topics! Below are 100 interesting research paper topics that will help you engage with your project and keep you motivated until you’ve typed the final period. 

A research paper is similar to an academic essay but more lengthy and requires more research. This added length and depth is bittersweet: although a research paper is more work, you can create a more nuanced argument, and learn more about your topic. Research papers are a demonstration of your research ability and your ability to formulate a convincing argument. How well you’re able to engage with the sources and make original contributions will determine the strength of your paper. 

You can’t have a good research paper without a good research paper topic. “Good” is subjective, and different students will find different topics interesting. What’s important is that you find a topic that makes you want to find out more and make a convincing argument. Maybe you’ll be so interested that you’ll want to take it further and investigate some detail in even greater depth!

For example, last year over 4000 students applied for 500 spots in the Lumiere Research Scholar Program , a rigorous research program founded by Harvard researchers. The program pairs high-school students with Ph.D. mentors to work 1-on-1 on an independent research project . The program actually does not require you to have a research topic in mind when you apply, but pro tip: the more specific you can be the more likely you are to get in!

Introduction

The introduction to a research paper serves two critical functions: it conveys the topic of the paper and illustrates how you will address it. A strong introduction will also pique the interest of the reader and make them excited to read more. Selecting a research paper topic that is meaningful, interesting, and fascinates you is an excellent first step toward creating an engaging paper that people will want to read.

Thesis Statement

A thesis statement is technically part of the introduction—generally the last sentence of it—but is so important that it merits a section of its own. The thesis statement is a declarative sentence that tells the reader what the paper is about. A strong thesis statement serves three purposes: present the topic of the paper, deliver a clear opinion on the topic, and summarize the points the paper will cover.

An example of a good thesis statement of diversity in the workforce is:

Diversity in the workplace is not just a moral imperative but also a strategic advantage for businesses, as it fosters innovation, enhances creativity, improves decision-making, and enables companies to better understand and connect with a diverse customer base.

The body is the largest section of a research paper. It’s here where you support your thesis, present your facts and research, and persuade the reader.

Each paragraph in the body of a research paper should have its own idea. The idea is presented, generally in the first sentence of the paragraph, by a topic sentence. The topic sentence acts similarly to the thesis statement, only on a smaller scale, and every sentence in the paragraph with it supports the idea it conveys.

An example of a topic sentence on how diversity in the workplace fosters innovation is:

Diversity in the workplace fosters innovation by bringing together individuals with different backgrounds, perspectives, and experiences, which stimulates creativity, encourages new ideas, and leads to the development of innovative solutions to complex problems.

The body of an engaging research paper flows smoothly from one idea to the next. Create an outline before writing and order your ideas so that each idea logically leads to another.

The conclusion of a research paper should summarize your thesis and reinforce your argument. It’s common to restate the thesis in the conclusion of a research paper.

For example, a conclusion for a paper about diversity in the workforce is:

In conclusion, diversity in the workplace is vital to success in the modern business world. By embracing diversity, companies can tap into the full potential of their workforce, promote creativity and innovation, and better connect with a diverse customer base, ultimately leading to greater success and a more prosperous future for all.

Reference Page

The reference page is normally found at the end of a research paper. It provides proof that you did research using credible sources, properly credits the originators of information, and prevents plagiarism.

There are a number of different formats of reference pages, including APA, MLA, and Chicago. Make sure to format your reference page in your teacher’s preferred style.

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• How does literature teach and inform about society?
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• What are the effects of deforestation on climate change?
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ORIGINAL RESEARCH article

Why students feel competent in the classroom: a qualitative content analysis of students’ views.

• 1 Department of Psychology, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
• 2 Department for Didactics of Biology, Faculty of Biology, Bielefeld University, Bielefeld, Germany

This qualitative study aimed to identify and to systematize factors that contribute to students’ competence satisfaction in class from students’ perspectives. Based on self-determination theory as our primary theoretical background, we conducted episodic interviews with 25 high school students. A combined deductive-inductive qualitative content analysis approach was applied. As our key finding, we revealed different teaching factors within and beyond self-determination theory (i.e., structure, autonomy support, relatedness support, mastery goal structure, perceived error climate, teaching quality, teachers’ reference norm orientations) as well as additional factors (e.g., students’ motivation and engagement, peer climate and reciprocal peer support) that contributed to students’ competence satisfaction in class from the students’ points of view. This study contributes to existing research on why students’ competence satisfaction arises in class by complementing it with an integrative, explorative, and student-oriented perspective.

Introduction

Students’ competence satisfaction plays a crucial role for motivation, achievement, and individual growth ( Ryan and Deci, 2017 ; Vansteenkiste et al., 2020 ; Vasconcellos et al., 2020 ). Therefore, in the literature, researchers have linked several teaching practices to students’ competence satisfaction (e.g., perceptions of structure and autonomy support) that can be addressed to support students’ competence satisfaction in different educational settings (e.g., school, extracurricular learning; Jang et al., 2010 ; Guay et al., 2017 ; Eckes et al., 2018 ; Aelterman et al., 2019 ; Ryan and Deci, 2020 ; Vasconcellos et al., 2020 ). However, in the context of self-determination theory (SDT), studies investigating the factors that contribute to students’ competence satisfaction have, in part, provided controversial findings. For instance, structure has been beneficial for students’ competence satisfaction when provided in an autonomy-supportive way ( Eckes et al., 2018 ). Autonomy support has, however, partly been negatively correlated with individuals’ competence satisfaction ( Steingut et al., 2017 ; Vasconcellos et al., 2020 ). Moreover, little is known about students’ views on why their competence satisfaction evolves in class. One reason is that students’ need satisfaction has rarely been studied qualitatively ( Hassandra et al., 2003 ). However, qualitative studies are an important step in order to understand the development and the manifestation of subjective experiences in social contexts through specific perspectives ( Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). To widen researchers’ view on why students’ competence satisfaction arises in classroom contexts from the students’ perspectives and to complement the mainly quantitative studies, this qualitative content analysis study explored students’ narratives about which factors contribute to their competence satisfaction in class.

Students’ competence satisfaction in self-determination theory

In the context of SDT, the basic psychological need theory describes three basic psychological needs, namely the needs for autonomy, relatedness, and competence ( Ryan and Deci, 2017 , 2020 ). The need for autonomy is the need to regulate one’s experiences and actions in a self-determined way. The need for relatedness is defined as the need to feel socially connected with others. The need for competence is the need on which we focus in this study. It is defined as the individuals’ need to experience effectiveness in interactions with their environment ( Deci and Ryan, 2000 ; Ryan and Deci, 2017 , 2020 ). Students’ need for competence is satisfied when students act in and experience classroom environments in which they can express and extend their skills and knowledge ( Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). Moreover, students feel competent when their abilities are in balance with the demands of actions ( Reeve, 2015 ). In the following, the satisfaction of students’ need for competence is referred to as students’ competence satisfaction.

Students’ competence satisfaction is essential for their motivation, achievement, and well-being ( Reis et al., 2000 ; Jeno et al., 2018 ; Muenks et al., 2018 ; Ryan and Deci, 2020 ; Vasconcellos et al., 2020 ). Contrarily, the frustration of students’ need for competence has been linked to disengagement, amotivation, and helplessness ( Legault et al., 2006 ; Earl et al., 2017 ; Ryan and Deci, 2017 , 2020 ; for an overview, see Vansteenkiste et al., 2020 ). These findings show the importance of taking students’ need for competence into account when designing lessons and school environments.

Fostering students’ competence satisfaction in self-determination theory

Within SDT, the measures designed to fulfill students’ basic psychological needs are subsumed under the term need support. Need support typically encompasses structure, autonomy support, and relatedness support ( Ryan and Deci, 2020 ). Structure describes to which extent teaching styles provide clear communication of expectations, appropriate feedback, and guidance ( Jang et al., 2010 ; Ryan and Deci, 2017 , 2020 ; Aelterman et al., 2019 ; Vasconcellos et al., 2020 ). It can be divided into clarifying and guiding structure ( Aelterman et al., 2019 ). Teachers with a focus on clarifying structure give overviews about the learning material, make their expectations transparent, and monitor the students’ progress. Teachers who apply guiding structure provide help and guidance when needed. They also assist the students to accept mistakes as an important step in their learning progress, as well as to reflect on them ( Aelterman et al., 2019 ).

Autonomy support focuses on identifying and nurturing students’ feelings, perspectives, and preferences ( Jang et al., 2010 ; Ryan and Deci, 2017 , 2020 ; Aelterman et al., 2019 ; Vasconcellos et al., 2020 ). It has been divided into participative and attuning autonomy support ( Aelterman et al., 2019 ). Teachers focusing on participative autonomy support engage in dialogue with their students. They invite them to provide input and give them opportunities to choose. Attuning autonomy support comprises the acceptance of students’ feelings, the provision of meaningful rationales, and the application of ways to make learning enjoyable for the students ( Aelterman et al., 2019 ).

Relatedness support includes teaching practices that empower students’ sense of social connection and belonging ( Reeve, 2015 ; Sparks et al., 2016 ; Vasconcellos et al., 2020 ). The latter has scarcely been explored in SDT ( Sparks et al., 2016 ). However, following physical education research, relatedness-supportive teachers provide individualized conversations, task-related feedback, and promote cooperation and teamwork. They also show enthusiasm, have high awareness, care about their students, and communicate in a friendly way with them ( Sparks et al., 2015 , 2016 ). Reeve (2015) has additionally proposed relatedness support to comprise the following aspects: taking time for other individuals, caring and knowing things about other individuals, expressing affection and appreciation with regard to other individuals, enjoying interaction, and sharing resources (e.g., interest) with other individuals.

From an empirical point of view, autonomy support and structure have been positively associated with students’ competence satisfaction quite consistently ( Patall et al., 2008 ; Mouratidis et al., 2013 ; Guay et al., 2017 ; Ryan and Deci, 2020 ; Vasconcellos et al., 2020 ). For instance, meta-analytical findings have shown a strong link between structure and students’ competence satisfaction as well as a positive relationship between opportunities to choose and students’ competence satisfaction ( Patall et al., 2008 ; Vasconcellos et al., 2020 ). Furthermore, relatedness support was positively correlated to students’ competence satisfaction in a meta-analysis ( Vasconcellos et al., 2020 ). Relatedness was also a major theme for youth in a social service context ( Nagpaul and Chen, 2019 ). These findings suggest that relatedness support could play an important role for students’ perspectives on which factors contribute to their competence satisfaction.

Still, first, compared to autonomy-supportive measures, SDT research has paid less attention to measures that foster students’ competence satisfaction ( Sparks et al., 2016 ; Vasconcellos et al., 2020 ). Second, these findings have partly been controversial ( Guay et al., 2016 ; Steingut et al., 2017 ; Vasconcellos et al., 2020 ). This controversy impedes implications on why students feel competent in class. It prompts more research on which factors contribute to their competence satisfaction. Third, the typically applied approach describing teachers’ need support does not make claims about completeness ( Vansteenkiste et al., 2020 ). Factors within and especially factors that go beyond perceptions of teaching practices, such as student factors, peer factors, and context factors, remain to be explored. Last, there is a lack of studies that explore students’ perspectives on how and why need-supportive measures influence their competence satisfaction in class ( Anderman et al., 2002 ; Ryan and Deci, 2020 ). However, students are one of the actors in classes as social contexts. Their perspectives are hence important in order to understand the motivational processes taking place within and across classrooms ( Nolen et al., 2015 ; Nolen, 2020 ).

Understanding why students feel competent in class – The need for qualitative and integrative research

Qualitative research is able to provide a deep understanding of students’ narratives and experiences, to describe even complex student-environment-interactions, and to reveal how and why need-supportive measures work through individuals’ perspectives ( Patrick et al., 2001 ; Anderman et al., 2002 ; Flick, 2011 ; Nolen et al., 2012 , 2015 ; Mayring, 2014 ; Nolen, 2020 ; Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). Additionally, qualitative studies enable researchers to take a holistic perspective ( Nolen et al., 2015 ; Vansteenkiste et al., 2020 ). That is because, for instance, qualitative studies can simultaneously consider a theory-based perspective (i.e., deductive thinking; the use of existing theory in deriving qualitative findings) and a data-based perspective (i.e., inductive thinking; the explorative analysis of data; Mayring, 2014 ). Furthermore, qualitative research facilitates the transfer of theoretical knowledge into school practice, because it offers more detailed insights into individuals’ behaviors and experiences compared to quantitative research ( Patrick et al., 2001 ; Mayring, 2014 ; Nolen et al., 2015 ; Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). Qualitative research is hence one useful approach to widen researchers’ view on which factors contribute to students’ competence satisfaction through students’ perspectives. In line with this, Ryan and Deci (2020) have called for more qualitative research in the context of need support.

Moreover, scientific knowledge is primarily gained by building on existing research ( Merton, 1957 ; Parolo et al., 2020 ). Accordingly, researchers in motivational psychology as well as in methodological literature have called for combining and integrating different theoretical frameworks in order to extend, refine, and integrate existing knowledge ( Mayring, 2016 ; Anderman, 2020 ; Flick, 2020 ). Such an integrative approach is particularly important when aiming to translate specific research questions (e.g., why students’ competence satisfaction arises in class) into comprehensible recommendations for practitioners in the classrooms (e.g., teachers; Anderman, 2020 ). After having reached several findings and contributions, translations into practitioner-oriented recommendations have been called for in the context of SDT ( Ryan and Deci, 2020 ). Therefore, one important question is which existing theories one can build on in addition to SDT. Besides taking the students’ perspectives into account, this work took an integrative perspective, and considered different theoretical frameworks in order to widen SDT researchers’ view on how to facilitate students’ competence satisfaction in class.

Understanding why students feel competent in class – Theoretical frameworks for qualitative research

The investigation of teaching practices is one approach which has extensively been investigated in motivational and educational psychology ( Lazowski and Hulleman, 2016 ; Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). Based on a continuous dialogue with experts in motivational psychology, didactics, and educational psychology, as well as on a literature informed strategy that sought to include renowned works (e.g., Corno and Anderman, 2016 ; Wentzel and Miele, 2016 ), we therefore preselected a variety of teaching practices that might contribute to students’ competence satisfaction beyond existing SDT assumptions from students’ perspectives. Specifically, some well-established teaching practices have been essential for educational outcomes, and have already been linked to students’ competence satisfaction or to related perceptions of competence (e.g., Halvari et al., 2011 ; Steuer et al., 2013 ; Scherer et al., 2016 ; Dickhäuser et al., 2017 ). In order to identify and to systematize additional factors that might contribute to students’ competence satisfaction beyond the existing SDT assumptions from our integrative perspective, we therefore considered the theoretical frameworks from which these teaching practices were derived, namely the achievement goal theory, perceived error climate, teaching quality, and reference norm orientation theory, in the conceptualization, analysis, and discussion of the present study. They are outlined hereafter.

In the classroom goal structure literature, researchers typically distinguish between mastery goal structure (a focus on developing competencies in class), performance approach goal structure (a focus on demonstrating competence and on outperforming others in class), and performance avoidance goal structure (a class focus on not demonstrating incompetence and on avoiding to be inferior to others in terms of performance; Meece et al., 2006 ; Urdan and Schoenfelder, 2006 ; Schwinger and Stiensmeier-Pelster, 2011 ). Classroom goal structures have been an important starting point for motivational interventions as well as for understanding students’ motivational and achievement-related functioning (e.g., Wolters, 2004 ; Urdan and Kaplan, 2020 ). Moreover, having a high level of mastery goal structure has been positively linked to perceptions of competence satisfaction (e.g., Kavussanu and Roberts, 1996 ; Cox and Williams, 2008 ; Halvari et al., 2011 ). Taking the well-investigated TARGET approach into account ( Ames, 1992 ; Meece et al., 2006 ; Lüftenegger et al., 2014 ; Urdan and Kaplan, 2020 ), the following mastery goal structure dimensions could thus help to investigate students’ perspectives on why their competence satisfaction arises in class: task (teachers design tasks that focus on learning, provide optimal challenge, and enable students’ active involvement), authority (teachers provide opportunities to choose, for sharing perspectives, and for taking responsibility), recognition (teachers recognize students’ acting and achievement, e.g., by using feedback), grouping (teachers enable collaborative work in heterogeneous groups and interaction among students), evaluation (teachers’ evaluations focus on learning and collaboration instead of competition), and time (teachers provide appropriate workload and pace; Meece et al., 2006 ; Lüftenegger et al., 2014 , 2017 ).

Another theoretical approach which we addressed is the perceived error climate research (e.g., Oser and Spychiger, 2005 ; Steuer et al., 2013 ; Reeve, 2015 ). Perceived error climate is defined as the way of evaluating and using errors within learning processes in classroom environments or other social learning environments ( Steuer et al., 2013 ). With regard to classroom contexts, Steuer et al. (2013) described the perceived error climate as a multidimensional construct including eight dimensions such as teachers’ error tolerance. Perceived error climate has not yet been linked to students’ competence satisfaction but to students’ self-concept and employees’ self-efficacy as competence-related variables ( Putz et al., 2013 ; Steuer et al., 2013 ). It also partly appeared in the literature on need-supportive measures ( Reeve, 2015 ; Aelterman et al., 2019 ; Jiang et al., 2019 ). These theoretical and empirical discussions suggest that a positive error climate might help to identify additional factors that contribute to students’ competence satisfaction through students’ perspectives.

In the teaching quality framework, researchers typically define three basic dimensions in order to explain under which circumstances students can learn effectively: classroom management (getting and keeping students attentive and on task), cognitive activation (providing optimal challenge and fostering students’ thinking), and student support (establishing a teacher–student-relationship which fulfills students’ needs; Praetorius et al., 2018 ). The teaching quality dimensions are one main precondition for self-perceptions of competence related to students’ competence satisfaction (e.g., self-concept) as well as for students’ achievement which again is related to students’ competence satisfaction ( Weinert et al., 1989 ; Scherer et al., 2016 ; Jeno et al., 2018 ; Praetorius et al., 2018 ; Blömeke and Olsen, 2019 ). Moreover, the student support dimension has been elaborated based on the need-supportive measures ( Praetorius et al., 2018 ). Hence, first empirical findings and theoretical elaborations indicate the relevance of teaching quality for students’ competence satisfaction. In contrast to this and the importance of this framework for several educational processes (e.g., Fauth et al., 2014 ; Scherer et al., 2016 ; Panayiotou et al., 2021 ), the teaching quality dimensions have not been empirically linked to students’ competence satisfaction in terms of SDT.

In reference norm orientation theory (e.g., Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ), researchers distinguish between three reference norms: The social (the use of interindividual comparisons), criteria-oriented (the use of comparisons with an absolute standard), and intraindividual reference norm (comparing students’ achievement with their own prior achievement) describe comparison standards by which actions, performance or competence are evaluated ( Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ; Lohbeck and Freund, 2021 ). Teachers use some reference norms more frequently than others which is called teachers’ reference norm orientation. Specifically, teachers who are oriented toward the intraindividual reference norm focus on improvement, have short-term expectations, and provide optimal challenge, among others. Teachers who are oriented toward the social reference norm focus on normative competence and provide uniform tasks for all students in class. Teachers who frequently use criteria-oriented reference norms presumably apply criteria-oriented teaching and task-focused feedback ( Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ; Lohbeck and Freund, 2021 ). However, teachers’ criteria-oriented reference norm orientation has not been elaborated yet. With regard to students’ competence satisfaction, teachers’ intraindividual reference norm orientation and teachers’ use of the criteria-oriented reference norm were found to be positively associated with related self-perceptions of competence (e.g., self-concept; Rheinberg, 1983 ; Krampen, 1987 ; Lüdtke et al., 2005 ; Dickhäuser et al., 2017 ; Lohbeck and Freund, 2021 ). Furthermore, in the context of SDT, some theoretical considerations as well as initial findings stressed the importance of differentiated instruction and improvement-focused feedback which are key elements of teachers’ intraindividual reference norm orientation ( Carpentier and Mageau, 2013 ; Reeve, 2015 ; Guay et al., 2017 ; Ryan and Deci, 2017 ). Although this prompts further research on whether teachers’ reference norm orientations might contribute to students’ competence satisfaction in terms of SDT, studies have not addressed this linkage.

To conclude, more research is required with respect to the competence-supportive measures within SDT, given the mainly quantitative, and some controversial findings in past research. Specifically, more qualitative research on students’ perspectives is required in order to take their essential perspectives into account in realistic classroom contexts. Furthermore, first hints suggest that, besides SDT and students’ perspectives, the depicted additional theoretical frameworks (i.e., the research on classroom goal structures, perceived error climate, teaching quality, and reference norm orientations) could provide additional factors that contribute to students’ competence satisfaction in the sense of SDT (e.g., Halvari et al., 2011 ; Dickhäuser et al., 2017 ; Praetorius et al., 2018 ). However, those frameworks as well as factors going beyond teaching practices (e.g., student factors, peer factors, and situational factors) have not sufficiently been considered with regard to students’ competence satisfaction in the context of SDT. A combined explorative investigation of students’ perspectives and integrative consideration of the depicted theoretical backgrounds hence is one fruitful approach to extend the existing literature on which factors contribute to students’ competence satisfaction through students’ perspectives.

Due to its procedure variety which allows a combined theory-driven and data-driven perspective, the qualitative content analysis is one approach which is particularly suitable in addressing these research desiderata by using qualitative material ( Mayring, 2014 ). Moreover, its rule-oriented as well as hybrid (i.e., combined qualitative and quantitative) or, in other words, integrated (i.e., combination of qualitative and quantitative analysis steps within one research design) approach allows an exact elaboration, validation, and further analysis of qualitative categories ( Mayring, 2007a , 2014 ; Burzan, 2016 ; Gläser-Zikuda et al., 2020 ). It is noteworthy that the need support, mastery goal structure, perceived error climate, teaching quality, and the reference norm orientation frameworks partly encompass similar teaching practices (e.g., optimal challenge; Rheinberg, 1983 ; Lüftenegger et al., 2017 ; Praetorius et al., 2018 ; Aelterman et al., 2019 ). From a pragmatical perspective, a combined theory- and data-based as well as a hybrid (or: integrated) approach to analyze qualitative material therefore seems particularly promising in order to get a holistic view of separable factors which represent reasons for students to feel competent in the classroom ( Mayring and Brunner, 2006 ; Mayring, 2007a , 2014 ; Burzan, 2016 ). By applying a combined theory-based and data-based content-analytical approach, one may hence identify and systematize already known (e.g., structure) and additional factors (e.g., student factors) that contribute to students’ competence satisfaction in class from the students’ perspectives.

Present study

Based on existing research, this interview study aimed to identify and to systematize additional factors that might contribute to students’ competence satisfaction. In addition to SDT, it focused on students’ perspectives (i.e., explorative research design; Mayring, 2007a , 2014 ) as well as on existing theoretical frameworks (classroom goal structure literature, perceived error climate research, teaching quality framework, reference norm orientation theory) that might add to SDT with regard to students’ competence satisfaction in class (i.e., descriptive research design; Mayring, 2007a , 2014 ). As a result, this qualitative study had a combined explorative-descriptive field research design. By doing so, it aimed to refine, extend, and integrate existing knowledge within and beyond SDT on how to fulfill students’ need for competence in realistic classroom settings, giving new directions for future research. By using episodic interviewing, this work addressed students’ generalized beliefs as well as the complexity of student-classroom environment-interactions in real school-life situations ( Flick, 2011 , 2018 ). By analyzing the interviews following the rules of qualitative content analysis, this study applied a rigorous and hybrid (or: integrated; i.e., combined qualitative and quantitative) approach for analyzing qualitative material ( Mayring, 2000 , 2007a , 2014 ; Mayring and Brunner, 2006 ; Scheufele, 2008 ; Burzan, 2016 ; Krippendorff, 2019 ; Gläser-Zikuda et al., 2020 ; e.g., Duchatelet et al., 2020 ). The research question that we addressed in our qualitative, integrative, and student-oriented study was: Which factors contribute to students’ competence satisfaction through students’ perspectives? The study was cross-sectional in nature, focusing the representativeness of our sample ( Nolen et al., 2012 ).

Materials and method

Participants and procedure.

The present study took place from May to July, 2019. It involved N = 25 ninth-grade students ( n = 9 male, n = 16 female) from two high schools (in German: Gymnasium) in the state of North Rhine-Westphalia, Germany. The students’ mean age was 14.84 years ( SD = 0.47 years). For the purpose of sampling, we deliberately addressed two contrasting schools in order to represent the perspectives of students’ coming from schools as diverse as possible, namely a private school in a rather rural area with a rather low socioeconomic status and a public school in a large city ( Küpper, 2016 ; Landatlas, 2019 ). This contrast-oriented sampling procedure is common in qualitative and mixed-methods research in order to overcome biased material (e.g., due to over-representing specific contexts) and to enhance validity ( Krippendorff, 1989 ; Brink, 1993 ; Collins et al., 2007 ; Onwuegbuzie and Leech, 2007 ; Creswell and Poth, 2016 ; e.g., Flick et al., 2019 ). After having obtained consent from the principals and teachers within the addressed schools, we orally presented the study, distributed information material as well as the written informed consent forms, and asked the students for participation during school lessons. This procedure combines the depicted purposive sampling procedure with a convenience sampling procedure. By doing so, we warranted the availability and willingness of the individual students to participate ( Collins et al., 2007 ). According to the clarity of the research field, the expected data quality, as well as the expected heterogeneity of participants, the first 25 students (School 1: n = 15; School 2: n = 10) who were willing to participate were included in the study ( Guest et al., 2006 ). Post hoc analyses revealed that data saturation was reached after coding 51% of the interview material.

According to the European Union General Data Protection Regulation 2016/679 and the Data Protection Act of North Rhine-Westphalia (Germany), all participants were informed about the voluntary nature of participation, and gave their written informed consent before the beginning of the interview. For students who were younger than 16 years old at the time of the interview, written parental consent was additionally provided. According to the depicted regulations, the written informed consent included content information (e.g., information on the study aims and procedures), legal information (e.g., the right to withdraw from the study), and the declaration of consent itself. The study was approved by the responsible research ethics committee.

In order to standardize the interview procedure, we conducted semi-structured episodic interviews according to Flick (2011) ( M duration = 42.86 min; SD duration = 12.87). Episodic interviews are a combination of narrative and semi-structured interviews. They contain open-ended questions and situation-specific narratives in order to capture both episodic and semantic components of students’ subjective narratives and experiences ( Flick, 2018 ). The applied interview schedule (see Supplementary Appendix A ) was revised after one pilot interview. In accordance to this interview schedule, the interviewers first introduced the topic of the study as well as the procedure and then asked the students to define their understanding of the term “competence satisfaction.” Second, the interviewers defined the aforementioned term in the sense of SDT. If necessary, the interviewers gave a standardized example. Third, the interviewers and students settled on a common definition according to the SDT definition. Fourth, the interviewers asked the students to describe at least one situation in which the students had perceived competence satisfaction in class, and at least one situation in which the students had perceived competence frustration (e.g., “Can you remember a current classroom situation [from the ongoing school year] in which you felt particularly competent? Please tell me about this situation.”). Fifth, the interviewers explored the students’ general beliefs about and experiences with factors that contribute to their competence satisfaction and with factors that contribute to their competence frustration in class. For this purpose, they asked some questions about the general reasons and circumstances under which the students felt competent and incompetent in class (e.g., “In general, what helps you in class to feel competent or what is important for you in class so that you can feel competent?”). Based on our research question, the situations in which the students perceived competence frustration and the generalized beliefs about and experiences with competence frustration were not of further relevance within this work. Last, the interviewers and students completed a short demographics questionnaire together.

The interviews were audio-recorded and conducted in one-to-one-settings (one interviewer, one student). They took place in a private room of the respective schools. In order to reach an adequate level of closeness and distance between interviewers and interviewees ( Helfferich, 2011 ), the interviewers were two student teachers. Given that the interviewers both had a more similar age and background to the interviewees than the authors of this study, it was assumable that the students would open up more easily by doing so. Nevertheless, the interviewers had sufficiently divergent backgrounds from the interviewees to uphold the interviewees’ willingness to verbalize information that is obvious to insiders but necessary to interpret the interview data ( Helfferich, 2011 ).

As far as professionality is concerned ( Helfferich, 2011 ), the interviewers had a strong school background (recent school experiences; a Bachelor’s degree in teaching, a well-advanced Master’s program in teaching), as well a motivational psychology background (successfully accomplished courses in motivational psychology). In addition, the interviewers were trained by the corresponding author of this study before the survey began. The training included the working through the literature which underlay our interview approach along with its debriefing ( Flick, 2011 , 2018 ; Helfferich, 2011 ), the discussion of the interview schedule and of questions, as well as the practicing of the interviews among the interviewers and in the mentioned pilot interview. The practicing interview and the pilot interview were debriefed with the corresponding author (practicing interview, pilot interview) and the pilot interviewee (pilot interview). The interview process and the Master thesis projects in the context of which the student teachers collected the data of the present study was supervised by the corresponding author (i.e., psychologist; researcher in the fields of motivational psychology and educational psychology), the third author (Master’s degree in teaching; researcher in the fields of biology didactics and motivational psychology), and the fourth author (i.e., teacher; researcher in the fields of biology didactics and motivational psychology) of the present study.

Beyond their important roles within the data collection of this study (investigation), the student teachers supported the participants’ acquisition (resources). The corresponding author provided the study materials (investigation), and took responsibility of the conceptualization, data curation, visualization, methodology, the writing of the original draft, and the project administration. In addition, the corresponding author and the second author of this manuscript (i.e., psychologist; researcher in the fields of motivational psychology and educational psychology) formally analyzed the interview material. A continuous peer review and peer debriefing across the author team was established during the entire research process, e.g., during the writing in review and editing stages. The last author of this manuscript (i.e., Master’s degree in Social Science; psychologist; researcher in the fields of motivational psychology, instructional psychology, and educational psychology) provided the resources and supervised the research project ( CRediT, 2020 ).

Qualitative and quantitative data analysis

After completion of the interviews, we transcribed and anonymized the interviews based on the well-established recommendations of Dresing and Pehl (2018) , Kuckartz (2010 , 2018) , and Selting et al. (2009 ; see Supplementary Appendix B for the transcription rules). The applied transcription rules represent a verbatim data transcription, except that they slightly adapt spoken language into standard German and to the written language. Moreover, standardized symbols are implemented to highlight specific audio recordings’ characteristics (e.g., […] for one-second-breaks in speaking). In order to anonymize the interviews, we anonymized any names, sites, and assigned a code to each transcript. Afterward, we analyzed the interviews according to qualitative content analysis ( Mayring and Brunner, 2006 ; Schilling, 2006 ; Mayring, 2014 ; Krippendorff, 2019 ). Qualitative content analysis is a hybrid (or: integrated) analysis approach that combines a rigorous qualitative and quantitative analysis of qualitative communication material, such as text material ( Mayring, 2007a ; Burzan, 2016 ; Gläser-Zikuda et al., 2020 ). Its qualitative analysis steps represent a phenomenological description of the interview material that is narrowly based on the interviewees’ statements. They result in a category system which gives a structured overview about the contents of the specific communication material with regard to a specific research question ( Mayring, 2014 ). Subsequent quantitative analysis steps regarding the resulting category system enable an exploration and description of the salience of specific categories within investigated samples, among other possibilities ( Mayring, 2014 ). An overview about the procedural model applied in our study can be found in Figure 1 . Note that Steps 1 to 3 in Figure 1 have already been considered in the sections theoretical background, present study, sample, procedure, and in the description of our transcription procedure.

Figure 1. Procedural model of the qualitative content analysis applied in the present study. Adapted from Mayring and Brunner (2006) .

Since we combined the deductive (i.e., theory-based derivation of categories) and inductive (i.e., material-based derivation of categories) approach of qualitative content analysis in this study ( Mayring and Brunner, 2006 ; Schilling, 2006 ; Mayring, 2014 ), we first elaborated the categories and definitions for the deductive category system (Step 4 in Figure 1 ). We thereby relied on the aforementioned theoretical frameworks (i.e., SDT, classroom goal structure literature, perceived error climate research, teaching quality framework, reference norm orientation theory) and on our interview schedule.

Then, we segmented the interview material into 8087 coding units in total (Step 5 in Figure 1 ; Chi, 1997 ; Schilling, 2006 ; for the segmentation rules, see Supplementary Appendix C ). Note that the participants are referred to by their codes (e.g., KM01) and by the corresponding interview segment (e.g., 80) to ensure anonymity in any participant quotations (e.g., female student KM01, 15 years old, reported in interview segment 80 “[Teachers who help me feel competent] above all, they explain things well.”). Based on our research question, we identified 1774 coding units describing factors that contributed to students’ competence satisfaction from the students’ perspectives (coding units per interview: M = 70.96; SD = 30.17). Specifically, we analyzed 34 situations ( n = 722 segments) in which the students had perceived competence satisfaction. Thousand fifty-two segments contained students’ general beliefs about and experiences with factors that contributed to their competence satisfaction. The remaining coding units referred either to another research question or did not contain any relevant information. They were therefore not analyzed within this work.

With regard to this work, the recording unit was one word, and minimally contained one proposition (i.e., one episode, one idea or one piece of information which is comprehensible by itself) describing a factor that contributed to students’ competence satisfaction. The context unit was one paragraph, and maximally contained one proposition describing one factor that contributed to students’ competence satisfaction ( Schilling, 2006 ; Tesch, 2013 ). The unit of classification was all coding units out of one interview referring to factors that contributed to students’ competence satisfaction since we chose a cross-interview approach ( Schilling, 2006 ; Mayring, 2014 ).

After an initial viewing of the material, provisional coding rules and text examples were inserted into the deductive category system (Step 6 in Figure 1 ). A second member of our research team was introduced to the category system and to the applied analysis software MAXQDA 2020 (Step 7 in Figure 1 ; VERBI Software, 2019 ). Afterward, both main coders commonly coded 10% of the material in order to get familiar with the category system, and to identify initial ambiguities in the category system (Step 8 in Figure 1 ). After solving those ambiguities (Step 9 in Figure 1 ), both main coders independently coded the same 24% of the interview material while documenting difficult coding units and categories. After a subsequent formative intercoder reliability check, further problems in the category system were discussed, identified, and resolved ( Schilling, 2006 ; Mayring, 2014 ). Specifically, minor overlaps between different categories or minor ambiguities entailed the addition or revision of definitions, coding rules, and text examples. For instance, we added coding rules that stressed the difference between Clear communication and high-quality explanations (coding units generally referring to adequate explanations when no support is required), Optimal challenge for student and regarding school requirements (coding units specifically describing teacher explanations having an appropriate challenge level regarding the students’ stage in learning progresses or regarding school requirements when no support is required), and Constructive and appropriately challenging support (coding units describing the usefulness of additional explanations the teachers use to support the students when support is required). Larger overlaps between categories entailed the integration of multiple categories if reasonable. For instance, we combined Participation possibilities and Autonomy-supportive interaction (see Participation possibilities and autonomy-supportive interaction) which were originally placed in two separate categories. Supplemental categories or subcategories were inductively added if both raters agreed about the fulfillment of the following criteria: (a) the content did not fit into the existing categories, (b) the interviewed students viewed this content as a factor that contributed to their competence satisfaction, (c) the content arose several times, (d) the integration of this content into existing categories would have biased the original categories. One deductive category ( Meta-cognition support ; Praetorius et al., 2018 ) was removed because it did not arise in our sample. Based on Mayring (2016) , we also adapted the coding units retroactively in order to calibrate the coding units to the applied abstraction level of analysis. In line with the iterative character of qualitative content analysis, the analysis steps in Step 9 of Figure 1 were each repeated by coding a further 10% of the interview material until formative intercoder reliability was acceptable ( Mayring and Brunner, 2006 ; Schilling, 2006 ; Mayring, 2014 ; Krippendorff, 2019 ).

For the final coding (Step 10 in Figure 1 ), the 25 interviews were randomly assigned to the two main coders (main coder 1: n = 15 interviews; main coder 2: n = 14 interviews; note that n = 4 of the N = 25 interviews were coded twice in order to perform the summative intercoder reliability check). In order to ensure that the final category system 1 works with interchangeable coders, a third coder (i.e., student assistant in the research field of motivational psychology and educational psychology; combined Bachelor’s degree in linguistics and psychology; advanced double degree in linguistics [Master program] and psychology [Bachelor program]) was involved in the summative intercoder reliability check after a short briefing regarding the final category system (Step 11 in Figure 1 ; Mayring and Brunner, 2006 ; Hayes and Krippendorff, 2007 ; Mayring, 2014 ). Krippendorff’s alpha (α = 0.74; 95% CI [0.71–0.77]) indicated an acceptable intercoder reliability ( Krippendorff, 2019 ).

We subsequently conducted qualitative and quantitative analyses of the interview material (Step 12 in Figure 1 ; Mayring and Brunner, 2006 ; Schilling, 2006 ; Mayring, 2014 ). In the course of the quantitative analyses, we conducted three indicators in order to identify the salience of the categories and subcategories within our sample. According to Schilling (2006) , we analyzed the absolute topic frequencies, which are the absolute frequency of coding for each subcategory across all students. Second, we analyzed how many students had addressed each subcategory in at least one segment, and at least from the perspective of one coder (i.e., person frequency; Schilling, 2006 ). Third, we analyzed the relative distribution of the different subcategories based on the person frequencies.

As has been implicitly addressed, multiple strategies have been used to establish reliability and validity in the present study: Besides a thick description of the study procedures, a standardized coding system, and an intercoder-reliability check ensured reliability ( Hayes and Krippendorff, 2007 ; Mayring, 2014 ; Morse, 2015 ). Regarding validity, triangulation, a continuous peer review, elements of negative case analysis, and the reflection on researcher bias complemented the depicted reliability criteria. Triangulation describes the complementation of multiple investigators, theories, methods, and data with each other to address a research question, and is used to reveal the complexity of investigated phenomena ( Mayring, 2007a ; Morse, 2015 ; Flick et al., 2019 ). A data triangulation took place, since we interviewed interviewees’ visiting two contrasting schools, conducted episodic interviews which explore both interviewees’ past experiences and current concepts, and quantitatively assessed the demographics ( Mayring, 2007a ; Flick et al., 2019 ). An investigator triangulation was given, since multiple stakeholders contributed different perspectives to the present study, for instance, in form of the intercoder-agreement-check ( Mayring, 2007a ; Krippendorff, 2019 ). A theory triangulation was applied, because we confronted the data with the theoretical backgrounds of self-determination theory, mastery goal structure, perceived error climate, teaching quality, and reference norm orientation ( Mayring, 2007a ). A methodological triangulation was part of the study, since the qualitative content analysis represents a hybrid (i.e., combined qualitative and quantitative) or integrated (i.e., a combination of qualitative and quantitative analysis steps in one research design) analysis of qualitative material which additionally combines explorative (inductive) and hypothesis-oriented (deductive) analysis procedures ( Mayring and Brunner, 2006 ; Mayring, 2007a ; Burzan, 2016 ). Concerning negative cases, the coders were attentive to categories that were salient (i.e., positive cases) and non-salient (i.e., negative cases) through the interviewed students’ perspectives ( Morse, 2015 ). Member checks were not applied in this study because they are not unconditionally recommended in interview research (e.g., Morse, 2015 ). Moreover, there are no clear recommendations on how to deal with potential differences between the researchers’ judgments and the participants’ judgments ( Morse, 2015 ).

On average, the students each described 1.36 situations in which they had perceived competence satisfaction ( SD = 0.57; Min = 1; Max = 3). The situations in which the students perceived competence satisfaction most commonly arose in the school subjects Mathematics (29%), History (15%), and English (12%). The students also reported situations out of eight further school subjects in which they had perceived competence satisfaction. On average, students’ current school grades in the reported school subjects were M = 1.85 ( SD = 0.94; Min = 1; Max = 4). In Germany, school grades range from 1 ( very good ) to 6 ( unsatisfactory ).

Table 1 and Figure 2 provide overviews of the final category system describing the factors that contributed to students’ competence satisfaction in class through the students’ perspectives both for the situations and the generalized beliefs and experiences (including the category labels and quantitative analyses). Specifically, Table 1 shows the categories, the absolute topic frequencies, and the relative person frequencies for the categories. Figure 2 illustrates the categories and the relative distributions of the categories (for the three frequency types, see the quantitative frequency indicators in the method section). As a finding, the category system comprised five main categories (e.g., Teaching factors ; including Others ), subsuming 16 categories (e.g., Constructive and appropriately challenging support ), that, in turn, comprised nine subcategories (e.g., Task-focused, constructive feedback ). In the following, we present our findings along the main category sequence Teaching factors , Teacher factors and student-teacher relationship factors , Student factors , and Peer climate and reciprocal peer support as displayed in Table 1 . Within those, we focused on the subcategories and categories with a person frequency higher than 50% and on surprising findings due to limited space. By doing so, we attempted to report the findings that were representative for large amounts of our sample or that gave new directions for future research ( Schilling, 2006 ).

Table 1. Factors contributing to students’ competence satisfaction through students’ perspective.

Figure 2. Relative category distribution of the factors that contribute to students’ competence satisfaction through students’ perspectives. The relative category distribution represents n % of all category codings, based on the absolute person frequencies (i.e., n students that have addressed the respective categories at least in one segment and at least from the perspective of one coder; see Table 1 ). The relative category distributions sum up to 101.29% instead of 100% because, for the sake of clarity, all values have been rounded to the nearest whole number, with one exception. The relative distribution of the category “Improvement-focused, constructive feedback and evaluation” has been rounded to two decimal places in order not to create the impression that its value is 0%.

Teaching factors

In line with our initial deductive category system (see the deductive categories, as presented in Table 1 ), the students reported different Teaching factors (T) as factors that contributed to their competence satisfaction in class. This main category included students’ perceptions of teaching styles that contributed to students’ competence satisfaction and could be divided into eight categories (e.g., Constructive and appropriately challenging support ), with two categories (i.e., Autonomy-supportive teaching ; Feedback and evaluation ) being further divided into three subcategories (e.g., Participation possibilities and autonomy-supportive interaction ; e.g., Task-focused, constructive feedback ).

Among the Teaching factors , Constructive and appropriately challenging support as well as Clear communication and high-quality explanations were the two most salient (sub-) categories from the views of our participants. Referring to the category Constructive and appropriately challenging support (T1), the students described that they felt competent because their teachers helped them at an appropriate challenge level when they required support. For instance, the students reported to feel competent because the teachers repeatedly explained content when something was unclear. Moreover, the students described that they felt competent because teachers created opportunities for asking questions and discussing students’ questions (e.g., female student AT04, 15 years old, reported in interview segment 45 “[A teaching attribute that helps me feel competent is that] if you have questions you can approach the teacher at any time.”). In the category Clear communication and high-quality explanations (T2), the students described that they felt competent because of teacher behaviors, such as making expectancies and procedures clear or explaining instructions and the material in an understandable manner. For example, the students frequently stressed that they felt competent because the teachers appropriately explained the learning material or instructions (e.g., female student SD03, 14 years old, reported in interview segment 49 “Simply if the task is well explained, the assignment.”; and female student KM01, 15 years old, stated in interview segment 80 “[Teachers who help me feel competent] above all, they explain things well.”).

A further frequent category was the category Optimal challenge for student and regarding school requirements (T3). Within this category, the students described that they felt competent because teachers set appropriate challenge levels for the students but also for the mastering of upcoming school requirements such as exams. Specifically, an appropriate challenge level was defined as neither under- nor overdemanding for the individual student while corresponding to the difficulty level required by respective curricula. An important difference between this category and the appropriately challenging support in the category Constructive and appropriately challenging support was that in Optimal challenge for student and regarding school requirements , the students perceived the challenge level as appropriate when no support was required. In Constructive and appropriately challenging support , the students described that their teachers successfully identified on which challenge level they may settle their support when support was required.

Turning to the next category Feedback and evaluation (T4), Task-focused, constructive feedback (T4.1) was the most frequently mentioned reason as to why the students felt competent. This subcategory was characterized by positive and informative teacher feedback, and by meeting the teachers’ expectations. Within this category, the students often described that they felt competent because the teachers generally gave sufficient feedback, or specifically gave positive feedback (e.g., female student SM03, 14 years old, reported in interview segment 4 “[I think I might have felt competent in this situation because] maybe being praised by the teacher has given me sort of a push.”). Additionally, we subsumed grades under the inductive subcategory Feedback through grades or scores (T4.2) since the students frequently reported them as helpful in order to feel competent. The students thereby frequently highlighted the informative character of grades (e.g., female student KM01, 15 years old, stated in interview segment 34 “[This gave me another confirmation that I had, uh, written a good test]. Because there was also a grade underneath, yes.”). Surprisingly, teacher feedback specifically oriented toward the intraindividual reference norm orientation ( Improvement-focused, constructive feedback and evaluation [T4.3]) seemed less relevant for the interviewed students’ competence satisfaction. It was only mentioned once and defined by teacher feedback that highlights improvements and individual developments over time.

As expected, the students frequently described some key elements of teacher autonomy support: Referring to the category Autonomy-supportive teaching (T5), the most frequent subcategory was Participation possibilities and autonomy-supportive interaction (T5.1). In this subcategory, the students described that they felt competent because teachers provided opportunities to actively interact with the classroom environment (including teacher–student interactions), and because teachers engaged in an active, respectful, and interested dialogue with their students. For example, the students highlighted the importance of opportunities for every student to actively engage in class (e.g., male student AT02, 15 years old, explained in interview segment 12 “[I think I felt competent in that situation because the teacher felt you were capable of doing that] and gave you the chance.”), and respectful teacher-student-interactions (e.g., female student AV03, 15 years old, stated in interview segment 52 “[Teachers who help me feel competent] deal respectfully with the students.”).

Beyond that, the students frequently emphasized factors subsumed under the next categories Classroom management and Opportunities for collaborative working and peer interaction as reasons for their competence satisfaction in class. The category Classroom management (T6) described that teachers effectively organized the classroom environment in order to establish a high and productive time-on-task for the students. For example, the students mentioned that they felt competent when teachers created a quiet working atmosphere, e.g.,

[To make me feel competent teachers could] make sure that it is quiet in class [because sometimes I have the feeling that many teachers somehow struggle to be assertive so that many students in my class don’t care and don’t listen to them. Then, it’s difficult to keep them quiet]. (Female student AT04, 15 years old, in interview segment 44)

Furthermore, the students felt competent when teachers succeeded in maintaining rules and guidelines, e.g.,

[[Teachers who help me feel competent] do not, it sounds harsh now, abuse their power. Rather, they are relatively on an equal footing.] But nevertheless, that they are still able to take action, I would say. That’s pretty important to me. (Female student CM11, 15 years old, interview segment 53)

The category Opportunities for collaborative working and peer interaction (T7) included the promotion of teamwork opportunities and interaction among classmates in class. Here, the students frequently reported feeling competent when teachers created opportunities for peers to interact with each other, such as in small group working or exchange among seating partners. Moreover, the students often felt competent when teachers created opportunities for classroom conversations with the whole class, for instance, in class discussions (e.g., female student AT04, 15 years old, reported in interview segment 54 “Not simply working individually on any tasks but maybe having more of a class discussion [helps me feel competent in class again].”).

Teacher factors and student–teacher-relationship factors

However, in addition to the teaching factors, the students described Teacher factors and student-teacher-relationship factors (TR; describing teachers’ person-related competencies, characteristics, traits, and attitudes that contributed to students’ competence satisfaction through the students’ perspectives) as relevant reasons for perceiving competence satisfaction in class. This inductive main category involved three categories (e.g., Teacher personality, characteristics, and attitudes ). For instance, several students told the interviewers that they felt competent because teachers mastered their school subject well and evaluated students competently (i.e., Teacher’s professional and diagnostic competence [TR2]). A positive student-teacher-relationship (i.e., Positive student-teacher-relationship [TR3]) was also mentioned several times. It was characterized by the student’s positive attitude toward the teacher and by the student’s perception of a good relationship with the teacher.

However, among our participants, the most salient category in this main category was Teacher personality, characteristics, and attitudes (TR1). This category was characterized by any teacher characteristics, personality traits, attitudes, and understandings of the teacher role that did not describe specific teaching behaviors or competencies. For example, the students felt competent because the teachers were generally kind and affable. Additionally, the students frequently reported that they felt competent because of relaxed teachers who were not too strict (e.g., female student SD03, 14 years old, stated in interview segment 53 “[Teachers who help me feel competent are] not necessarily too strict.”).

Student factors

Moreover, we inductively added the main category Student factors (S) into the category system. This main category described students’ own skills, characteristics, attributes and attitudes that contributed to their competence satisfaction. It included six categories (e.g., Student motivation and engagement ), with one of those categories ( Current mastery experience ) being further divided into three subcategories (e.g., Notion of a currently successful interaction with teaching or exam material ).

In the main category Student factors , two factors were salient for 100% of the participants of our study: Student motivation and engagement as well as Notion of a currently successful interaction with teaching or exam material . With regard to the category Student motivation and engagement (S1), the students stated that they traced their competence satisfaction to their own motivation, preparation, and engagement such as in class or at home (e.g., female student KM01, 15 years old, explained in interview segment 19 “[I believe the reason why I felt competent on my part was] above all, that I personally tried to prepare myself for it in advance.”). In the category Notion of a currently successful interaction with teaching or exam material (S2.1), the students described that they perceived competence satisfaction because they were successfully interacting with teachers, the teaching material, or with the exam material. For example, the students described that they felt competent because they understood the learning material, were capable of doing something, or recognized that they had done or understood something correctly (e.g., female student KM01, 15 years old, commented in interview segment 4 “I realized that what I did was right.”).

In contrast to the related Teaching factors subcategory Improvement-focused, constructive feedback and evaluation , more than 50% of our sample also considered factors describing the Notion of own learning improvement (S2.2) as a reason for their competence satisfaction in class. This subcategory was characterized by descriptions in which the students compared their current actions or achievement with their prior actions or achievement, and in which they recognized learning improvements. For example, the students chose previous situations and the beginnings of current situations as benchmarks. They also explicitly addressed learning gains or improvements (e.g., female student SD03, 14 years old, reported in interview segment 8 “As opposed to back in the day, I improved.”).

Turning to the two last Student factors categories which were relevant for more than 50% of our participants, the two categories Successful emotional coping and Generalized self-perceptions of competence and control beliefs both described factors that went beyond one single situation, stressing the dynamics of classroom environments. In Successful emotional coping (S3), the students described reasons for the transition from a competence frustration to a brighter side of students’ competence satisfaction (i.e., the reduction of competence frustration or the beginnings of competence satisfaction) through specific thoughts, emotions or behaviors. For instance, the students reported that they felt competent because of putting situations behind them, positive thoughts, or relativizing thoughts, e.g.,

When I see that I got something right or that I was able to participate after all, I try not to let the bad drag me down. Instead, I try to focus on the bigger picture, for example, [to see] that I did better in another lesson, that it was just one lesson and I can still prove myself in the next lesson. (Female student SW12, 15 years old, in interview segment 42)

In the category Generalized self-perceptions of competence and control beliefs (S4), the students described that they felt competent because they were generally competent or had beneficial self-perceptions of competence (e.g., in a specific subject, topic, or task type) which went beyond one single situation in which the students felt competent. Additionally, this category included students’ general beliefs about being able to influence their own competence satisfaction or school outcomes. For example, the students stated that they felt competent because they generally felt that they were proficient or confident in a school subject or topic (e.g., female student DJ10, 15 years old, explained in interview segment 26 “[Because] I actually feel pretty confident in this subject.”).

Peer climate and reciprocal peer support

Lastly, more than 80% of the interviewed students highlighted the importance of Peer climate and reciprocal peer support (P) as a factor that contributed to their competence satisfaction in class. This main category described a respectful, collaborative and learning-facilitating atmosphere among peers in which the students could or would help each other. For instance, the students reported that they felt competent because they were capable of helping other students (e.g., male student KA12, 15 years old, stated in interview segment 6 “Because I could help [others] with my skills.”). Also, the students felt competent because peers explained the learning material to them (e.g., female student SC06, 14 years old, reported in interview segment 66 “[To feel competent again] [Yes actually also, like, exchange with others] so that maybe not only the teacher, but also classmates explain things to you.”). An important characteristic of this main category was that the students traced the responsibility for these occurrences to interactions with their peers or to their peers but not to their teachers, or to the teachers’ teaching behaviors.

In this study, we attempted to widen researchers’ view on factors that contribute to students’ competence satisfaction at school by taking a qualitative, integrative, and student-oriented perspective. Specifically, we aimed to enrich and extend existing SDT-knowledge on which factors contribute to students’ competence satisfaction in realistic classroom settings. For this purpose, we combined a data-driven (explorative) and a theory-driven (descriptive) research design in which we integrated existing SDT assumptions as well as additional theoretical frameworks (i.e., classroom goal structure, perceived error climate, teaching quality, reference norm orientations; integrative approach). As one main finding of our qualitative content analysis approach, we identified and systematized 23 data- and theory-based factors (i.e., Teaching factors, Teacher factors and student–teacher-relationship factors, Student factors, Peer climate and reciprocal peer support) that contributed to students’ competence satisfaction in classroom contexts through the interviewed students’ perspectives. The most frequent categories were Student motivation and engagement , Notion of a currently successful interaction with teacher, teaching or exam material , Clear communication and high-quality explanations , as well as Constructive and appropriately challenging support . In contrast, the least frequent categories were Improvement-focused, constructive feedback and evaluation , Opportunities to choose , and Positive student-teacher-relationship (besides Others ). Concluding, our data-based perspective first showed additional factors that seem to be beneficial for students’ competence satisfaction (e.g., student factors) through the interviewed students’ perspectives. Second, our theory-based perspective complemented quantitative SDT findings on need support and offered new conceptual insights into which teaching practices beyond the ones anchored in SDT (e.g., the fostering of a high mastery goal structure in class, teaching practices that characterize a high teaching quality) might facilitate students’ competence satisfaction at school through students’ perspectives (e.g., Rheinberg, 1980 , 1983 ; Patall et al., 2008 ; Steuer et al., 2013 ; Lüftenegger et al., 2014 , 2017 ; Dickhäuser et al., 2017 ; Praetorius et al., 2018 ; Vasconcellos et al., 2020 ). In the following, we present our specific findings along the sequence Teaching factors, Teacher factors and student-teacher-relationship factors, Student factors , and Peer climate and reciprocal peer support .

Teaching factors contributing to students’ competence satisfaction

Expectedly, teaching factors were the most salient reasons for the interviewed students’ competence satisfaction (accounting for 48.70% of the relative category distribution). Based on SDT as our primary theoretical framework, we discuss our findings regarding the teaching factors along the need support variables structure, autonomy support, and relatedness support ( Ryan and Deci, 2017 ). Moreover, we present additional factors we revealed within the teaching factors that could extend the need support literature in future research. The additional theoretical frameworks considered in our category system (i.e., achievement goal theory, perceived error climate, teaching quality, reference norm orientation theory) are used to extend existing knowledge on which teaching practices might be beneficial for students’ competence satisfaction through students’ views in the context of SDT.

In line with SDT, the categories Clear communication and high-quality explanations , Constructive and appropriately challenging support , as well as Task-focused, constructive feedback underpin the importance of structure for students’ competence satisfaction in class ( Jang et al., 2010 ; Ryan and Deci, 2017 , 2020 ; Aelterman et al., 2019 ; Vasconcellos et al., 2020 ). Together with the categories Optimal challenge for student and regarding school requirements as well as Classroom management , which are also discussable along the structure construct ( Jang et al., 2010 ; Reeve, 2015 ; Ryan and Deci, 2017 , 2020 ; Aelterman et al., 2019 ), these findings suggest that students might feel competent when teachers make expectations clear, give overviews, and provide appropriate help when necessary. Moreover, they emphasize the importance of positive and informative feedback, neither over- nor under-challenging tasks, and transparent as well as consistent rules. This description of competence-supportive teaching is in line with existing conceptualizations of structure in SDT ( Jang et al., 2010 ; Aelterman et al., 2019 ; Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ).

Along with the benefits of qualitative approaches ( Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ), we additionally revealed data-based factors by which the structure construct might be enriched in the educational setting. Whereas conceptualizations of clarifying structure have focused on overviews, transparent expectations, and the monitoring of students’ progress, our findings underlined the importance of understandable, precise, and sufficiently detailed explanations of the learning material for the students’ competence satisfaction (i.e., Clear communication and high-quality explanations ; Aelterman et al., 2019 ). Moreover, our participants emphasized the indirect link of explanation quality and competence satisfaction via students’ understanding of the learning material, prompting future studies to consider both students’ motivational and cognitive functioning (e.g., Manganelli et al., 2019 ). This was particularly evident from the frequent consecutive occurrence of the categories Clear communication and high-quality explanations and Notion of a currently successful interaction with teaching or exam material across many participants in our study. Regarding the Constructive and appropriately challenging support , we conclude that an optimal challenge level might be important not only when providing tasks but also when providing help, which extends typical conceptualizations in SDT research ( Jang et al., 2010 ; Guay et al., 2017 ; Aelterman et al., 2019 ; Ryan and Deci, 2020 ). Our findings also prompt future research to further investigate whether an appropriate challenge level should not only be defined regarding students’ current possibilities but additionally considering the challenge level of upcoming school requirements (e.g., final exams; Jang et al., 2010 ; Aelterman et al., 2019 ; Ryan and Deci, 2020 ).

To further enrich the structure construct, a theory-based and integrative perspective has been taken. The interviewed students mentioned several characteristics out of existing theoretical frameworks (e.g., teaching quality) as factors that contributed to their competence satisfaction in class (e.g., creating a quiet working atmosphere as an indicator for classroom management; Praetorius et al., 2018 ). These might extend typical conceptualizations in the need support literature. In line with existing research, our findings indicate that it is worthwhile to further investigate the link of mastery goal structure and students’ competence satisfaction ( Kavussanu and Roberts, 1996 ; Cox and Williams, 2008 ; Quested and Duda, 2009 ; Halvari et al., 2011 ). They also prompt researchers to study whether the perceived error climate ( Steuer et al., 2013 ), the teaching quality ( Praetorius et al., 2018 ), and teachers’ reference norm orientations ( Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ; Lohbeck and Freund, 2021 ) might be related to students’ competence satisfaction in quantitative research. In order to facilitate the follow-up of our findings, Table 2 gives an overview about the theoretical frameworks and their dimensions that, based on our study, provide a fruitful foundation for further investigations of factors that can be beneficial for students’ competence satisfaction from a structure perspective. As can be seen in Table 2 , the teaching factors derived from the different theories could be classified into common categories based on the interviewed students’ perspectives in the present research. These results are promising for future research, as this should facilitate to answer the call for integrative recommendations to practitioners that overcome conceptual overlaps between different motivational theories ( Anderman, 2020 ).

Table 2. Dimensions describing the factors that contribute to students’ competence satisfaction from a structure perspective.

However, even though we retained the theory-based category Improvement-focused, constructive feedback and evaluation in our category system for transparency reasons and because it arose once in our sample, feedback focusing on students’ intraindividual improvement was not salient for students’ competence satisfaction in our study. At first sight, this stands in contrast to existing literature ( Rheinberg, 1980 , 1983 ; Reeve, 2015 ; Dickhäuser et al., 2017 ; Rheinberg and Krug, 2017 ; Ryan and Deci, 2017 ). However, several methodological (e.g., the high specificity of this category compared to the other feedback categories) and theoretical explanation approaches (e.g., small effect sizes of teachers’ intraindividual reference norm orientation; context specificities under which we conducted this study) might have caused this finding. For instance, teachers might rarely be oriented toward the intraindividual reference norm in the regular school system in Germany where this study has been conducted. Alternatively, the students might not have noticed the teachers’ efforts to focus on intraindividual improvement when they gave their feedback. Specifically, in line with some initial difficulties to differentiate between the categories Task-focused, constructive feedback as well as Improvement-focused, constructive feedback and evaluation in our study, several students might not have differentiated between constructive, task-focused, and improvement focused feedback in the present study. These possible explanations might have made it difficult to link teachers’ reference norm orientations to students’ competence satisfaction within the applied study design. From an SDT perspective, further research (accounting for context-specific influences, e.g., by intervention studies) is required in order to understand whether intraindividual comparison standards are related to students’ competence satisfaction in classroom contexts.

Autonomy support

In line with SDT, the students additionally mentioned that participation opportunities, respectful teacher-student interactions, and teachers who were responsive to students’ views, needs, and interests facilitated their competence satisfaction in class. This is in line with conceptualizations of attuning and participative autonomy support as well as with empirical SDT findings on individuals’ competence satisfaction ( Patall et al., 2008 , 2018 ; Jang et al., 2010 ; Ryan and Deci, 2017 , 2020 ; Eckes et al., 2018 ; Aelterman et al., 2019 ).

However, matching some inconsistent findings in past research ( Patall et al., 2014 ; Steingut et al., 2017 ; Vasconcellos et al., 2020 ), some autonomy support facets were more salient among the interviewed students ( Participation possibilities and autonomy-supportive interaction ) than others ( interestingness and relevance , opportunities to choose ). One possible explanation could be that opportunities for engaging in active interactions with classroom environments (i.e., Participation possibilities and autonomy-supportive interaction) might represent more proximal reasons for the students’ competence satisfaction compared to the sense of being self-determined causers of such active student-environment-interactions (e.g., opportunities to choose ). Other explanation approaches might be that interestingness, relevance, and opportunities to choose are not salient teaching practices in Germany or that some students might have overlooked their teachers’ efforts to provide opportunities to choose or rationales. As one fruitful approach to understand how students perceive autonomy-supportive practices and their influence on students’ competence satisfaction while controlling for context specificities, one could manipulate specific autonomy support facets within qualitative intervention studies and explore students’ competence experiences (e.g., via open questions). Evidently, our findings should also be investigated considering several moderating and mediating processes (e.g., Patall et al., 2014 ; Steingut et al., 2017 ; Vasconcellos et al., 2020 ).

Furthermore, our findings revealed additional factors that might improve existing prediction results for students’ competence satisfaction in future studies. For instance, from a data-based perspective, the students felt competent when teachers gave equal opportunities to all students to participate in class. This approach extends past need support conceptualizations ( Jang et al., 2010 ; Ryan and Deci, 2017 , 2020 ; Aelterman et al., 2019 ), is in line with research on adaptive teaching ( Corno, 2008 ), and prompts SDT researchers to complement existing competence satisfaction research by considering teaching equality beyond differentiated instruction ( Deci, 2009 ; Roy et al., 2013 ; Guay et al., 2017 ).

In line with structure, autonomy support might additionally be refined based on our theory-based approach. Specifically, the interviewed students stressed factors that described mastery goal structure, teaching quality, and teachers’ intraindividual reference norm orientations as salient reasons for their competence satisfaction at school ( Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ; Lüftenegger et al., 2017 ; Praetorius et al., 2018 ). Therefore, our findings give first hints that existing autonomy support conceptualizations might be extended by dimensions out of those frameworks. In order to facilitate the transfer of our findings to quantitative research, Table 3 shows the theoretical frameworks and the specific dimensions that, following our findings, seem to be a fruitful approach in order to investigate which factors are positively linked to students’ competence satisfaction from an autonomy support perspective. As discussed for Table 2 , the analyzed teaching factors derived from the different theories could be classified into common categories in the present research. This potential integrability might facilitate the derivation of recommendations for practitioners in future research ( Anderman, 2020 ).

Table 3. Dimensions describing the factors that contribute to students’ competence satisfaction from an autonomy support perspective.

Relatedness support

Within the categories Constructive and appropriately challenging support, Participation possibilities and autonomy supportive interaction as well as Opportunities for collaborative working and peer interaction , the students also viewed factors attributable to relatedness support as factors that contributed to their competence satisfaction which is in line with first hints in the literature ( Vasconcellos et al., 2020 ). In line with existing relatedness support conceptualizations (e.g., opportunities for individualized teacher–student conversations; Reeve, 2015 ; Sparks et al., 2015 , 2016 ), for instance, the students felt competent because their teachers had an approachable, helpful, and interactive teaching style. They also felt competent because teachers invited the students to interact with each other (e.g., by working in groups). Hence, our findings prompt future SDT research to focus on relatedness support as a potential predictor of students’ competence satisfaction.

Additionally, our findings provide first evidence that might broaden existing relatedness support conceptualizations. For instance, through the interviewed students’ perspectives, it seemed essential for students’ competence satisfaction that teachers and students met as equals, that students felt treated fairly by the teachers, and that the teachers were patient with students’ learning difficulties. For example, the students found it helpful when teachers actively provided voluntary opportunities for getting additional assistance (e.g., building small groups for whom teachers give additional assistance). This is partly in line with Reeve (2015) who proposed teacher patience to be an autonomy-supportive measure.

From our theory-based perspective, we conclude that existing SDT knowledge on relatedness support might be refined based on the depicted theoretical frameworks. Specifically, the categories Opportunities for collaborative working and peer interaction , Participation possibilities and autonomy-supportive interaction , and Constructive and appropriately challenging support revealed several teaching practices that have been elaborated in the TARGET framework (e.g., authority; Lüftenegger et al., 2017 ), in the student support and cognitive activation dimensions out of the teaching quality framework (e.g., discursive and co-constructive learning; Praetorius et al., 2018 ), in reference norm orientation theory ( Rheinberg, 1980 , 1983 ; Dickhäuser et al., 2017 ), and in perceived error climate research ( Steuer et al., 2013 ). In view of the requirements for further research on relatedness support in SDT, it might be helpful for future research to integrate those factors into existing relatedness support conceptualizations ( Reeve, 2015 ; Sparks et al., 2015 , 2016 ). Future studies might also investigate whether our qualitative findings can be replicated in quantitative research. Attempting to facilitate the follow-up of our findings, Table 4 represents an overview of our findings concerning the theoretical frameworks and their dimensions that seem to contribute to students’ competence satisfaction from a relatedness support perspective. As discussed for Tables 2 and 3 , the interviewed students’ perspectives additionally indicate that, based on the analyzed teaching factors, it might be fruitful as well as feasible for future research to elaborate integrative recommendations for practitioners with regard to students’ competence satisfaction ( Anderman, 2020 ).

Table 4. Dimensions describing the factors that contribute to students’ competence satisfaction from a relatedness support perspective.

However, in line with past SDT research ( Ahn et al., 2019 ; Vasconcellos et al., 2020 ), our category system indicated overlaps between relatedness support and the other need support variables from the interviewed students’ perspective. For example, the category Participation possibilities and autonomy-supportive interaction might be discussed both from a relatedness support perspective and an autonomy support perspective. Moreover, the category Constructive and appropriately challenging support can be viewed from a relatedness support perspective as well as from a structure perspective. Therefore, our findings prompt future research to investigate the empirical separability of structure, autonomy support, and relatedness support in the context of students’ competence satisfaction.

Additional factors contributing to students’ competence satisfaction

Extending past SDT research that mainly focused on teaching practices (e.g., Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ; Vasconcellos et al., 2020 ), our findings also suggest that, from the interviewed students’ perspectives, additional factors beyond the teaching practices (accounting for 51.30% of the relative category distribution in our study when including Others ) might be considered in order to understand why students’ competence satisfaction subjectively arises in class. In the following, we discuss the most salient additional factors through the interviewed students’ perspectives.

Based on the main category Teacher factors and student–teacher-relationship factors , teacher factors might be a fruitful approach in understanding students’ perspectives on why their competence satisfaction arises in class (accounting for 9.22% of the relative category distribution in our study). For example, within the category Teacher personality, characteristics, and attitudes , the students described teacher agreeableness (i.e., kindness) and teacher characteristics presumably interpretable as generalized autonomy and relatedness supportive, as well as non-controlling orientations (e.g., “generally being attentive to students’ needs”) as factors that contributed to their competence satisfaction. This is in line with teacher personality research within and beyond SDT ( Ryan and Deci, 2017 ; Kim et al., 2018 ; Reeve et al., 2018 ). According to teaching quality research and SDT research on relatedness support, the students also frequently reported teacher humor, enthusiasm, and teacher motivation as reasons for their competence satisfaction in class ( Kunter, 2013 ; Sparks et al., 2015 , 2016 ; Baier et al., 2019 ; Shahid and Ghazal, 2019 ; Ahn et al., 2021 ).

However, some inconsistent findings on the links between teacher personality and students’ motivational functioning prompt future research to follow up on our findings ( Kim et al., 2018 ; Reeve et al., 2018 ; Baier et al., 2019 ; Khalilzadeh and Khodi, 2021 ). Moreover, students may not have differentiated between teaching behaviors and teachers’ orientations in our study, urging researchers to interpret our findings with caution.

Through the interviewed students’ perspectives, student factors seemed crucial in explaining why students’ competence satisfaction arises in class (accounting for 34.87% of the relative category distribution). For instance, the students viewed their own motivation and engagement as one of the most important preconditions for their competence satisfaction, suggesting that students’ competence satisfaction might be considered as a predictor and as an outcome of motivation and engagement in future research. This is in line with longitudinal studies in which students’ need fulfillment and competence satisfaction have predicted students’ motivation and engagement, as well as vice versa ( Papaioannou et al., 2006 ; Reeve and Lee, 2014 ).

Based on the main category Notion of a currently successful interaction with teacher, teaching or exam material , we conclude that, from students’ views, it seems essential for students’ competence satisfaction that students subjectively notice their own competence in current situations. This is in line with the hierarchical model of intrinsic and extrinsic motivation and with studies in which students who performed above-average did not necessarily feel competent ( Miserandino, 1996 ; Vallerand, 1997 ). However, the conceptual similarity of this category with students’ competence satisfaction might indicate that it revealed indicators rather than reasons for students’ competence satisfaction ( White, 1959 ; Ryan and Deci, 2017 , 2020 ). Although we rigorously defined our coding units (i.e., reasons for students’ competence satisfaction; Mayring, 2014 ), future studies should address the empirical separability of the factors described in this category from students’ competence satisfaction. By doing so, one might disentangle definitional and preceding aspects of students’ competence satisfaction in class.

Peer climate and the reciprocal peer support were further additional factors that contributed to the students’ competence satisfaction in class (accounting for 6.05% of the relative category distribution in our study). For instance, the students perceived the capability to help others, getting help from peers, asking of questions, and exchanging of ideas, views, and information among peers as reasons for their competence satisfaction in class. In line with first hints in the literature ( Steuer et al., 2013 ; Vasconcellos et al., 2020 ), our findings thus shed light on how peer climate and peer interactions might influence students’ competence satisfaction in classroom contexts. Due to scarce research on how peer factors and students’ motivational processes interact in class ( Núñez and León, 2015 ), our findings widen researchers’ view on why students’ competence satisfaction arises in class. They prompt future studies to focus on peer factors and peer interactions.

Limitations

Despite our promising findings, some limitations must be addressed. First, qualitative research is object to researcher biases (e.g., sampling effects, anticipations, unconscious biases; Morse, 2015 ). For instance, although the applied qualitative content analysis approach is a transparent, rigorous, and rule-oriented approach to analyze qualitative material ( Mayring, 2014 ), the interpretative coding of interview material remains a subjective process which can lead to subjective bias ( Morse, 2015 ). Second, an important validity concern in qualitative research is to aptly describe the investigated phenomenon ( Morse, 2015 ). In the present study, we investigated the interviewed students’ subjective experiences with and generalized beliefs about the factors that contribute to their competence satisfaction in class. According to Mayring (2007b) , we hence may speculate that students in similar schools, in a similar age, in similar school subjects, and in similar life circumstances may report similar reasons for their competence satisfaction in class. However, the generalizability of the identified factors remains to be investigated since qualitative content analysis does not make claims of generalizability ( Krippendorff, 1989 ; Mayring, 2007b ). Furthermore, potential threats to the accurate description of the investigated phenomenon have to be considered. For example, although we conceptualized our interviews along typical SDT definitions ( White, 1959 ; Deci and Ryan, 2000 ; Ryan and Deci, 2017 , 2020 ), the applied operationalization of students’ competence satisfaction might be confounded with other self-perceptions of competence. In order to verify whether we described the phenomenon of interest, future studies should test whether our qualitative findings are replicable, for instance, in quantitative studies with well-validated SDT questionnaires (e.g., Heissel et al., 2018 ). Third, conclusions on whether the identified factors can predict students’ competence satisfaction in addition to structure, autonomy support, and relatedness support have yet to be drawn. Last, even though our study offered important insights into students’ perspectives on which factors contribute to their competence satisfaction based on a limited and purposeful sample, it was beyond the scope of this study to control for context-specific influences. For instance, the students may not have had the possibility to feel competent because of specific teaching practices if their teachers did not implement these practices in class. Therefore, future studies should combine the gains of qualitative approaches such as in our study with research designs which allow causal inferences (e.g., mixed-methods intervention studies).

Following existing calls for qualitative research and giving insights into students’ views, this interview study explored students’ perspectives on which factors contribute to their competence satisfaction in class ( Ryan and Deci, 2020 ; Vansteenkiste et al., 2020 ). From an SDT point of view, we first conclude that teaching factors within and beyond SDT were beneficial for students’ competence satisfaction from the students’ perspectives. Second, additional factors going beyond students’ perceptions of teaching practices, such as students’ perceptions of student factors (e.g., students’ motivation and engagement), teacher and student–teacher-relationship factors (e.g., teacher kindness), and peer climate factors (e.g., helping each other), played essential roles for the development of students’ competence satisfaction at school from the students’ perspectives. From a cross-theoretical point of view, our study shows the benefits of taking a qualitative, hybrid (or: integrated), integrative, and student-oriented perspective. The results of this study do not only enrich existing need-supportive measures by our integrative approach. They might also give new directions for the depicted additional theoretical backgrounds (i.e., classroom goal structure, perceived error climate, teaching quality, reference norm orientations). That is, the frameworks used in our study might benefit from integrating need-supportive measures anchored in SDT to enrich existing conceptualizations, and improve existing prediction results for the outcomes relevant to these frameworks (e.g., students’ reactions to errors in the error climate research; Steuer et al., 2013 ). This study might additionally inspire future research to reduce gaps not only within theoretical backgrounds by considering additional theoretical backgrounds, as in our study. In line with Anderman (2020) , it might also inspire researchers to clarify differences and commonalities between related theoretical frameworks.

Data availability statement

The datasets presented in this article are not readily available because the participants were assured the raw data would remain confidential and would not be shared. This was necessary due to the sensitive nature of the questions asked in this study. Requests to access the datasets should be directed to NR, [email protected] .

Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Bielefeld University. Written informed consent to participate in this study was provided by the participants and, if necessary by legal law, by the participants’ legal guardian/next of kin.

Author contributions

NR developed the idea and study design (conceptualization), provided the study materials (investigation), and performed the data curation, visualization, methodology, writing of the original draft, as well as the project administration. NR, NG, and MW supervised the data acquisition which was conducted by two student teachers in the course of their master’s theses. NR and RN analyzed the data. SF supervised the project and provided the resources. All authors contributed to the article and approved the submitted version.

We acknowledge the financial support of the German Research Foundation (DFG) and the Open Access Publication Fund of Bielefeld University for the article processing charge.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.928801/full#supplementary-material

• ^ The final category system can be obtained from the corresponding author of this study.

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Keywords : competence satisfaction, need support, mastery goal structure, perceived error climate, reference norm orientation, teaching quality

Citation: Reymond NC, Nahrgang RG, Großmann N, Wilde M and Fries S (2022) Why students feel competent in the classroom: A qualitative content analysis of students’ views. Front. Psychol. 13:928801. doi: 10.3389/fpsyg.2022.928801

Received: 26 April 2022; Accepted: 16 September 2022; Published: 13 October 2022.

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*Correspondence: Nadia Catherine Reymond, [email protected]

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• Published: 02 December 2020

Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program

• Locke Davenport Huyer   ORCID: orcid.org/0000-0003-1526-7122 1 , 2   na1 ,
• Neal I. Callaghan   ORCID: orcid.org/0000-0001-8214-3395 1 , 3   na1 ,
• Sara Dicks 4 ,
• Edward Scherer 4 ,
• Andrey I. Shukalyuk 1 ,
• Margaret Jou 4 &
• Dawn M. Kilkenny   ORCID: orcid.org/0000-0002-3899-9767 1 , 5  

npj Science of Learning volume  5 , Article number:  17 ( 2020 ) Cite this article

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The multi-disciplinary nature of science, technology, engineering, and math (STEM) careers often renders difficulty for high school students navigating from classroom knowledge to post-secondary pursuits. Discrepancies between the knowledge-based high school learning approach and the experiential approach of future studies leaves some students disillusioned by STEM. We present Discovery , a term-long inquiry-focused learning model delivered by STEM graduate students in collaboration with high school teachers, in the context of biomedical engineering. Entire classes of high school STEM students representing diverse cultural and socioeconomic backgrounds engaged in iterative, problem-based learning designed to emphasize critical thinking concomitantly within the secondary school and university environments. Assessment of grades and survey data suggested positive impact of this learning model on students’ STEM interests and engagement, notably in under-performing cohorts, as well as repeating cohorts that engage in the program on more than one occasion. Discovery presents a scalable platform that stimulates persistence in STEM learning, providing valuable learning opportunities and capturing cohorts of students that might otherwise be under-engaged in STEM.

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Introduction.

High school students with diverse STEM interests often struggle to understand the STEM experience outside the classroom 1 . The multi-disciplinary nature of many career fields can foster a challenge for students in their decision to enroll in appropriate high school courses while maintaining persistence in study, particularly when these courses are not mandatory 2 . Furthermore, this challenge is amplified by the known discrepancy between the knowledge-based learning approach common in high schools and the experiential, mastery-based approaches afforded by the subsequent undergraduate model 3 . In the latter, focused classes, interdisciplinary concepts, and laboratory experiences allow for the application of accumulated knowledge, practice in problem solving, and development of both general and technical skills 4 . Such immersive cooperative learning environments are difficult to establish in the secondary school setting and high school teachers often struggle to implement within their classroom 5 . As such, high school students may become disillusioned before graduation and never experience an enriched learning environment, despite their inherent interests in STEM 6 .

It cannot be argued that early introduction to varied math and science disciplines throughout high school is vital if students are to pursue STEM fields, especially within engineering 7 . However, the majority of literature focused on student interest and retention in STEM highlights outcomes in US high school learning environments, where the sciences are often subject-specific from the onset of enrollment 8 . In contrast, students in the Ontario (Canada) high school system are required to complete Level 1 and 2 core courses in science and math during Grades 9 and 10; these courses are offered as ‘applied’ or ‘academic’ versions and present broad topics of content 9 . It is not until Levels 3 and 4 (generally Grades 11 and 12, respectively) that STEM classes become subject-specific (i.e., Biology, Chemistry, and/or Physics) and are offered as “university”, “college”, or “mixed” versions, designed to best prepare students for their desired post-secondary pursuits 9 . Given that Levels 3 and 4 science courses are not mandatory for graduation, enrollment identifies an innate student interest in continued learning. Furthermore, engagement in these post-secondary preparatory courses is also dependent upon achieving successful grades in preceding courses, but as curriculum becomes more subject-specific, students often yield lower degrees of success in achieving course credit 2 . Therefore, it is imperative that learning supports are best focused on ensuring that those students with an innate interest are able to achieve success in learning.

When given opportunity and focused support, high school students are capable of successfully completing rigorous programs at STEM-focused schools 10 . Specialized STEM schools have existed in the US for over 100 years; generally, students are admitted after their sophomore year of high school experience (equivalent to Grade 10) based on standardized test scores, essays, portfolios, references, and/or interviews 11 . Common elements to this learning framework include a diverse array of advanced STEM courses, paired with opportunities to engage in and disseminate cutting-edge research 12 . Therein, said research experience is inherently based in the processes of critical thinking, problem solving, and collaboration. This learning framework supports translation of core curricular concepts to practice and is fundamental in allowing students to develop better understanding and appreciation of STEM career fields.

Despite the described positive attributes, many students do not have the ability or resources to engage within STEM-focused schools, particularly given that they are not prevalent across Canada, and other countries across the world. Consequently, many public institutions support the idea that post-secondary led engineering education programs are effective ways to expose high school students to engineering education and relevant career options, and also increase engineering awareness 13 . Although singular class field trips are used extensively to accomplish such programs, these may not allow immersive experiences for application of knowledge and practice of skills that are proven to impact long-term learning and influence career choices 14 , 15 . Longer-term immersive research experiences, such as after-school programs or summer camps, have shown successful at recruiting students into STEM degree programs and careers, where longevity of experience helps foster self-determination and interest-led, inquiry-based projects 4 , 16 , 17 , 18 , 19 .

Such activities convey the elements that are suggested to make a post-secondary led high school education programs successful: hands-on experience, self-motivated learning, real-life application, immediate feedback, and problem-based projects 20 , 21 . In combination with immersion in university teaching facilities, learning is authentic and relevant, similar to the STEM school-focused framework, and consequently representative of an experience found in actual STEM practice 22 . These outcomes may further be a consequence of student engagement and attitude: Brown et al. studied the relationships between STEM curriculum and student attitudes, and found the latter played a more important role in intention to persist in STEM when compared to self-efficacy 23 . This is interesting given that student self-efficacy has been identified to influence ‘motivation, persistence, and determination’ in overcoming challenges in a career pathway 24 . Taken together, this suggests that creation and delivery of modern, exciting curriculum that supports positive student attitudes is fundamental to engage and retain students in STEM programs.

Supported by the outcomes of identified effective learning strategies, University of Toronto (U of T) graduate trainees created a novel high school education program Discovery , to develop a comfortable yet stimulating environment of inquiry-focused iterative learning for senior high school students (Grades 11 & 12; Levels 3 & 4) at non-specialized schools. Built in strong collaboration with science teachers from George Harvey Collegiate Institute (Toronto District School Board), Discovery stimulates application of STEM concepts within a unique term-long applied curriculum delivered iteratively within both U of T undergraduate teaching facilities and collaborating high school classrooms 25 . Based on the volume of medically-themed news and entertainment that is communicated to the population at large, the rapidly-growing and diverse field of biomedical engineering (BME) were considered an ideal program context 26 . In its definition, BME necessitates cross-disciplinary STEM knowledge focused on the betterment of human health, wherein Discovery facilitates broadening student perspective through engaging inquiry-based projects. Importantly, Discovery allows all students within a class cohort to work together with their classroom teacher, stimulating continued development of a relevant learning community that is deemed essential for meaningful context and important for transforming student perspectives and understandings 27 , 28 . Multiple studies support the concept that relevant learning communities improve student attitudes towards learning, significantly increasing student motivation in STEM courses, and consequently improving the overall learning experience 29 . Learning communities, such as that provided by Discovery , also promote the formation of self-supporting groups, greater active involvement in class, and higher persistence rates for participating students 30 .

The objective of Discovery , through structure and dissemination, is to engage senior high school science students in challenging, inquiry-based practical BME activities as a mechanism to stimulate comprehension of STEM curriculum application to real-world concepts. Consequent focus is placed on critical thinking skill development through an atmosphere of perseverance in ambiguity, something not common in a secondary school knowledge-focused delivery but highly relevant in post-secondary STEM education strategies. Herein, we describe the observed impact of the differential project-based learning environment of Discovery on student performance and engagement. We identify the value of an inquiry-focused learning model that is tangible for students who struggle in a knowledge-focused delivery structure, where engagement in conceptual critical thinking in the relevant subject area stimulates student interest, attitudes, and resulting academic performance. Assessment of study outcomes suggests that when provided with a differential learning opportunity, student performance and interest in STEM increased. Consequently, Discovery provides an effective teaching and learning framework within a non-specialized school that motivates students, provides opportunity for critical thinking and problem-solving practice, and better prepares them for persistence in future STEM programs.

Program delivery

The outcomes of the current study result from execution of Discovery over five independent academic terms as a collaboration between Institute of Biomedical Engineering (graduate students, faculty, and support staff) and George Harvey Collegiate Institute (science teachers and administration) stakeholders. Each term, the program allowed senior secondary STEM students (Grades 11 and 12) opportunity to engage in a novel project-based learning environment. The program structure uses the problem-based engineering capstone framework as a tool of inquiry-focused learning objectives, motivated by a central BME global research topic, with research questions that are inter-related but specific to the curriculum of each STEM course subject (Fig. 1 ). Over each 12-week term, students worked in teams (3–4 students) within their class cohorts to execute projects with the guidance of U of T trainees ( Discovery instructors) and their own high school teacher(s). Student experimental work was conducted in U of T teaching facilities relevant to the research study of interest (i.e., Biology and Chemistry-based projects executed within Undergraduate Teaching Laboratories; Physics projects executed within Undergraduate Design Studios). Students were introduced to relevant techniques and safety procedures in advance of iterative experimentation. Importantly, this experience served as a course term project for students, who were assessed at several points throughout the program for performance in an inquiry-focused environment as well as within the regular classroom (Fig. 1 ). To instill the atmosphere of STEM, student teams delivered their outcomes in research poster format at a final symposium, sharing their results and recommendations with other post-secondary students, faculty, and community in an open environment.

The general program concept (blue background; top left ) highlights a global research topic examined through student dissemination of subject-specific research questions, yielding multifaceted student outcomes (orange background; top right ). Each program term (term workflow, yellow background; bottom panel ), students work on program deliverables in class (blue), iterate experimental outcomes within university facilities (orange), and are assessed accordingly at numerous deliverables in an inquiry-focused learning model.

Over the course of five terms there were 268 instances of tracked student participation, representing 170 individual students. Specifically, 94 students participated during only one term of programming, 57 students participated in two terms, 16 students participated in three terms, and 3 students participated in four terms. Multiple instances of participation represent students that enrol in more than one STEM class during their senior years of high school, or who participated in Grade 11 and subsequently Grade 12. Students were surveyed before and after each term to assess program effects on STEM interest and engagement. All grade-based assessments were performed by high school teachers for their respective STEM class cohorts using consistent grading rubrics and assignment structure. Here, we discuss the outcomes of student involvement in this experiential curriculum model.

Student performance and engagement

Student grades were assigned, collected, and anonymized by teachers for each Discovery deliverable (background essay, client meeting, proposal, progress report, poster, and final presentation). Teachers anonymized collective Discovery grades, the component deliverable grades thereof, final course grades, attendance in class and during programming, as well as incomplete classroom assignments, for comparative study purposes. Students performed significantly higher in their cumulative Discovery grade than in their cumulative classroom grade (final course grade less the Discovery contribution; p  < 0.0001). Nevertheless, there was a highly significant correlation ( p  < 0.0001) observed between the grade representing combined Discovery deliverables and the final course grade (Fig. 2a ). Further examination of the full dataset revealed two student cohorts of interest: the “Exceeds Expectations” (EE) subset (defined as those students who achieved ≥1 SD [18.0%] grade differential in Discovery over their final course grade; N  = 99 instances), and the “Multiple Term” (MT) subset (defined as those students who participated in Discovery more than once; 76 individual students that collectively accounted for 174 single terms of assessment out of the 268 total student-terms delivered) (Fig. 2b, c ). These subsets were not unrelated; 46 individual students who had multiple experiences (60.5% of total MTs) exhibited at least one occasion in achieving a ≥18.0% grade differential. As students participated in group work, there was concern that lower-performing students might negatively influence the Discovery grade of higher-performing students (or vice versa). However, students were observed to self-organize into groups where all individuals received similar final overall course grades (Fig. 2d ), thereby alleviating these concerns.

a Linear regression of student grades reveals a significant correlation ( p  = 0.0009) between Discovery performance and final course grade less the Discovery contribution to grade, as assessed by teachers. The dashed red line and intervals represent the theoretical 1:1 correlation between Discovery and course grades and standard deviation of the Discovery -course grade differential, respectively. b , c Identification of subgroups of interest, Exceeds Expectations (EE; N  = 99, orange ) who were ≥+1 SD in Discovery -course grade differential and Multi-Term (MT; N  = 174, teal ), of which N  = 65 students were present in both subgroups. d Students tended to self-assemble in working groups according to their final course performance; data presented as mean ± SEM. e For MT students participating at least 3 terms in Discovery , there was no significant correlation between course grade and time, while ( f ) there was a significant correlation between Discovery grade and cumulative terms in the program. Histograms of total absences per student in ( g ) Discovery and ( h ) class (binned by 4 days to be equivalent in time to a single Discovery absence).

The benefits experienced by MT students seemed progressive; MT students that participated in 3 or 4 terms ( N  = 16 and 3, respectively ) showed no significant increase by linear regression in their course grade over time ( p  = 0.15, Fig. 2e ), but did show a significant increase in their Discovery grades ( p  = 0.0011, Fig. 2f ). Finally, students demonstrated excellent Discovery attendance; at least 91% of participants attended all Discovery sessions in a given term (Fig. 2g ). In contrast, class attendance rates reveal a much wider distribution where 60.8% (163 out of 268 students) missed more than 4 classes (equivalent in learning time to one Discovery session) and 14.6% (39 out of 268 students) missed 16 or more classes (equivalent in learning time to an entire program of Discovery ) in a term (Fig. 2h ).

Discovery EE students (Fig. 3 ), roughly by definition, obtained lower course grades ( p  < 0.0001, Fig. 3a ) and higher final Discovery grades ( p  = 0.0004, Fig. 3b ) than non-EE students. This cohort of students exhibited program grades higher than classmates (Fig. 3c–h ); these differences were significant in every category with the exception of essays, where they outperformed to a significantly lesser degree ( p  = 0.097; Fig. 3c ). There was no statistically significant difference in EE vs. non-EE student classroom attendance ( p  = 0.85; Fig. 3i, j ). There were only four single day absences in Discovery within the EE subset; however, this difference was not statistically significant ( p  = 0.074).

The “Exceeds Expectations” (EE) subset of students (defined as those who received a combined Discovery grade ≥1 SD (18.0%) higher than their final course grade) performed ( a ) lower on their final course grade and ( b ) higher in the Discovery program as a whole when compared to their classmates. d – h EE students received significantly higher grades on each Discovery deliverable than their classmates, except for their ( c ) introductory essays and ( h ) final presentations. The EE subset also tended ( i ) to have a higher relative rate of attendance during Discovery sessions but no difference in ( j ) classroom attendance. N  = 99 EE students and 169 non-EE students (268 total). Grade data expressed as mean ± SEM.

Discovery MT students (Fig. 4 ), although not receiving significantly higher grades in class than students participating in the program only one time ( p  = 0.29, Fig. 4a ), were observed to obtain higher final Discovery grades than single-term students ( p  = 0.0067, Fig. 4b ). Although trends were less pronounced for individual MT student deliverables (Fig. 4c–h ), this student group performed significantly better on the progress report ( p  = 0.0021; Fig. 4f ). Trends of higher performance were observed for initial proposals and final presentations ( p  = 0.081 and 0.056, respectively; Fig. 4e, h ); all other deliverables were not significantly different between MT and non-MT students (Fig. 4c, d, g ). Attendance in Discovery ( p  = 0.22) was also not significantly different between MT and non-MT students, although MT students did miss significantly less class time ( p  = 0.010) (Fig. 4i, j ). Longitudinal assessment of individual deliverables for MT students that participated in three or more Discovery terms (Fig. 5 ) further highlights trend in improvement (Fig. 2f ). Greater performance over terms of participation was observed for essay ( p  = 0.0295, Fig. 5a ), client meeting ( p  = 0.0003, Fig. 5b ), proposal ( p  = 0.0004, Fig. 5c ), progress report ( p  = 0.16, Fig. 5d ), poster ( p  = 0.0005, Fig. 5e ), and presentation ( p  = 0.0295, Fig. 5f ) deliverable grades; these trends were all significant with the exception of the progress report ( p  = 0.16, Fig. 5d ) owing to strong performance in this deliverable in all terms.

The “multi-term” (MT) subset of students (defined as having attended more than one term of Discovery ) demonstrated favorable performance in Discovery , ( a ) showing no difference in course grade compared to single-term students, but ( b outperforming them in final Discovery grade. Independent of the number of times participating in Discovery , MT students did not score significantly differently on their ( c ) essay, ( d ) client meeting, or ( g ) poster. They tended to outperform their single-term classmates on the ( e ) proposal and ( h ) final presentation and scored significantly higher on their ( f ) progress report. MT students showed no statistical difference in ( i ) Discovery attendance but did show ( j ) higher rates of classroom attendance than single-term students. N  = 174 MT instances of student participation (76 individual students) and 94 single-term students. Grade data expressed as mean ± SEM.

Longitudinal assessment of a subset of MT student participants that participated in three ( N  = 16) or four ( N  = 3) terms presents a significant trend of improvement in their ( a ) essay, ( b ) client meeting, ( c ) proposal, ( e ) poster, and ( f ) presentation grade. d Progress report grades present a trend in improvement but demonstrate strong performance in all terms, limiting potential for student improvement. Grade data are presented as individual student performance; each student is represented by one color; data is fitted with a linear trendline (black).

Finally, the expansion of Discovery to a second school of lower LOI (i.e., nominally higher aggregate SES) allowed for the assessment of program impact in a new population over 2 terms of programming. A significant ( p  = 0.040) divergence in Discovery vs. course grade distribution from the theoretical 1:1 relationship was found in the new cohort (S 1 Appendix , Fig. S 1 ), in keeping with the pattern established in this study.

Teacher perceptions

Qualitative observation in the classroom by high school teachers emphasized the value students independently placed on program participation and deliverables. Throughout the term, students often prioritized Discovery group assignments over other tasks for their STEM courses, regardless of academic weight and/or due date. Comparing within this student population, teachers spoke of difficulties with late and incomplete assignments in the regular curriculum but found very few such instances with respect to Discovery -associated deliverables. Further, teachers speculated on the good behavior and focus of students in Discovery programming in contrast to attentiveness and behavior issues in their school classrooms. Multiple anecdotal examples were shared of renewed perception of student potential; students that exhibited poor academic performance in the classroom often engaged with high performance in this inquiry-focused atmosphere. Students appeared to take a sense of ownership, excitement, and pride in the setting of group projects oriented around scientific inquiry, discovery, and dissemination.

Student perceptions

Students were asked to consider and rank the academic difficulty (scale of 1–5, with 1 = not challenging and 5 = highly challenging) of the work they conducted within the Discovery learning model. Considering individual Discovery terms, at least 91% of students felt the curriculum to be sufficiently challenging with a 3/5 or higher ranking (Term 1: 87.5%, Term 2: 93.4%, Term 3: 85%, Term 4: 93.3%, Term 5: 100%), and a minimum of 58% of students indicating a 4/5 or higher ranking (Term 1: 58.3%, Term 2: 70.5%, Term 3: 67.5%, Term 4: 69.1%, Term 5: 86.4%) (Fig. 6a ).

a Histogram of relative frequency of perceived Discovery programming academic difficulty ranked from not challenging (1) to highly challenging (5) for each session demonstrated the consistently perceived high degree of difficulty for Discovery programming (total responses: 223). b Program participation increased student comfort (94.6%) with navigating lab work in a university or college setting (total responses: 220). c Considering participation in Discovery programming, students indicated their increased (72.4%) or decreased (10.1%) likelihood to pursue future experiences in STEM as a measure of program impact (total responses: 217). d Large majority of participating students (84.9%) indicated their interest for future participation in Discovery (total responses: 212). Students were given the opportunity to opt out of individual survey questions, partially completed surveys were included in totals.

The majority of students (94.6%) indicated they felt more comfortable with the idea of performing future work in a university STEM laboratory environment given exposure to university teaching facilities throughout the program (Fig. 6b ). Students were also queried whether they were (i) more likely, (ii) less likely, or (iii) not impacted by their experience in the pursuit of STEM in the future. The majority of participants (>82%) perceived impact on STEM interests, with 72.4% indicating they were more likely to pursue these interests in the future (Fig. 6c ). When surveyed at the end of term, 84.9% of students indicated they would participate in the program again (Fig. 6d ).

We have described an inquiry-based framework for implementing experiential STEM education in a BME setting. Using this model, we engaged 268 instances of student participation (170 individual students who participated 1–4 times) over five terms in project-based learning wherein students worked in peer-based teams under the mentorship of U of T trainees to design and execute the scientific method in answering a relevant research question. Collaboration between high school teachers and Discovery instructors allowed for high school student exposure to cutting-edge BME research topics, participation in facilitated inquiry, and acquisition of knowledge through scientific discovery. All assessments were conducted by high school teachers and constituted a fraction (10–15%) of the overall course grade, instilling academic value for participating students. As such, students exhibited excitement to learn as well as commitment to their studies in the program.

Through our observations and analysis, we suggest there is value in differential learning environments for students that struggle in a knowledge acquisition-focused classroom setting. In general, we observed a high level of academic performance in Discovery programming (Fig. 2a ), which was highlighted exceptionally in EE students who exhibited greater academic performance in Discovery deliverables compared to normal coursework (>18% grade improvement in relevant deliverables). We initially considered whether this was the result of strong students influencing weaker students; however, group organization within each course suggests this is not the case (Fig. 2d ). With the exception of one class in one term (24 participants assigned by their teacher), students were allowed to self-organize into working groups and they chose to work with other students of relatively similar academic performance (as indicated by course grade), a trend observed in other studies 31 , 32 . Remarkably, EE students not only excelled during Discovery when compared to their own performance in class, but this cohort also achieved significantly higher average grades in each of the deliverables throughout the program when compared to the remaining Discovery cohort (Fig. 3 ). This data demonstrates the value of an inquiry-based learning environment compared to knowledge-focused delivery in the classroom in allowing students to excel. We expect that part of this engagement was resultant of student excitement with a novel learning opportunity. It is however a well-supported concept that students who struggle in traditional settings tend to demonstrate improved interest and motivation in STEM when given opportunity to interact in a hands-on fashion, which supports our outcomes 4 , 33 . Furthermore, these outcomes clearly represent variable student learning styles, where some students benefit from a greater exchange of information, knowledge and skills in a cooperative learning environment 34 . The performance of the EE group may not be by itself surprising, as the identification of the subset by definition required high performers in Discovery who did not have exceptionally high course grades; in addition, the final Discovery grade is dependent on the component assignment grades. However, the discrepancies between EE and non-EE groups attendance suggests that students were engaged by Discovery in a way that they were not by regular classroom curriculum.

In addition to quantified engagement in Discovery observed in academic performance, we believe remarkable attendance rates are indicative of the value students place in the differential learning structure. Given the differences in number of Discovery days and implications of missing one day of regular class compared to this immersive program, we acknowledge it is challenging to directly compare attendance data and therefore approximate this comparison with consideration of learning time equivalence. When combined with other subjective data including student focus, requests to work on Discovery during class time, and lack of discipline/behavior issues, the attendance data importantly suggests that students were especially engaged by the Discovery model. Further, we believe the increased commute time to the university campus (students are responsible for independent transit to campus, a much longer endeavour than the normal school commute), early program start time, and students’ lack of familiarity with the location are non-trivial considerations when determining the propensity of students to participate enthusiastically in Discovery . We feel this suggests the students place value on this team-focused learning and find it to be more applicable and meaningful to their interests.

Given post-secondary admission requirements for STEM programs, it would be prudent to think that students participating in multiple STEM classes across terms are the ones with the most inherent interest in post-secondary STEM programs. The MT subset, representing students who participated in Discovery for more than one term, averaged significantly higher final Discovery grades. The increase in the final Discovery grade was observed to result from a general confluence of improved performance over multiple deliverables and a continuous effort to improve in a STEM curriculum. This was reflected in longitudinal tracking of Discovery performance, where we observed a significant trend of improved performance. Interestingly, the high number of MT students who were included in the EE group suggests that students who had a keen interest in science enrolled in more than one course and in general responded well to the inquiry-based teaching method of Discovery , where scientific method was put into action. It stands to reason that students interested in science will continue to take STEM courses and will respond favorably to opportunities to put classroom theory to practical application.

The true value of an inquiry-based program such as Discovery may not be based in inspiring students to perform at a higher standard in STEM within the high school setting, as skills in critical thinking do not necessarily translate to knowledge-based assessment. Notably, students found the programming equally challenging throughout each of the sequential sessions, perhaps somewhat surprising considering the increasing number of repeat attendees in successive sessions (Fig. 6a ). Regardless of sub-discipline, there was an emphasis of perceived value demonstrated through student surveys where we observed indicated interest in STEM and comfort with laboratory work environments, and desire to engage in future iterations given the opportunity. Although non-quantitative, we perceive this as an indicator of significant student engagement, even though some participants did not yield academic success in the program and found it highly challenging given its ambiguity.

Although we observed that students become more certain of their direction in STEM, further longitudinal study is warranted to make claim of this outcome. Additionally, at this point in our assessment we cannot effectively assess the practical outcomes of participation, understanding that the immediate effects observed are subject to a number of factors associated with performance in the high school learning environment. Future studies that track graduates from this program will be prudent, in conjunction with an ever-growing dataset of assessment as well as surveys designed to better elucidate underlying perceptions and attitudes, to continue to understand the expected benefits of this inquiry-focused and partnered approach. Altogether, a multifaceted assessment of our early outcomes suggests significant value of an immersive and iterative interaction with STEM as part of the high school experience. A well-defined divergence from knowledge-based learning, focused on engagement in critical thinking development framed in the cutting-edge of STEM, may be an important step to broadening student perspectives.

In this study, we describe the short-term effects of an inquiry-based STEM educational experience on a cohort of secondary students attending a non-specialized school, and suggest that the framework can be widely applied across virtually all subjects where inquiry-driven and mentored projects can be undertaken. Although we have demonstrated replication in a second cohort of nominally higher SES (S 1 Appendix , Supplementary Fig. 1 ), a larger collection period with more students will be necessary to conclusively determine impact independent of both SES and specific cohort effects. Teachers may also find this framework difficult to implement depending on resources and/or institutional investment and support, particularly if post-secondary collaboration is inaccessible. Offerings to a specific subject (e.g., physics) where experiments yielding empirical data are logistically or financially simpler to perform may be valid routes of adoption as opposed to the current study where all subject cohorts were included.

As we consider Discovery in a bigger picture context, expansion and implementation of this model is translatable. Execution of the scientific method is an important aspect of citizen science, as the concepts of critical thing become ever-more important in a landscape of changing technological landscapes. Giving students critical thinking and problem-solving skills in their primary and secondary education provides value in the context of any career path. Further, we feel that this model is scalable across disciplines, STEM or otherwise, as a means of building the tools of inquiry. We have observed here the value of differential inclusive student engagement and critical thinking through an inquiry-focused model for a subset of students, but further to this an engagement, interest, and excitement across the body of student participants. As we educate the leaders of tomorrow, we suggest that use of an inquiry-focused model such as Discovery could facilitate growth of a data-driven critical thinking framework.

In conclusion, we have presented a model of inquiry-based STEM education for secondary students that emphasizes inclusion, quantitative analysis, and critical thinking. Student grades suggest significant performance benefits, and engagement data suggests positive student attitude despite the perceived challenges of the program. We also note a particular performance benefit to students who repeatedly engage in the program. This framework may carry benefits in a wide variety of settings and disciplines for enhancing student engagement and performance, particularly in non-specialized school environments.

Study design and implementation

Participants in Discovery include all students enrolled in university-stream Grade 11 or 12 biology, chemistry, or physics at the participating school over five consecutive terms (cohort summary shown in Table 1 ). Although student participation in educational content was mandatory, student grades and survey responses (administered by high school teachers) were collected from only those students with parent or guardian consent. Teachers replaced each student name with a unique coded identifier to preserve anonymity but enable individual student tracking over multiple terms. All data collected were analyzed without any exclusions save for missing survey responses; no power analysis was performed prior to data collection.

Ethics statement

This study was approved by the University of Toronto Health Sciences Research Ethics Board (Protocol # 34825) and the Toronto District School Board External Research Review Committee (Protocol # 2017-2018-20). Written informed consent was collected from parents or guardians of participating students prior to the acquisition of student data (both post-hoc academic data and survey administration). Data were anonymized by high school teachers for maintenance of academic confidentiality of individual students prior to release to U of T researchers.

Educational program overview

Students enrolled in university-preparatory STEM classes at the participating school completed a term-long project under the guidance of graduate student instructors and undergraduate student mentors as a mandatory component of their respective course. Project curriculum developed collaboratively between graduate students and participating high school teachers was delivered within U of T Faculty of Applied Science & Engineering (FASE) teaching facilities. Participation allows high school students to garner a better understanding as to how undergraduate learning and career workflows in STEM vary from traditional high school classroom learning, meanwhile reinforcing the benefits of problem solving, perseverance, teamwork, and creative thinking competencies. Given that Discovery was a mandatory component of course curriculum, students participated as class cohorts and addressed questions specific to their course subject knowledge base but related to the defined global health research topic (Fig. 1 ). Assessment of program deliverables was collectively assigned to represent 10–15% of the final course grade for each subject at the discretion of the respective STEM teacher.

The Discovery program framework was developed, prior to initiation of student assessment, in collaboration with one high school selected from the local public school board over a 1.5 year period of time. This partner school consistently scores highly (top decile) in the school board’s Learning Opportunities Index (LOI). The LOI ranks each school based on measures of external challenges affecting its student population therefore schools with the greatest level of external challenge receive a higher ranking 35 . A high LOI ranking is inversely correlated with socioeconomic status (SES); therefore, participating students are identified as having a significant number of external challenges that may affect their academic success. The mandatory nature of program participation was established to reach highly capable students who may be reluctant to engage on their own initiative, as a means of enhancing the inclusivity and impact of the program. The selected school partner is located within a reasonable geographical radius of our campus (i.e., ~40 min transit time from school to campus). This is relevant as participating students are required to independently commute to campus for Discovery hands-on experiences.

Each program term of Discovery corresponds with a five-month high school term. Lead university trainee instructors (3–6 each term) engaged with high school teachers 1–2 months in advance of high school student engagement to discern a relevant overarching global healthcare theme. Each theme was selected with consideration of (a) topics that university faculty identify as cutting-edge biomedical research, (b) expertise that Discovery instructors provide, and (c) capacity to showcase the diversity of BME. Each theme was sub-divided into STEM subject-specific research questions aligning with provincial Ministry of Education curriculum concepts for university-preparatory Biology, Chemistry, and Physics 9 that students worked to address, both on-campus and in-class, during a term-long project. The Discovery framework therefore provides students a problem-based learning experience reflective of an engineering capstone design project, including a motivating scientific problem (i.e., global topic), subject-specific research question, and systematic determination of a professional recommendation addressing the needs of the presented problem.

Discovery instructors were volunteers recruited primarily from graduate and undergraduate BME programs in the FASE. Instructors were organized into subject-specific instructional teams based on laboratory skills, teaching experience, and research expertise. The lead instructors of each subject (the identified 1–2 trainees that built curriculum with high school teachers) were responsible to organize the remaining team members as mentors for specific student groups over the course of the program term (~1:8 mentor to student ratio).

All Discovery instructors were familiarized with program expectations and trained in relevant workspace safety, in addition to engagement at a teaching workshop delivered by the Faculty Advisor (a Teaching Stream faculty member) at the onset of term. This workshop was designed to provide practical information on teaching and was co-developed with high school teachers based on their extensive training and experience in fundamental teaching methods. In addition, group mentors received hands-on training and guidance from lead instructors regarding the specific activities outlined for their respective subject programming (an exemplary term of student programming is available in S 2 Appendix) .

Discovery instructors were responsible for introducing relevant STEM skills and mentoring high school students for the duration of their projects, with support and mentorship from the Faculty Mentor. Each instructor worked exclusively throughout the term with the student groups to which they had been assigned, ensuring consistent mentorship across all disciplinary components of the project. In addition to further supporting university trainees in on-campus mentorship, high school teachers were responsible for academic assessment of all student program deliverables (Fig. 1 ; the standardized grade distribution available in S 3 Appendix ). Importantly, trainees never engaged in deliverable assessment; for continuity of overall course assessment, this remained the responsibility of the relevant teacher for each student cohort.

Throughout each term, students engaged within the university facilities four times. The first three sessions included hands-on lab sessions while the fourth visit included a culminating symposium for students to present their scientific findings (Fig. 1 ). On average, there were 4–5 groups of students per subject (3–4 students per group; ~20 students/class). Discovery instructors worked exclusively with 1–2 groups each term in the capacity of mentor to monitor and guide student progress in all project deliverables.

After introducing the selected global research topic in class, teachers led students in completion of background research essays. Students subsequently engaged in a subject-relevant skill-building protocol during their first visit to university teaching laboratory facilities, allowing opportunity to understand analysis techniques and equipment relevant for their assessment projects. At completion of this session, student groups were presented with a subject-specific research question as well as the relevant laboratory inventory available for use during their projects. Armed with this information, student groups continued to work in their classroom setting to develop group-specific experimental plans. Teachers and Discovery instructors provided written and oral feedback, respectively , allowing students an opportunity to revise their plans in class prior to on-campus experimental execution.

Once at the relevant laboratory environment, student groups executed their protocols in an effort to collect experimental data. Data analysis was performed in the classroom and students learned by trial & error to optimize their protocols before returning to the university lab for a second opportunity of data collection. All methods and data were re-analyzed in class in order for students to create a scientific poster for the purpose of study/experience dissemination. During a final visit to campus, all groups presented their findings at a research symposium, allowing students to verbally defend their process, analyses, interpretations, and design recommendations to a diverse audience including peers, STEM teachers, undergraduate and graduate university students, postdoctoral fellows and U of T faculty.

Data collection

Teachers evaluated their students on the following associated deliverables: (i) global theme background research essay; (ii) experimental plan; (iii) progress report; (iv) final poster content and presentation; and (v) attendance. For research purposes, these grades were examined individually and also as a collective Discovery program grade for each student. For students consenting to participation in the research study, all Discovery grades were anonymized by the classroom teacher before being shared with study authors. Each student was assigned a code by the teacher for direct comparison of deliverable outcomes and survey responses. All instances of “Final course grade” represent the prorated course grade without the Discovery component, to prevent confounding of quantitative analyses.

Survey instruments were used to gain insight into student attitudes and perceptions of STEM and post-secondary study, as well as Discovery program experience and impact (S 4 Appendix ). High school teachers administered surveys in the classroom only to students supported by parental permission. Pre-program surveys were completed at minimum 1 week prior to program initiation each term and exit surveys were completed at maximum 2 weeks post- Discovery term completion. Surveys results were validated using a principal component analysis (S 1 Appendix , Supplementary Fig. 2 ).

Identification and comparison of population subsets

From initial analysis, we identified two student subpopulations of particular interest: students who performed ≥1 SD [18.0%] or greater in the collective Discovery components of the course compared to their final course grade (“EE”), and students who participated in Discovery more than once (“MT”). These groups were compared individually against the rest of the respective Discovery population (“non-EE” and “non-MT”, respectively ). Additionally, MT students who participated in three or four (the maximum observed) terms of Discovery were assessed for longitudinal changes to performance in their course and Discovery grades. Comparisons were made for all Discovery deliverables (introductory essay, client meeting, proposal, progress report, poster, and presentation), final Discovery grade, final course grade, Discovery attendance, and overall attendance.

Statistical analysis

Student course grades were analyzed in all instances without the Discovery contribution (calculated from all deliverable component grades and ranging from 10 to 15% of final course grade depending on class and year) to prevent correlation. Aggregate course grades and Discovery grades were first compared by paired t-test, matching each student’s course grade to their Discovery grade for the term. Student performance in Discovery ( N  = 268 instances of student participation, comprising 170 individual students that participated 1–4 times) was initially assessed in a linear regression of Discovery grade vs. final course grade. Trends in course and Discovery performance over time for students participating 3 or 4 terms ( N  = 16 and 3 individuals, respectively ) were also assessed by linear regression. For subpopulation analysis (EE and MT, N  = 99 instances from 81 individuals and 174 instances from 76 individuals, respectively ), each dataset was tested for normality using the D’Agostino and Pearson omnibus normality test. All subgroup comparisons vs. the remaining population were performed by Mann–Whitney U -test. Data are plotted as individual points with mean ± SEM overlaid (grades), or in histogram bins of 1 and 4 days, respectively , for Discovery and class attendance. Significance was set at α ≤ 0.05.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author DMK. These data are not publicly available due to privacy concerns of personal data according to the ethical research agreements supporting this study.

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Acknowledgements

This study has been possible due to the support of many University of Toronto trainee volunteers, including Genevieve Conant, Sherif Ramadan, Daniel Smieja, Rami Saab, Andrew Effat, Serena Mandla, Cindy Bui, Janice Wong, Dawn Bannerman, Allison Clement, Shouka Parvin Nejad, Nicolas Ivanov, Jose Cardenas, Huntley Chang, Romario Regeenes, Dr. Henrik Persson, Ali Mojdeh, Nhien Tran-Nguyen, Ileana Co, and Jonathan Rubianto. We further acknowledge the staff and administration of George Harvey Collegiate Institute and the Institute of Biomedical Engineering (IBME), as well as Benjamin Rocheleau and Madeleine Rocheleau for contributions to data collation. Discovery has grown with continued support of Dean Christopher Yip (Faculty of Applied Science and Engineering, U of T), and the financial support of the IBME and the National Science and Engineering Research Council (NSERC) PromoScience program (PROSC 515876-2017; IBME “Igniting Youth Curiosity in STEM” initiative co-directed by DMK and Dr. Penney Gilbert). LDH and NIC were supported by Vanier Canada graduate scholarships from the Canadian Institutes of Health Research and NSERC, respectively . DMK holds a Dean’s Emerging Innovation in Teaching Professorship in the Faculty of Engineering & Applied Science, U of T.

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These authors contributed equally: Locke Davenport Huyer, Neal I. Callaghan.

Authors and Affiliations

Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer, Neal I. Callaghan, Andrey I. Shukalyuk & Dawn M. Kilkenny

Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer

Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, ON, Canada

Neal I. Callaghan

George Harvey Collegiate Institute, Toronto District School Board, Toronto, ON, Canada

Sara Dicks, Edward Scherer & Margaret Jou

Institute for Studies in Transdisciplinary Engineering Education & Practice, University of Toronto, Toronto, ON, Canada

Dawn M. Kilkenny

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Contributions

LDH, NIC and DMK conceived the program structure, designed the study, and interpreted the data. LDH and NIC ideated programming, coordinated execution, and performed all data analysis. SD, ES, and MJ designed and assessed student deliverables, collected data, and anonymized data for assessment. SD assisted in data interpretation. AIS assisted in programming ideation and design. All authors provided feedback and approved the manuscript that was written by LDH, NIC and DMK.

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Correspondence to Dawn M. Kilkenny .

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Davenport Huyer, L., Callaghan, N.I., Dicks, S. et al. Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program. npj Sci. Learn. 5 , 17 (2020). https://doi.org/10.1038/s41539-020-00076-2

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A qualitative study exploring high school students' understanding of, and attitudes towards, health information and claims

Affiliation.

• 1 Centre for Research in Evidence-Based Practice (CREBP), Bond University, Robina, QLD, Australia.
• PMID: 28475266
• PMCID: PMC5600218
• DOI: 10.1111/hex.12562

Background: Exposure to health claims, particularly in the media and social media, is pervasive, and the information conveyed is often inaccurate, incomplete or misleading. Some young people of high school ages are already making decisions about using readily available health interventions (such as sports drinks and beauty products).Although previous research has assessed adults' understanding of health claims, no research has examined this issue in young adults who are attending high school.

Objective: To explore high school students' understanding of, and attitudes towards, concepts relevant to assessing health information and claims.

Design: A qualitative study involving semi-structured interviews with 27 Australian high school students. Responses were recorded, transcribed and a thematic analysis performed. Three themes emerged as follows: (i) Variability in sources of health information and claims, and general understanding of their creation and accuracy of content, (ii) The use of substitute indicators to assess health information and claims and make judgements about their trustworthiness, (iii) Uncertainty about, and literal interpretation of, the language of health claims. Despite general scepticism of health claims and admitted uncertainty of research terminology, many students were generally convinced. Students had poor understanding about how health claims are generated and tended to rely on substitute indicators, such as endorsements, when evaluating the believability of claims.

Conclusion: School students' lack of awareness of basic health research processes and methods of assessing the accuracy of health information and claims makes them vulnerable to distorted and misleading health information. This restricts their ability to make informed health decisions - a skill that increases in importance as they become adults.

Keywords: appraisal; health claims; health literacy; students.

© 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.

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• Effects of School-Based Educational Interventions for Enhancing Adolescents Abilities in Critical Appraisal of Health Claims: A Systematic Review. Nordheim LV, Gundersen MW, Espehaug B, Guttersrud Ø, Flottorp S. Nordheim LV, et al. PLoS One. 2016 Aug 24;11(8):e0161485. doi: 10.1371/journal.pone.0161485. eCollection 2016. PLoS One. 2016. PMID: 27557129 Free PMC article. Review.
• Educational interventions to improve people's understanding of key concepts in assessing the effects of health interventions: a systematic review. Cusack L, Del Mar CB, Chalmers I, Gibson E, Hoffmann TC. Cusack L, et al. Syst Rev. 2018 May 2;7(1):68. doi: 10.1186/s13643-018-0719-4. Syst Rev. 2018. PMID: 29716639 Free PMC article.
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• Teaching Australian high school students to think critically about health claims: a cluster randomized trial. Cusack L, Jones M, Desha L, Hoffmann TC. Cusack L, et al. Health Educ Res. 2023 Sep 20;38(5):412-425. doi: 10.1093/her/cyad029. Health Educ Res. 2023. PMID: 37428473 Free PMC article. Clinical Trial.
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189+ Innovative Qualitative Research Topics for STEM Students

Explore engaging qualitative research topics for STEM students. Discover insights into user experiences, tech impacts, and learning processes with our inspiring ideas.

Qualitative research offers a fresh perspective on STEM, highlighting the human experiences behind the data. This blog post will show how qualitative research can transform your understanding of STEM and provide exciting topic ideas to kickstart your research.

From user experiences to the social effects of technology and learning methods, qualitative research reveals new insights and opportunities. Let’s dive in and see what this approach can uncover!

Table of Contents

Qualitative Research Topics for STEM Students PDF

What is qualitative research.

Qualitative research explores human experiences and perspectives through non-numerical data like words and images. Unlike quantitative research, which uses numbers, qualitative research seeks to understand the reasons behind behaviors.

Key Characteristics

• Exploratory: Uncovers new insights.
• In-depth: Provides detailed data.
• Subjective: Focuses on personal experiences.
• Contextual: Considers social and cultural factors.

Common methods include interviews, focus groups, observations, and document analysis.

Importance of Qualitative Research in STEM

Qualitative research is key to understanding the human side of STEM. Here’s why it matters:

• Human Interaction: Shows how people interact with STEM.
• Context: Adds meaning to quantitative data.
• Innovation: Finds new research ideas.
• Ethics: Highlights ethical issues.
• Policy: Guides decisions with people’s needs and experiences.

It helps STEM fields get a fuller picture of complex issues.

How to choose a qualitative research topic for STEM?

Choosing a qualitative research topic in STEM involves:

Identify Interests

• Passion: Pick something you care about.
• Expertise: Choose a field you know.
• Career Goals: Align with your future plans.

Explore Research

• Literature: Find gaps in current studies.
• Trends: Look for new, innovative areas.
• Questions: Develop research questions from your findings.

Check Feasibility

• Data: Ensure you can access what you need.
• Time: Make sure you can complete it in time.
• Resources: Consider what support and resources you’ll need.

Refine Topic

• Focus: Narrow down to a specific area.
• Relevance: Ensure it adds value to the field.
• Originality: Aim for a unique angle.

Consult Mentors

• Feedback: Get advice from professors or experts.

These steps will help you choose a practical and engaging research topic.

Qualitative Research Topics for STEM Students

Check out qualitative research topics for STEM students:-

Engineering

• Sustainable engineering’s community impact.
• User experiences with smart home tech.
• Team dynamics in engineering projects.
• Innovation in civil engineering.
• Safety perceptions of new construction materials.
• Green tech adoption in mechanical engineering.
• Ethics in autonomous vehicle design.
• Role of mentorship in engineering careers.
• Industry 4.0 effects on engineering education.
• Challenges in interdisciplinary engineering projects.

Computer Science

• AI user experience in healthcare.
• Diversity in computer science teams.
• Privacy concerns with digital surveillance.
• Adoption challenges of blockchain tech.
• Coding boot camp impacts on skills.
• Programming languages and productivity.
• Ethics of algorithmic decision-making.
• Gamification in learning programming.
• Women’s experiences in cybersecurity.
• Human-computer interaction in VR.
• Public views on GMOs in agriculture.
• Citizen science in biodiversity.
• Environmental changes affecting wildlife .
• Field research experiences in biology.
• Ethics in genetic research.
• Community health initiatives’ effects.
• Conservation efforts among indigenous groups.
• Interdisciplinary approaches to health issues.
• Communicating scientific discoveries to the public.
• Urban biodiversity challenges.
• Green chemistry and sustainability.
• Public views on new chemical products.
• Eco-friendly lab practices challenges.
• Chemistry education and innovation.
• Public understanding of chemical safety.
• Interdisciplinary research in chemistry.
• Impact of patent laws on chemistry.
• Ethics in chemical experimentation.
• Collaboration in chemical research.
• Media portrayal of chemistry.
• Quantum computing’s future impact.
• Public understanding of physics concepts.
• Interdisciplinary research in theoretical physics.
• Particle physics lab experiences.
• Ethics in nuclear physics research.
• Theoretical physics and tech development.
• Space exploration in popular culture.
• Outreach in physics education.
• Physics research influencing policy.
• Educational background’s effect on physics perspectives.

Mathematics

• Mathematical modeling in climate research.
• Mathematicians’ experiences in applied settings.
• Technology’s impact on problem-solving.
• Public perceptions of math education.
• Theories’ real-world applications.
• Teaching abstract math concepts.
• Collaboration in math advancements.
• Data analysis ethical issues.
• Math research and economic forecasting.
• Evolution of math education with tech.

Environmental Science

• Community responses to conservation policies.
• Environmental science in disaster prep.
• Public views on climate change policies.
• Field research experiences in environmental science.
• Ethics in resource management.
• Local cultures and conservation efforts.
• Interdisciplinary solutions for environmental issues.
• Communicating environmental science research.
• Urbanization’s impact on ecosystems.
• Grassroots environmental activism experiences.
• Space exploration’s tech impact.
• Public views on extraterrestrial life.
• Role of amateur astronomers in discovery.
• Space telescope research experiences.
• Space science’s effect on education.
• Ethical issues in planetary exploration.
• Astronomy’s cultural impact.
• Challenges in explaining astronomy to the public.
• International collaboration in space research.
• Space research benefits to humanity.

Materials Science

• Nanotechnology in materials science.
• Public views on new materials.
• Interdisciplinary approaches to material innovation.
• Developing sustainable materials challenges.
• Ethical issues in advanced materials use.
• Materials science in healthcare.
• Researcher experiences in high-performance labs.
• Industry partnerships in materials science.
• Testing and validating new materials.
• Materials science and environmental sustainability.
• Geological research in disaster preparedness.
• Public views on earthquake prediction.
• Field research experiences in geology.
• Geology’s role in climate understanding.
• Ethics in resource extraction.
• Geology’s influence on infrastructure.
• Community involvement in geological surveys.
• Communicating geological risks to the public.
• Geological education’s community impact.
• Research influence on environmental policies.
• Statistics in public health research.
• Data privacy concerns in research.
• Applying statistics to social science.
• Impact of statistical software on analysis.
• Ethics in data use.
• Statistical literacy and decision-making.
• Statisticians in interdisciplinary research.
• Statistical modeling in predictive analytics.
• Public understanding of statistics in media.
• Statistical methods’ role in discovery.
• Robotics impact on manufacturing.
• Public views on robots in daily life.
• Ethics in autonomous robots.
• Challenges in healthcare robots.
• Robotics’ effect on employment.
• Collaborative robots research experiences.
• Robotics in education.
• Communicating robotics research.
• Interdisciplinary teams in robotics tech.
• Robotics impact on elderly quality of life.

Biomedical Engineering

• Impact of biomedical tech on patient care.
• Public views on medical device safety.
• Integrating biomedical tech with clinical practice.
• Collaboration in biomedical research.
• Ethics in medical tech development.
• Engineers’ experiences with healthcare professionals.
• Regulatory effects on biomedical innovation.
• Medical device portrayal in media.
• Patient feedback in device design.
• Tech advances in medical diagnostics.

Industrial Engineering

• Lean manufacturing’s modern impact.
• Automation’s workplace perceptions.
• Optimizing supply chains challenges.
• Industrial engineering and sustainability.
• Data analytics in manufacturing.
• Industrial engineering project experiences.
• Ergonomics and workplace safety.
• Industry 4.0 effects on practices.
• Industrial engineering in services.
• Ethics in process optimization.

Agricultural Science

• Tech’s role in crop yields.
• Public views on GM crops.
• Sustainable agricultural practices challenges.
• Climate change’s effect on agriculture.
• Farmers’ experiences with precision agriculture.
• Agricultural research and food security.
• Ethics in pesticide use.
• Community involvement in agricultural research.
• Communicating agricultural science to consumers.
• Policy changes and agricultural innovation.

These condensed topics should provide a clear and manageable starting point for qualitative research.

Conducting Qualitative Research in STEM

Qualitative Research in STEM: Key Steps

Research Design

• Define Question : State the problem and goals.
• Choose Methods : Select methods (e.g., interviews, focus groups).
• Sampling : Choose your participants and sampling method .

Data Collection

• Create Guidelines : Develop interview or observation questions.
• Build Rapport : Establish trust with participants.
• Record Data : Use audio, video, or notes.

Data Analysis

• Transcribe : Convert recordings to text.
• Code : Find themes and patterns.
• Interpret : Analyze and draw conclusions.
• Apply Framework : Use a theory to understand findings.

Ethical Considerations

• Consent : Get participant approval.
• Confidentiality : Protect privacy.
• Bias : Be aware of and address biases.

Rigor and Trustworthiness

• Triangulate : Use multiple sources.
• Member Check : Verify findings with participants.
• Peer Review : Get feedback from peers.
• Describe : Provide detailed context.

This concise format covers the essential steps for conducting qualitative research effectively.

Challenges and Opportunities in STEM Qualitative Research

• Access : Hard to reach participants or settings.
• Data Collection : Difficulties in gathering detailed data.
• Analysis : Time-consuming coding and interpretation.
• Bias : Avoiding personal biases.
• Generalizability : Balancing detail with broader relevance.
• Rigor : Ensuring research credibility.

Opportunities

• Understanding : Gaining deep insights.
• Context : Seeing issues in their context.
• Theory : Building or refining theories.
• Innovation : Finding research gaps.
• Collaboration : Enhancing findings through teamwork.
• Impact : Influencing policy and practice.

These points highlight the key challenges and opportunities in STEM qualitative research.

Overcoming challenges in qualitative research

Overcoming Qualitative Research Challenges

• Access : Build connections with gatekeepers.
• Recruitment : Use varied methods for diverse samples.
• Quality : Use multiple methods for accuracy.
• Management : Use data management software.
• Coding : Follow clear guidelines and check consistency.
• Saturation : Analyze until insights are complete.

Researcher Bias

• Reflexivity : Be aware of personal biases.
• Triangulation : Cross-check with various sources.
• Member Checking : Get participant feedback.
• Thick Description : Provide detailed context.
• Peer Review : Seek colleague feedback.
• Ethics : Follow ethical guidelines.

These simplified strategies help improve the quality and credibility of qualitative research.

Case studies of successful qualitative research projects in STEM

Check out the case studies of successful qualitative research prokects in STEM:-

• User Experience : Improving design through user feedback.
• Design Processes : How engineers tackle problems.
• Ethics : Public concerns about new technologies.
• Development : Teamwork and problem-solving in software.
• Cybersecurity : User practices and behaviors.
• Software Design : User needs and preferences.
• Education : Student views on science.
• Public Views : Opinions on scientific issues.
• Communication : How well science is communicated.

General STEM

• Diversity : Experiences of underrepresented groups.
• Education : Factors affecting STEM student success.
• Ethics : Ethical issues in STEM.

These concise points capture the essence of qualitative research topics in STEM.

Tips for finding research participants

Finding Participants for Qualitative Research

• Personal Contacts : Ask friends and colleagues.
• Academic : Collaborate with professors.
• Professional : Use industry connections.
• Social Media : Post on Facebook, LinkedIn.
• Forums : Engage in relevant online communities.
• Platforms : Use participant recruitment sites.

Traditional

• Flyers : Distribute in key places.
• Ads : Use newspapers or magazines.
• Organizations : Partner with relevant groups.
• Rewards : Offer small gifts or payments.
• Benefits : Explain the advantages of participating.
• Targeting : Focus on suitable participants.
• Transparency : Clearly state the research purpose.
• Consent : Ensure participants agree and understand.
• Privacy : Keep data confidential.

These points will help you effectively find participants for your research.

Writing a Research Proposal

Tips for Writing a Research Proposal

Research Question

• Clear : State the problem or question.
• Important : Show its significance.
• Feasible : Ensure it’s doable.

Literature Review

• Find Gaps : Spot missing areas in research.
• Framework : Build your study’s foundation.
• Justify : Explain its value.

Methodology

• Choice : Justify your method.
• Collection : Describe data gathering and recruitment.
• Analysis : Outline how you’ll analyze data.
• Consent : Explain how you’ll get it.
• Privacy : Describe how you’ll protect it.
• Bias : Discuss minimizing bias.
• Phases : Outline stages.
• Milestones : Set goals and deadlines.
• Resources : Identify needs and budget.
• Clarity : Use simple language.
• Structure : Organize with headings.
• Proofread : Check for errors.

These tips will help you craft a strong research proposal.

Combining Qualitative and Quantitative Research Methods

combining qualitative and quantitative research methoda:-

• Complementary Strengths : Qualitative adds depth; quantitative adds breadth.
• Enhanced Validity : Combining data strengthens findings.
• Deeper Insights : Provides a richer understanding.
• Improved Explanation : Qualitative can explain quantitative results, and vice versa.

Common Designs

• Sequential : Collect and analyze one type of data first, then the other.
• Concurrent : Collect both types of data simultaneously and integrate results.
• Embedded : One method is primary, with the other supporting it.
• Complexity : Managing both data types can be difficult.
• Resources : Requires more time and resources.
• Integration : Combining results needs careful analysis.

This summary captures the core aspects of mixed methods research.

Qualitative Research Topics for STEM Students in the Philippines 

Check out qualitative research topics for stem students in the philippines :-

Education and STEM

• STEM Programs : Effectiveness in Philippine schools.
• Teacher Views : Challenges faced by STEM teachers.
• Student Experiences : Insights from STEM students, especially marginalized ones.

Technology and Society

• Digital Divide : Tech access impact on marginalized groups.
• Social Media : Effects on STEM students.
• E-learning : Online STEM education effectiveness.

Environment and Sustainability

• Climate Change : Public views in the Philippines.
• Disaster Preparedness : Community responses to disasters.
• Indigenous Knowledge : Use in sustainability solutions.

Health and Medicine

• Medicine Integration : Traditional vs. Western medicine.
• Healthcare Access : Barriers in rural areas.
• Health Promotion : Effectiveness of campaigns.

Agriculture and Food Security

• Farming Practices : Traditional methods and challenges.
• Climate Impact : Effects on agriculture.
• Food Security : Access and dietary patterns.

Experimental Qualitative Research Topics for STEM Students

Check out experimental qualitative research topics for stem students :-

• Design Thinking : Document engineers’ design processes.
• Human-Computer Interaction : Test usability and observe user behavior.
• Engineering Education : Evaluate teaching methods and learning outcomes.
• Software Development : Study team collaboration dynamics.
• Cybersecurity Awareness : Assess user behavior in focus groups.
• Human-Computer Interaction : Test different interface designs.
• Science Education : Observe teaching and learning in science classrooms.
• Environmental Science : Explore community views on environmental issues.
• Public Health : Study health behaviors and attitudes in populations.

Mathematics and Statistics

• Math Education : Observe student engagement and difficulties.
• Statistics Education : Study perceptions of statistics.
• Data Visualization : Test visualization techniques and user preferences.

Qualitative research provides a deep look into STEM fields by exploring people’s experiences and views. This kind of research helps uncover insights that shape theories, practices, and policies.

Whether it’s about how people use technology, tackling educational issues, or understanding tech’s impact on society, qualitative research lets STEM students ask important questions and make a real difference. By using this approach, researchers can find new patterns, come up with creative solutions, and drive positive change.

The key to great qualitative research is careful planning, good methods, and a focus on the human side of STEM.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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HumSS Research Topics – Humanities & Social Sciences Topics

Humss (Humanities & Social Sciences) is an interesting field of study featuring college courses like Journalism, Communication Arts, and Education. Research projects for humss revolve around intellect, change, societal issues, and human conditions. Finding humss research topics is not as hard as it seems. For instance, you should know that research topics for humss differ from science topics because scholars are more interested in questions than answers. Also, your topics should be interesting and controversial to capture your readers. Choosing the right research topic about humss will simplify finding content and buy research paper .

Exciting Research Topic about Humss Strand

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Humss strand is one of the courses offered to students who want to pursue college degrees in education, liberal arts, or other social sciences. Choose any of the exciting topics below for your high school humss research project:

• The impact of aging on social interactions
• Anti-vaccination is the latest trending social movement
• Remote working is the latest trend in the corporate world
• What is the root cause of social media addiction?
• Is there a valid connection between social class and success?
• How much control should parents have over their kid’s social life?
• What is the appropriate age to start teaching students about gender studies?
• The impact of single parenting on a child’s social connection

Choosing interesting research about humss strand will help you stand out from the rest and impact the quality of your paper. Below are some thought-provoking humss research topics you can explore:

• Feminism in the corporate place: a critical analysis
• Does parental control influence a child’s social personality?
• Conventional families: how do they impact a child’s development?
• Growing up in an LGBTQ family: How does it influence a child’s sexual identity?
• The effects of social media on teens and youths
• The outcomes of social networking
• Are unconventional families beneficial for child development?
• Young motherhood: How does it impact a child’s wellbeing?

Are you a humss student looking for good topics for your research paper about the humss strand? Below are some ideas worth considering:

• The impacts of foreign education on professional growth
• The link between economic prosperity and the feeling of patriotism among citizens
• The right to privacy: a critical analysis in the digital era
• Social media preferences among different age and social groups
• Does social media increase or reduce loneliness among individuals?
• Is there a link between social media addiction and age?
• How important is adding food education to the modern education curriculum?
• A case study on the correlation between food and national identity

Whether you specialize in education, media, communication, liberal arts, or other social sciences, your humss research topic will influence your grade. You can choose an example of a research title about humss strand from the suggestions below:

• The changes that feminism has bought on gender roles at home
• The social perception of vegetarianism in different cultures
• Spirituality and raw food diets: what is the connection?
• Factors that affect students’ productivity during their free time
• Social media activism: is it as effective as old-fashioned street protests?
• Why you should take body language seriously during online interviews
• Twitter: How it shifted from an ordinary social media platform to a political platform
• Gender bias: concept definition

You can make your essay or research paper stand out and earn good marks by selecting quality topics. Pick a topic about humss strand from the ideas below:

• How has the digital era negatively influenced the social concept of morality?
• The impact of social media on people’s ability to understand others’ feelings
• Justice and wars: Who is the right person to judge?
• The influence of the mass media on political attitudes and statistics
• Awareness of public choice: Why is it so important?
• Framing: What is its role in the political sector?
• The root cause of reduced voter turnout: A case study of the United States
• What impact do advertisements have on political views?

Quantitative research involves collecting and analyzing data from deductive approaches like questionnaires while focusing on testing a specific theory. Finding a good top quantitative research topic about humss strand can make your study easier and more effective. Here are some noteworthy ideas:

• The electoral process in Michigan (specify location): A quantitative analysis
• The cultural practices related to childbirth rates in third-world countries
• An evaluation of the factors promoting teenage pregnancies in the 21 st century
• The rate of teenage pregnancies in third-world countries Vs. first-world countries
• Mass Media: Its impact on political statistics and voter behaviors
• How critical are self-defending networks?
• A critical analysis of the voter turnout in the recent elections in (state country or state)
• Can technology upgrades influence relationships?

Quantitative research involves data collection using questionnaires, interviews, and online or offline surveys. Below are some interesting topics you can write about in this area:

• How can cyber-crimes affect human lives?
• Racial bullying on social media: a critical analysis
• Drug testing in the workplace: is it necessary?
• How practical are modern components of sex education in High Schools?
• The impacts of the government controlling women’s reproductive rights?
• The root cause of stereotypes in society
• How gambling feels to an addict
• Group social education: What are its benefits?

Qualitative research depends on data obtained through first-hand observation, recordings, or focus groups. You can pick a good qualitative research topic about the humss strand from the following examples:

• Why do many students perform poorly in sciences?
• The rate of college acceptance in developing nations
• Academic preparedness of university students in the United States
• Victims of bullying in schools: a case study of (state a specific school or location)
• The relationship between android and apple products
• Online digital marketing: what is it all about?
• Virtual reality worlds: their role in transforming society
• Should kids under four years get a preschool education?

Humss is a vast field with thousands of research topic options for students with various specialties. Choose a research topic related to humss from the following option:

• The cultural construct of the masculine and feminine identity
• How individuals interact with various physical elements
• Inter-nation relationships: what challenges hinder healthy relationships between nations?
• The value of language in societal success
• How has the political sector in the United States evolved in the past century?
• The implications of philosophical studies for the growth of a society
• Diversity: how does it make society better?
• Peace and harmony: why are differences vital for peace and harmony?

Choosing a research title about humss can be challenging if you have not done one before. For this reason, we prepared the following title ideas:

• Religious discrimination in the digital era
• The conflict between religion and the digital era
• Social relations between Islam and Christianity
• The unification of Germany: a look at the process
• The great migration: a critical analysis
• Feminism movements and their impacts on society
• Does studying social sciences give you a better chance of success?
• The impact of the Ottoman Empire on socialization

When choosing the perfect research topics for humss, you should consider your specialization and research type (qualitative or quantitative). Here are some examples to consider:

• The impact of the pandemic on people’s social media behaviors
• Internet purchases: how sales taxes affect them
• The significance of understanding history in studying humanities
• Are all human beings anatomically similar?
• The role of humanities in higher learning institutions
• Do humanities help students achieve higher analytical and problem-solving skills?
• Why do universities require multiple humanities courses?
• The influence of William Shakespeare’s plays on modern literature

Focusing on a social issue is the best way to get a unique and interesting research topic for humss students. Here are some examples:

• The beginning of the feminist era
• How has the pandemic influenced the education sector?
• The implications of social media on religion and culture
• The impact of healthy doctor-patient relationships on the healthcare sector
• The relationship between social media interaction and personality development
• How is the digital era affecting the elderly in society?
• Modern inter-nation wars: implications of the war between Ukraine and Russia
• Is the United States still the most powerful country in the world?

Writing a research paper is as easy or hard as the topic you choose. Here are some humss research title ideas:

• The relationship between empathy and the experience of illness
• The impact of media on the study of medicine
• The relationship between social media and education
• Is diversity vital in society?
• The impact of gun violence on school attendance
• Modern aspects of poetry: a critical analysis
• The COVID-19 pandemic’s influence on social media addiction
• Social media addiction and age: what is the correlation?

Below are some key ideas on the topic about humss you can focus your research on:

• How do parents influence their children’s social behaviors
• Social education: how it helps students develop
• How do teachers include their student’s course choices?
• Boarding schools for boys Vs. boarding schools for girls
• How has social media influenced people’s views of celebrities?
• The role of social influencing in purchasing behaviors
• When is military force justifiable
• Should community service be mandatory for all students?

Your research title for humss will help you determine your paper’s outline and research methods. Below are some incredible topics you should consider:

• Do advertisements still influence people’s purchasing behaviors?
• Social media marketing Vs. conventional advertising
• Dual nationality: its impact on political views
• The implications of personality on political attitudes
• The correlation between collective action and public policies
• Do changes in public policies influence public opinions?
• The correlation between law-making and bureaucracy
• The influence of public policy on innovation

A concept paper provides your research’s purpose, background, and outline. Therefore, choosing the perfect topic is vital. Below are some ideas to look into:

• The US-Mexico Border Dilemma: an analysis
• Perfectionist policy: concept definition
• Why are more people turning to digital work in the 21 st century?
• Ethical issues in the dialysis of homelessness
• Effects of stigma among leaders
• How is technology reshaping the future of social interaction?
• Importance of practical counseling sessions for Psychology students
• How can parents cope with their kids’ disabilities

A good humss research paper should have a background research topic. Here are some great examples:

• The root cause of international cyber-attacks
• The history of Europe and its importance in humanities studies
• The root of punishment in households
• Should religious freedom be granted to kids under 18 years?
• The growth and spread of Islam in African nations
• How missionaries shaped Africans’ views on religion
• The impact of the Great Awakenings on US history
• The growth of Pentecostalism in Latin nations

Quantitative research is a dominant research technique in social sciences, where students can focus on topics like politics and elections. Here are some good ideas:

• The effectiveness of home care against nursing homes
• The development of telehealth in the 21 st century
• How effective are cardiovascular treatments?
• The link between mortality rates and gender
• The changes in critic ratings and their impact on equity returns
• Do people’s decision-making processes depend on their subconscious?
• Impact of racism on mental health
• Social anxiety triggers in youths

Let’s Help You

The humss strand is so vast that you can easily find a topic depending on your area of specialization. You can also pick a topic based on interesting social issues . Also, you must be keen on selecting a quality research title that stands out and makes your writing easier. If you feel overwhelmed choosing a title or writing a humss paper, we are here to help you. Talk to us now!

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Pursuing STEM Careers: Perspectives of Senior High School Students

2020, Participatory Educational Research

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A model proposal explaining the influence of smartphone addiction related factors on high school students’ academic success

• Open access
• Published: 26 August 2024

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• Engin Kutluay   ORCID: orcid.org/0000-0001-9347-2557 1 &
• Feride Karaca   ORCID: orcid.org/0000-0001-6342-4976 1  

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An exploratory sequential mixed-method study is designed to develop and test a comprehensive model explaining the relationships between factors associated with smartphone addiction and high school students’ academic achievement. Involving two main phases of qualitative and quantitative, focus group discussions with high school students and interviews with teachers were conducted in the first phase, both to decide on the factors and to reveal the relationships between the factors in the model. According to the qualitative results, the most important factors were found as social media usage, cyberloafing, academic procrastination, external and internal academic locus of control. Then, a hypothesis model involving these factors was developed to explain high school students' academic achievement. Obtained from 410 high school students, quantitative data are collected online by the use of some scales measuring the factors included in the model. Using the path analysis method, the hypothesis model was tested, and it was observed that the model fit the data well. As a result, the factors that most influence academic achievement were found as duration of social media usage, external academic locus of control, smartphone addiction, internal academic locus of control, academic procrastination, and cyberloafing respectively. The uniqueness of the presented model in this research is believed to lie in its holistic perspective on the relationships between smartphone addiction and related factors, and their effects on academic achievement. Looking from a big picture, this model is expected to provide a roadmap for practitioners and decision-makers in terms of how to improve students’ academic achievement.

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1 Introduction

Smartphones have become an indispensable part of daily life due to their various functionalities for information gathering, communication, education, and entertainment (Al-Khlaiwi et al., 2020 ; Haug et al., 2015 ). These functionalities have made smartphones increasingly popular, with new features being added (Altundağ & Bulut, 2017 ; Suresh et al., 2021 ; Yang et al., 2021 ). Recent developments suggest that these technologies can affect information exchange, social relationships, and the learning process (Shtepura, 2018 ) and may pose a potential risk factor, particularly for adolescents prone to excessive use and addiction to smartphones (Huang et al., 2021 ). Furthermore, potential risk factors associated with smartphone use and/or addiction, such as fatigue, stress, headaches, concentration issues (Ikeda & Nakamura, 2014 ; Khan, 2008 ), depression (Zhang et al., 2023 ), difficulties in concentration (Prasad et al., 2017 ), loneliness and weak social relationships (Yayan et al., 2019 ), poor sleep quality (Jniene et al., 2019 ), and low academic achievement (Baert et al., 2020 ; Felisoni & Godoi, 2018 ; Han & Yi, 2019 ; Kates et al., 2018 ; Kibona & Mgaya, 2015 ; Yang et al., 2019 ) are of concern. In fact, the adverse effects of smartphone use on educational performance may lead to broader societal consequences (Baert et al., 2020 ). Moreover, it has been reported that adolescents may be more vulnerable and susceptible to the excessive use of technology and smartphones (Ding, 2016 ). In this context, it is considered important to conduct studies that comprehensively examine the effects of smartphone addiction on the academic achievement of high school students.

While various studies have been conducted on factors influencing academic achievement (Ismail et al., 2018 ; Pérez et al., 2012 ; Sarıer, 2016 ; Széll, 2013 ), more research is needed to understand how and to what extent factors related to smartphone addiction, which has come to the forefront with technological development, affect academic achievement. Academic achievement is considered an essential criterion in determining the quality of human resources (Ali et al., 2009 ), making it crucial to identify the factors that predict it (Becerra et al., 2019 ). In the relevant field, various factors that can affect students' academic performance are discussed, including internal and external factors (Cachia et al., 2018 ), social, psychological, economic, environmental, and personal factors (Mushtaq & Khan, 2012 ), motivation, self-efficacy, parental attitudes, behaviors, and socioeconomic levels (Sarıer, 2016 ), technology usage, interaction processes, student characteristics, and class characteristics (Ismail et al., 2018 ). Correlational studies among various factors, such as academic achievement and smartphone usage (Baert et al., 2020 ; Kibona & Mgaya, 2015 ), smartphone addiction (Chaudhury & Tripathy, 2018 ; Khan et al., 2019 ; Oswal et al., 2020 ; Ozer, 2020 ; Rathakrishnan et al., 2021 ; Sunday et al., 2021 ), social media usage (Alamri et al., 2020 ; Giunchiglia et al., 2018 ; Lau, 2017 ), academic procrastination (Batool, 2020 ; Goroshit, 2018 ; Kim & Seo, 2015 ), academic locus of control (Anderson et al., 2005 ; Arslan & Akın, 2014 ; Bahçekapılı & Karaman, 2020 ; Uguak et al., 2007 ), and cyberloafing (Ravizza et al., 2017 ; Şenel et al., 2019 ; Wu et al., 2018 ) are all noteworthy. The following section includes a literature review that explains the relationships among the factors mentioned here.

2 Theoretical background

With the widespread use of smartphones, excessive usage and even addiction have become significant global issues (Sunday et al., 2021 ). Smartphone addiction can be characterized by the frequent use of smartphones for social networking and entertainment purposes, short periods of time between the last use before sleep and the first use after waking up, and excessive usage throughout the day (Haug et al., 2015 ; Lin et al., 2014 ). When smartphone usage interferes with daily life activities, it can lead to problematic or addictive behavior (Samaha & Hawi, 2016 ). In light of research in this field, uncontrolled, excessive, problematic, or compulsive smartphone usage can be considered among addictive behaviors (Bian & Leung, 2015 ; Cha & Seo, 2018 ; Haug et al., 2015 ; Marciano et al., 2021 ; Sunday et al., 2021 ; van Deursen et al., 2015 ).

Excessive smartphone usage can affect individuals' physical, mental, and social well-being, as well as their educational and professional lives (Amez & Baert, 2020 ). Indeed, findings from various studies in the literature have shown that academic achievement is affected by problematic smartphone use and/or smartphone addiction (Ahmed et al., 2020 ; Amez & Baert, 2020 ; Baert et al., 2020 ; Chaudhury & Tripathy, 2018 ; Grant et al., 2019 ; Khan et al., 2019 ; Kibona & Mgaya, 2015 ; Rathakrishnan et al., 2021 ; Sunday et al., 2021 ). When examining the literature, various studies have shown that the relationship between smartphone addiction and academic achievement can be negative (Amez & Baert, 2020 ; Kates et al., 2018 ; Sunday et al., 2021 ).

One of the most common purposes of students' smartphone usage is social media use (Dos, 2014 ). Social media platforms, which are used for building social relationships and sharing text, image, and audio content, also include instant messaging applications (Giunchiglia et al., 2018 ). Similar to smartphone usage, social media use is widespread among students (Lau, 2017 ). Especially, adolescents with a need for socialization tend to access their social media accounts via smartphones frequently (Enez Darcin et al., 2016 ; Minaz & Çetinkaya Bozkurt, 2017 ). Indeed, in the relevant literature, it has been emphasized that social media use can increase smartphone addiction (Alkın et al., 2020 ; Işık & Kaptangi̇l, 2018 ; Roberts et al., 2014 ; Sözbilir & Dursun, 2018 ). Furthermore, it has been reported that social media use is among the determinants of smartphone addiction (Yanık & Özçiçek, 2021 ). Additionally, duration of social media usage classes has been found to be related to problematic phone usage (Rozgonjuk et al., 2018 ). Several studies have shown that using social media on mobile phones can negatively impact academic performance (Domoff et al., 2020 ; Giunchiglia et al., 2018 ). In this field, negative and significant relationships have been found between students' academic achievements and their general use of social networks (Abu-Snieneh et al., 2020 ; Azizi et al., 2019 ; Kumcağız et al., 2019 ; Paul et al., 2012 ).

One of the factors related to smartphone addiction is cyberloafing, defined as the unintended use of the internet at work or in a school setting (Wu et al., 2018 ). Moreover, the use of the internet, social media, and/or smartphones for non-academic purposes in learning environments (such as classrooms or study spaces) can be seen as a cyberloafing behavior (Gezgi̇n & Sarsar, 2020 ; Junco & Cotten, 2012 ; Şenel et al., 2019 ; Şumuer et al., 2018 ; Yaşar & Yurdugül, 2013 ). The use of smartphones during class, as well as non-task-related technology use, has been shown to distract students' attention, which results in hindering their learning (Heflin et al., 2017 ). Additionally, cyberloafing behaviors in the classroom can reduce the time spent on learning and negatively impact performance (Ravizza et al., 2017 ). In fact, a study with university students have shown a negative relationship between cyberloafing behaviors in the classroom and academic performance (Wu et al., 2018 ). In another study with university students, it was found that students with low academic achievement may be more inclined to engage in cyberloafing behavior (Şumuer et al., 2018 ). Furthermore, the results of a meta-analysis showed that using the phone more during studying have more detrimental effects to learning and academic performance (Sunday et al., 2021 ).

Another factor related to smartphone addiction is academic procrastination, which refers to delaying or not completing academic tasks (Gafni & Geri, 2010 ; Steel, 2007 ). For today's students, when academic tasks are performed on smartphones, unwanted behaviors such as browsing the internet, engaging in social media, and watching videos can often lead to time wasting, problematic internet use, and procrastination of academic tasks (Aznar-Díaz et al., 2020 ). Therefore, academic procrastination is not a desirable behavior in terms of academic performance since it can reduce the time and effort students allocate to academic tasks (Gareau et al., 2019 ; Kim & Seo, 2015 ). Indeed, the results of various studies in the literature have shown that academic procrastination can have a negative impact on students' academic performance (Batool, 2020 ; Gareau et al., 2019 ; Goroshit, 2018 ; Kim & Seo, 2015 ; Kutlu & Demir, 2017 ; Sop, 2020 ; Üztemur, 2020 ).

Another factor related to smartphone addiction is academic locus of control. Locus of control represents an individual's belief in the accomplishment of a task (Arslan & Akın, 2014 ). Academic locus of control is related to how students attribute their successes or failures to certain factors (internal or external) (Hasan & Khalid, 2014 ). In other words, the tendency to associate success and/or failure with oneself, efforts, personal characteristics, and responsibilities indicates an internal academic locus of control, while explaining success and failure with external factors suggests a dominant external academic locus of control (Akın, 2007 ; Jain et al., 2018 ).

In the relevant field, positive relationships between external locus of control and smartphone addiction (Meena et al., 2021 ), cyberloafing (Blanchard & Henle, 2008 ), or internet addiction (İskender & Akın, 2010 ) have been found. However, one study found that individuals with a high internal locus of control may better control inappropriate smartphone use at inappropriate times, while those with a high external locus of control may struggle to control smartphone use at inappropriate times (Li et al., 2015 ). In a study with Chinese university students, excessive use of the WeChat social network application was shown to be associated with a higher external locus of control (Hou et al., 2017 ). On the other hand, university students with a high internal locus of control may have fewer online interactions, be less dependent on the internet, and take less risk in sharing information on social networks (Ahadzadeh et al., 2021 ). In light of all these studies, it can be suggested that an external locus of control may be related to addictive behaviors (smartphone addiction, social media addiction, internet addiction, cyberloafing, game addiction), and an internal locus of control may mitigate the risk of addiction (Hou et al., 2017 ; Lloyd et al., 2019 ; Meena et al., 2021 ; Ye & Lin, 2015 ).

In the literature, it has been shown that locus of control can affect academic procrastination (Batubara, 2017 ), and an external academic locus of control has been positively related to academic procrastination (Albayrak et al., 2016 ; Özer & Altun, 2011 ). However, the locus of control has been identified as one of the factors affecting the time students spend studying (Bodill & Roberts, 2013 ). Bahçekapılı & Karaman ( 2020 ) found that an external academic locus of control had a direct negative effect on university students' academic achievements. On the other hand, Abid et al. ( 2016 ) found that university students with an internal locus of control had higher learning performance than those with an external locus of control. Furthermore, Jain et al. ( 2018 ) found that participants with an internal academic locus of control were more advantageous in terms of academic performance compared to participants with an external academic locus of control.

In conclusion, although various studies have been conducted on factors related to smartphone addiction, the number of studies that examine the relationships between these factors and their effects on academic achievement is quite limited. Moreover, research explaining the relationships between smartphone addiction and academic achievement in a comprehensive model is rare, with most studies focusing on university students (Oswal et al., 2020 ; Rathakrishnan et al., 2021 ; Sunday et al., 2021 ), while the number of studies with high school students who use smartphones intensively is relatively limited. Nevertheless, Haug et al. ( 2015 ) found that smartphone addiction in adolescents is higher compared to young adults aged 19 and above. In fact, even a short period without smartphone use may be intolerable for adolescents (Thomée, 2018 ). Additionally, it has been reported that adolescents are more attracted to smartphones, and they are considered a high-risk group for smartphone addiction (Cha & Seo, 2018 ). As a result, smartphone addiction is a risk factor that can negatively affect academic performance in adolescents (Domoff et al., 2020 ). Therefore, more research is needed to examine the effects of smartphone addiction on the academic achievements of high school students (Ng et al., 2017 ; Zou et al., 2019 ). This research, being a mixed-methods study and explaining the relationships between smartphone addiction and academic achievement in a comprehensive model, is expected to shed light on studies in the field. Furthermore, it provides a holistic view of the factors related to smartphone addiction that can affect students' academic performance. The model developed in this study, with both qualitative and quantitative stages, can provide a more comprehensive framework. This model is expected to provide a roadmap for practitioners and decision-makers in terms of the relationship between smartphone addiction and academic achievement. Therefore, the development of a model that can offer a comprehensive perspective on the factors related to smartphone addiction that affect the academic achievements of high school students is believed to make a significant contribution to the literature.

3 Research objectives

The primary aim of this research is to develop a model that explains the impact of factors related to smartphone addiction on the academic performance of high school students. To achieve this objective, the following research questions will be addressed:

What are the opinions of high school students and teachers regarding the factors that affect academic success in relation to smartphone addiction?

What are the factors that affect the academic performance of high school students in relation to smartphone addiction?

To what extent does the model encompassing the direct and indirect relationships between factors related to smartphone addiction and the academic performance of high school students explain academic success and other internal factors?

Employing a mixed-methods approach, this study was designed in the form of an exploratory sequential design consisting of successive qualitative and quantitative phases (Creswell & Creswell, 2018 ). Thus, this study consists of consecutive qualitative and quantitative phases. In the first phase, factors affecting academic achievement related to smartphone addiction were identified, along with the relationships among these factors, using qualitative data. As shown in Fig.  1 , the initial phase of the research involved a literature review, followed by one-on-one interviews with teachers and focus group interviews with students, aiming to identify the factors related to smartphone addiction that explain academic success and to reveal the relationships between these factors. Following the analysis of qualitative data, factors related to smartphone addiction were identified. Both qualitative findings and models from the literature were used to determine interrelationships among factors. Consequently, based on the identified factors and their interrelationships, a hypothesis model was proposed. In the second phase of the study, scales were decided for the factors included in the model. Subsequently, quantitative data were collected and this hypothesis model was tested.

Research design

Before starting to conduct this research, an approval was taken from the Research Ethics Board of the Institute of Educational Sciences at Marmara University. Furthermore, ethical consideration were taken into account by informing participants about confidentiality, respect for privacy, and the purpose of the research. Additionally, the researchers get permissions from the participants for recording the interviews.

4.1 Phase I: Qualitative research

In this phase, it was aimed to identify the factors related to smartphone addiction that affect high school students’ academic achievement, and to propose a hypothesis model including these factors. To achieve this aim, focus group interviews were conducted with 20 high school students from a state school in Istanbul, and interviews were conducted with 15 teachers working at the same school. In the initial stage of the qualitative study, the school was selected using the convenient sampling method which is one of the preferred methods when limited time and resources are available (Dawson, 2009 ).

The students to be involved in focus group interviews were selected using maximum variation sampling method, which allows to select individuals with different perspectives (Creswell & Creswell, 2018 ). The students' academic performance was taken into consideration to decide on the participants to be involved in focus group interviews. To determine common and divergent aspects in terms of smartphone addiction and related factors, separate focus groups were held for students with a general grade point average (GPA) below 50 and those with a GPA above 50. Similarly, in the selection of teachers, the maximum variation sampling method was used to ensure that teachers from different subject areas in the chosen school were included. The interviews lasted approximately 40 min.

In the analysis of qualitative data, common patterns and relationships among prevalent factors were considered, and content analysis was employed to uncover concepts and relationships that could explain the collected data (Yıldırım & Şimşek, 2016 ). Content analysis included the stages of description, coding, categorization, and labeling (Patton, 2015 ). Additionally, an independent coding was conducted by a different field expert, and after a collaborative process, an agreement criterion of 94.25% was reached among the coders (Miles et al., 2014 ).

As a result of analyzing the content of the interviews, considering both the number of opinions and the number of individuals, five factors with the potential to have a significant impact were identified. These factors include “Duration of Social media usage”, “Smartphone addiction”, “Academic procrastination”, “Cyberloafing” and “Academic locus of control”. Subsequently, both qualitative findings and literature review were used to examine the relationships between these factors. Ultimately, as shown in in Fig.  2 , a comprehensive hypothesis model encompassing these factors and their relationships was proposed.

Hypothesized model

4.2 Phase II: Quantitative research

4.2.1 determination of scales.

In the quantitative phase of the study, the aim was to test the hypothetical path model proposed in the previous phase of the study. To achieve this goal, a questionnaire containing questions and scales related to all variables in the hypothetical model was used in this phase. This questionnaire consisted of five main sections.

First, a personal information form containing questions about the students' self-reported duration of social media usage and academic achievement scores (GPA) was utilized. This form also included 14 questions, such as students' gender, age, grade level, department, and duration of daily phone usage. Second, the “Smartphone Addiction Scale—Turkish Version”, developed by Kwon et al. ( 2013 ) and adapted to Turkish by Demirci et al. ( 2014 ), was employed to measure smartphone addiction levels. Involving 33 items, this scale is based on a six-point Likert scale (1: Definitely not, 6: Definitely yes). Third, participants' cyberloafing levels were measured using the “Cyberloafing Activities Scale,” developed by Blau et al. ( 2006 ) and adapted to Turkish by Polat ( 2018 ). This scale comprises 16 items and uses a six-point Likert scale (1: Never, 6: Always). Fourth, to measure academic locus of control, the “Academic Locus of Control Scale,” developed by Akın ( 2007 ), was used. The scale consists of 17 items and uses a five-point Likert scale (1: Strongly disagree, 5: Strongly agree).

Finally, the short Turkish version of the “Academic Procrastination Scale,” developed by McCloskey ( 2011 ) was adapted to Turkish for this study and it was employed to assess academic procrastination levels. This short form contains 5 items and uses a five-point Likert scale (1: Strongly disagree, 5: Strongly agree). The procedure recommended by Hambleton et al. ( 2005 ) was followed to adopt this scale to Turkish. The English version of the scale was translated into Turkish by three language experts fluent in both English and Turkish. The translations of the three experts were consolidated into a single form and sent to three experts in the Computer Education and Instructional Technologies department. Based on the feedback received from these experts, the final version of the scale was developed. Subsequently, the final version of the scale was sent to an academician in Turkish Language and Literature, and it was determined that there were no language-related issues in the Turkish version. As a result, it was found that the language equivalence of the items in the Turkish form was established. Then, a pilot study was conducted with 131 high school students enrolled in a private educational institution to determine that the Turkish version of the form structurally similar to the original short form. In the pilot study, the Kaiser–Meyer–Olkin (KMO) value, determined as 0.77, indicated that the data matrix was suitable for factor analysis (Büyüköztürk, 2010 ). Additionally, the factor analysis revealed a unifactorial structure with eigenvalues greater than 1, explaining 53% of the total variance (Büyüköztürk, 2010 ). It has been reported that for single-factor scales, it may be sufficient for the explained variance to be 30% or more (Büyüköztürk, 2010 ). Therefore, the Turkish version's factor structure was found to be similar to the original short form. A Cronbach's alpha coefficient of 0.78 was determined, and it was concluded that no items needed to be removed.

4.2.2 Data collection and testing the model

Schools participating in the study were selected using the convenient sampling method (Dawson, 2009 ). In this context, two “Science High Schools,” two “Anatolian High Schools,” and two “Vocational and Technical Anatolian High Schools” were selected as the researchers have access to six teachers in these schools. These teachers sent the relevant online form links to the class advisors in their schools through a social media application. As a result, a total of 461 students were sent the questionnaires, and 421 students returned them, resulting in a 91.3% response rate. After removing missing and erroneous data, the number of participants in the quantitative part of the study was determined as 410.

In conclusion, in this research, the path analysis method was used to test the hypothetical model, examine direct and indirect effects, and determine which independent variable is the most influential or important factor, as it can demonstrate all kinds of effects of each independent variable on the dependent variable (Agresti, 2018 ). This method was especially preferred as it allowed to understand the effects of each independent variable on the dependent variable in a comprehensive way (Nayebi, 2020 ). In this study, SPSS Statistics 22.0 was used for missing value analysis, data normality, linearity, multicollinearity, and singularity analyses, while AMOS 23.0 was used for model fit via path analysis.

5.1 Phase I: Qualitative research findings

5.1.1 findings on smartphone addiction.

All students who participated in the focus group discussion were smartphone users, and it was determined that excessive usage was more common in the focus group with a general grade average below 50 compared to the other group. It was understood that when these students' smartphone usage infiltrated their daily lives or became uncontrolled, they exhibited behavior resembling addiction symptoms. For example, some students reported experiencing problems such as impatience, irritability, and restlessness in the absence of their smartphones. Especially in the focus group with a general grade average below 50, students were observed to constantly think about their smartphones, wanting to use them without interruption, and getting angry when disturbed while using them. Furthermore, in the group with a general grade average above 50, it was found that addiction-like behaviors were less common. Some used their phones to relieve stress, while others could use them excessively for study preparation.

Most of the students in both focus groups expressed that smartphone usage negatively affected their academic performance. Those with negative opinions were mainly focused on the idea that they used their phones excessively and in an uncontrolled manner. From the students' statements, it was understood that smartphone usage may not necessarily affect academic performance if used in moderation, but it could have a negative impact when used excessively or when addiction-like behaviors towards smartphones were developed. For example, in the focus group with a general grade average below 50, the following statements stand out:

“I can't sleep at night because of using the smartphone all the time, and I sleep in the first four classes at school. As a result, it can't be said that I really pay attention to the lessons.” (S7). “I don't have much time to study because I can't stop using it most of the time, and my performance is inevitably dropping, but even if using the phone is banned at school, I still don't think I will be successful because it's not prohibited at home.” (S2).

On the other hand, some students in the focus group with a general grade average above 50 stated that smartphone usage had a positive impact on their academic success, while others expressed that smartphone usage would not affect their success when used in a controlled manner. For example, some students used the following statements regarding this issue:

“I don't use it too much if I don't need to study, but when I have classes or can't learn some topics well, the usage can go up to 5-6 hours while watching educational videos. But if I don't have classes, I use it for 1-2 hours.” (S5). “I think it will definitely reduce success if you use a smartphone to play games or hang out on social media.” (S5). “After using it as much as needed, there is no problem. That is, if it is used without interrupting the lessons. For example, when I get tired of studying, I watch an entertaining video for 5-10 minutes to relax and feel better.” (S8).

Teachers who participated in the research believe that high school students excessively use smartphones, have a strong desire for smartphone usage, and think that this desire leads to uncontrolled use and addiction. However, most teachers believe that students' smartphone usage is at an addictive level and negatively affects academic success. For example:

“In our school, this situation is more pronounced; they don't have a minute without their phones. I definitely think they are addicted, so failure is inevitable.” (T11). “In some classes, when I try to teach, I see that more than half of the students are asleep. When I ask them the reason, I get answers like 'I couldn't sleep at night,' 'I stayed up too late, sorry.' When I ask why they stayed up late, they say they were watching movies or playing games on their phones. I really think the situation is quite serious.” (T7). “While trying to teach, I observe that many students constantly have their hands on their phones and try to place them on the desk to read incoming messages deliberately. Our warnings lose their effect after a certain period, and they try to make it a natural habit because some teachers allow them to use their phones during class.” (T13).

5.1.2 Findings on social media usage

Content analysis revealed that the majority of students in the focus group with a general grade average below 50 had a high daily duration of social media usage that significantly interfered with their daily activities, and their usage was predominantly interactive. In contrast, although the focus group with a general grade average above 50 had lower social media usage times, the use of social media for study preparation was significantly higher compared to the other group. However, students in the focus group with a general grade average below 50 generally believed that their social media usage could negatively impact their academic performance. For example:

“It affects me a lot, especially because of social network usage. Even during exam times, I continue to use unnecessary social networks. I realized it had a bad effect on my academic success when the exam results were announced. I use my phone for 7 hours a day, and 5 of those hours are spent on social media.” (S2). “It has a negative impact, but phones can also have a positive effect on studying. Some of my friends can enhance their academic success by watching educational videos. But because I always find myself watching funny videos, it negatively affects my performance.” (S3).

Students in the focus group with a general grade average above 50 stated that their academic success could be negatively affected if they used smartphones uncontrollably, but it could positively impact their success if used in a controlled and purposeful manner. For example:

“If my smartphone is in the same room while I'm studying, messages from WhatsApp or Instagram can distract me, which negatively affects my performance. However, sometimes there are very useful educational content and posts on social media. I usually watch those videos, so I think it positively affects my academic performance.” (S7).

According to teachers, the majority of high school students use social media on their smartphones for interaction purposes. Therefore, teachers believe that high school students do not allocate time for studying, spend most of their time on social media, and that parents complain about this situation. In individual interviews, all teachers explained that students spend too much time on social media. According to teachers, the majority of high school students use social media for interaction purposes, including chat, comments, messaging, photo, and video sharing. For example, most teachers expressed the opinion that the relationship between social media usage and academic success was negative:

“It has a clear negative effect. They use it excessively, and they spend all their time on social media, and they don't care about studying. They don't do homework, and they don't care about failing.” (T12). “It doesn't matter whether students are male or female; they use their phones even during classes, which shows their dependence. For example, when I catch students playing with their phones during class, WhatsApp, Instagram, or YouTube are usually open on their phones.” (T3). “They never study efficiently; when I allow them some freedom, very few of them focus on the lesson, and most of them either start messaging someone or look at pictures on Instagram.” (T14). “It definitely has a negative effect because they see themselves as someone different on social media. For them, it's like a sanctuary. They are happier on social media platforms than in real life. That's why they want to use it constantly.” (T14).

5.1.3 Findings on cyberloafing

Another factor that explains academic success in relation to smartphone addiction and is frequently mentioned in focus group discussions is cyberloafing. According to focus group discussions, students with a general grade average below 50 tend to engage more in recreational and interactive cyberloafing. A significant portion of the students admitted to engaging in recreational cyberloafing during class, such as playing games or other entertaining activities. On the other hand, a significant portion of the students also acknowledged engaging in interactive cyberloafing, such as sending instant messages, sending emails, or checking emails. However, students in the focus group with a general grade average below 50 generally believed that their cyberloafing behaviors were negatively related to academic success. For example:

“Especially because I don't understand anything in math, I play with my phone during the class without the teacher noticing, so I don't really pay attention to what's being taught or what assignments are given. That's why I get very low grades.” (S7). “Classes are generally boring; I only come to school to not upset my mom and to get a diploma. Maybe if I didn't play with my phone during class, I could get better grades, but especially for subjects like math, even if I study, it doesn't work, so I prefer to play with my phone, and I generally get low grades.” (S5).

Some students in the focus group with a general grade average above 50 mentioned that they engaged in cyberloafing because they found the classes boring. For example:

“I really don't like math, so when the teacher is explaining something, I don't understand it even if I listen. So, at that time, I chat with friends, but when I listen to the lesson, I get really bored.” (S8).

Teachers' opinions on students' social media usage are mostly related to the recurring themes of entertainment and interactive subthemes. The entertainment subtheme of cyberloafing refers to students playing online games, using their phones, or spending time on entertainment activities during class. The interactive subtheme, on the other hand, pertains to students engaging in activities such as sending instant messages during class. According to individual interviews with teachers, the majority of teachers revealed that cyberloafing behaviors were negatively related to students' academic success:

“Of course, it has a negative effect. Even during class, they are constantly trying to hang out on social media. It seems like their goal is not to study, but they come to school just because their parents want them to.” (T6). “Yes, it happens, especially male students try to sleep by putting their heads on their desks during class. In fact, they are watching videos under the desk or visiting sports websites. That's why their grades are generally low. Sometimes, when I warn them, and if they don't pay attention, I have to take their phones away.” (T14).

5.1.4 Findings on academic procrastination

Another factor explaining academic achievement in relation to smartphone addiction is academic procrastination. According to student expressions, academic procrastination is a situation where they find it challenging to start studying, tend to delay tasks, and often submit assignments at the last minute. When examining the study results, it is evident that the majority of students in the focus group with a general grade point average below 50 indicated a negative relationship between academic procrastination and academic success, stating that they cannot find time to study. For instance:

“The main reason for my low performance is the inability to start studying. Whenever I try to study, a notification from my phone distracts me, and I find myself dealing with the phone instead.” (S2). “I don't have time to study, and the assignments given are really tough. Sometimes I open entertaining educational videos on YouTube, but while watching them, I come across more interesting videos and end up watching those instead.” (S10).

A significant majority of teachers believe that academic procrastination in high school students is associated with irresponsibility. Irresponsibility implies that students allocate more time to activities they enjoy and are unable to initiate studying. In fact, most teachers participating in this study expressed that students engage in academic procrastination due to excessive use of smartphones and social media, negatively impacting academic success. For example:

“Very few students submit performance tasks and project assignments. Most students don't care about their grades or whether they will pass the class. They don't study or do their assignments because their minds are constantly on their phones. Based on complaints from parents, it is understood that students excessively use their phones not only at school but also at home.” (TCH 1). “It wouldn't make a difference even if it were completely banned. They have lost control; as far as I have observed, they are excessively attached to their phones. For instance, on exam days, they ask for permission to study, and sometimes I allow it, but they never start studying; they are busy with their phones most of the time.” (TCH 2). “Based on what we observe in classes and learn from parents, the most negative factor affecting students' success is their excessive use of social media and smartphones. In short, they do not use social media consciously. Some students are so addicted that they want to use their phones even during classes, to find out what's happening on Instagram or to play games. In fact, when I open the internet from the smartboard to teach, some students request, 'Could you open YouTube, and we watch funny videos?' They have no intention of studying at all. If they submit assignments, at least their performance grades would be high, but they don't care. I am sure that ten years ago, only a small percentage of students did not submit assignments, but now this ratio has significantly increased.” (TCH 3). “When I assign a project, I constantly have to remind students to start. Even if I remind them of the deadline, the number of those who actually start the project is quite low. Those who do usually copy content unrelated to the assignment just to submit it and get it over with.” (TCH 8). This is already one of the most significant problems. You asked about studying during the question, but they can't start studying because of the phone. When I ask them the reason for not submitting assignments, one response I get is that they struggle while doing assignments, and I think it's because they find assignments difficult due to not studying at all (TCH 4).

5.1.5 Findings on academic locus of control

In this study, another factor explaining academic achievement in relation to smartphone addiction is identified as academic locus of control (f = 62). Among the opinions of the students participating in the focus group interviews, the internal locus of control subtheme was reiterated 33 times by 9 students (f = 33, n = 9), while the external locus of control subtheme was reiterated 29 times by 7 different students ( f  = 29, n  = 7). It was observed that students with a general grade point average below 50 tended to attribute their failure more to external factors. External academic locus of control refers to attributing one's success or failure to factors beyond oneself. For instance, one student (S5) explained studying to avoid disappointing parents: " I don't like studying; I haven't liked it since childhood. Actually, it would be more accurate to say since I started middle school. But when I fail, my mom gets very upset, so I try to study so she won't be sad. " Another student (S6) expressed reluctance to study due to the perceived easiness of exam questions: " I don't think I need to study. After all, the questions are very easy, and I usually get lucky, so I don't feel the need to study or start studying ." Another student (S8) attributed not studying to a disagreement with the teacher and explained, " But I don't want to do the assignments given by some teachers because I don't get along with them, but I know it harms me ." Yet another student (S4) attributed not studying to lack of time caused by social media:

“ I don't have big goals for myself; I just want to get my high school diploma, that's enough. So, I don't need to work too hard. If you have a good relationship with the teacher in vocational courses and show respect, you'll pass with an average above fifty. I try to focus on those courses, but it's limited. Most of my time is spent on the phone anyway. Watching YouTube videos takes up most of my time, so there's hardly any time left for studying. ”

Internal academic locus of control refers to attributing success or failure situations to personal efforts. It was found that students with a general grade point average above 50 tended to attribute their success or failure more to their own efforts. For example, one student (S3) attributed his success to his own efforts and stated, “ I don't think the smartphone has a very negative impact on my success. Because even if I mess around with my phone a bit after studying effectively for an exam, it doesn't affect me in the end. I get bored of studying too much sometimes, but the phone is a bit of a distraction.” Another student (S7) saw his failure as his own fault and said, “ Dealing with the phone sometimes negatively affects starting to study. But my family and I made a decision. When I have an exam, I put the phone in another room. I don't get distracted while studying because I took this precaution. But when I first got the phone, I used it excessively, and that's why I couldn't study well for the chemistry exam. So, it was my fault .”

A student (S4), who believed that excessive use of the phone negatively affects success, mentioned, “ If I mess around too much with the smartphone, of course, my performance will drop, but I use the phone not to play games but to watch videos of topics I don't understand. So, I think the smartphone increases my success. ” Similarly, another student (S9) who believed that his success was not affected because he did not use it excessively stated, “ It doesn't affect me because I don't use the smartphone too much. When I use it, I ask my friends about the parts I don't understand. So, it can positively affect my success in that regard. ” Other example opinions supporting the internal locus of control theme are as follows:

“Some of the low scores I get in exams are not solely because of the smartphone; it's also because I don't want to start studying. When I study, I can get high grades. But the truth is that sometimes I can't sleep because of the phone. It makes me tired too.” (S2). “After becoming a responsible student, the smartphone doesn't negatively affect success. I think I'm doing my best to be successful. I make an effort. Sometimes I may not have worked enough for exams where I got low grades because I didn't have time.” (S1).

In conclusion, according to student views, the relationship between academic locus of control and academic success varied depending on the type of locus of control. For example, in the low-achieving group, the frequency of reiteration of the external locus of control theme (f = 24) was much higher than the frequency of reiteration of the internal locus of control theme (f = 3).

According to teacher views, the frequencies of reiteration of factors affecting students' academic locus of control were external locus of control (f = 42, n = 11) and internal locus of control (f = 10, n = 3), respectively. When teacher views were analyzed, it was observed that the opinion that students mostly have an external locus of control was reiterated more frequently than the opinion that they have an internal locus of control. Some teachers also believed that students with specific goals use smartphones in a more controlled way and make an effort to be successful. For example, one teacher (TCH9) stated the following about students having an external locus of control:

“ Most students are addicted to their phones; it's really worrying that their minds are constantly on their phones. We need to know the underlying reasons for this. According to my observations, most students think the subjects are unnecessary. When I ask them why they don't listen, they can ask why learning the subject will be useful for them. Those who listen can be affected by the smallest things and experience concentration problems, becoming students who do everything they can not to listen to the lesson .”

Similarly, another teacher (TCH14) explained that students attribute their failures to factors outside themselves:

“When students get high grades, it's always their success, but when they get low grades, they can say, 'Teacher, you gave a very low grade.' Although most students mess around with their phones during class and don't listen to the lesson properly, parents also complain that students are constantly playing with their phones.”

Some teachers also believed that students with specific goals use smartphones in a more controlled way and make an effort to be successful. For example, one teacher (TCH7) indirectly expressed that when students have an internal locus of control, smartphones do not negatively affect their successes:

“The use of smartphones negatively affects students who already have no sense of work. In our school, there are project classes where hardworking students are the majority and classes where non-hardworking students study. Since I enter both types of classes, I can say comfortably that students in classes with hardworking students know that if they play with their phones and don't listen to the lesson, they will fail, so they don't usually need to be warned about this. Sometimes I have to ask them to do research with their phones in those classes. They can immediately put their phones down when the task is done. However, in the other classes, I have to remind them constantly. They have no sense of responsibility.”

Another teacher (TCH15) stated that students who attribute their failures to factors outside themselves use social media more, try to use their phones during class, do not complete the given assignments, and ultimately have very low academic achievements:

“Since I teach vocational courses, some classes have a considerable number of weekly hours. Students passing the class depends on the grades they get from these courses. So, I don't have much smartphone use in my own class because I share the rules with the students from the beginning. But if a child's only goal is to get a diploma, if he has no future goal, if he doesn't like his department, if he only comes to school under the pressure of his family, such children try to use their phones during class, do their assignments either superficially at the last minute or not at all. Naturally, they can't pass the class because they can't meet the average. But I receive many complaints about smartphone use, especially from cultural teachers and parents. Students who cause problems are generally students who have no purpose in life, I think. As far as I have learned from parents, students who want to achieve something, have the goal of getting a good job, and are responsible, use the phone and social media more than usual, but at least they try to comply with the rules during class and do not delay their assignments to the point of becoming a problem.”

6 Summary of qualitative research findings

Statistical information regarding themes derived from teacher and student perspectives is summarized in Table  1 . As a result of the interviews, five factors potentially influencing academic achievement in relation to smartphone dependency have been identified: “duration of social media usage,” “cyberloafing,” “academic procrastination,” “academic locus of control,” and “gaming addiction.” However, considering that the sub-dimensions of academic locus of control, namely internal and external academic locus of control, may have different effects on smartphone dependency, duration of social media usage, cyberloafing, academic procrastination, and academic achievement, and due to the common practice of separately addressing these sub-dimensions in the literature, it was decided in this study to add the dimensions of academic locus of control as two separate factors to the proposed model.

In determining the prevalent factors to be involved in the model, four criteria were taken into account: context in which the response is given, consistency of the response, frequency of themes, and the number of individuals making similar comments (Baş & Akturan, 2017 ). As seen in Table  1 , both the frequency of theme repetitions and the number of individuals indicate that values related to the gaming addiction theme are dramatically lower compared to other themes. Therefore, it was decided to include all factors except gaming addiction in the model. Consequently, the model proposed in this study includes five prevalent factors: smartphone dependency, duration of social media usage, cyberloafing, academic procrastination, and academic locus of control. Once the factors to be included in the model were determined, a path model was developed based on both qualitative findings and relevant literature, elucidating the relationships between these factors (see Fig.  2 ).

6.1 Phase II: Quantitative research findings

6.1.1 preliminary analyses.

Considering that the sample size for the data collected from 410 high school students is over 200, it was found sufficient for path analysis (Kline, 2016 ). As the missing value analysis results indicated that four participants did not respond to more than 90% of the items, their data were excluded from the study (Çokluk et al., 2018 ). Additionally, data from seven participants were excluded from the study as discrepancies were detected in their responses to control-purpose fake items in the survey (Berry et al., 2019 ). Consequently, data for a total of 11 participants were not included in the path analysis, and quantitative data analyses were conducted with the remaining 410 individuals.

The existence of outliers that could complicate the fulfillment of normality assumptions was examined through Mahalanobis distance values in this study (Aksu et al., 2017 ). As a result, no outliers were detected based on the calculated Mahalanobis distances in this study (Kline, 2016 ; Stevens, 2009 ). Furthermore, since the Cook's distance assumption was satisfied due to being less than 1, and Cook's distances were smaller than 4/410 = 0.00975, it was determined that there were no outliers (Karagöz, 2016 ).

Since skewness and kurtosis coefficients are reported to be excellent within ± 1.0 for most psychometric purposes and acceptable within ± 2.0, the distribution is assumed to be normal in this study (George & Mallery, 2020 ). Furthermore, as shown in Table  2 , all correlation values between the factors were less than 0.90, so the assumption of multicollinearity is satisfied (Kline, 2016 ). In addition, since all VIF values are less than 5, all CI values are less than 30, and all tolerance values are greater than 0.01, multicollinearity assumption was also met (Aksu et al., 2017 ). The normality of residuals was checked with a histogram and P-P plot, and it was observed that the residuals were normally distributed, and the linearity assumption was satisfied. However, since Durbin-Watson values are desired to be between 1.5–2.5, and in this research, according to the analysis conducted, d = 2.02, the assumption of independence of errors was satisfied (Karagöz, 2016 ). Ultimately, it was concluded that all assumptions of path analysis were met for the data obtained in the research.

6.1.2 Path analysis

The hypothesized model proposed in the study (see Fig.  2 ) was tested using path analysis. When examining the values of the multiple fit indices; χ2/df = 3.09 (n = 410), RMSEA = 0.072, SRMR = 0.008, NFI = 0.997, TLI = 0.981, CFI = 0.998, GFI = 0.999, it is observed that the model has an acceptable level of fit (Karagöz, 2016 ; Kline, 2016 ; Sümer, 2000 ). Direct, indirect, and total effects on each dependent factor in the model are presented in Table  3 . In this study, a path coefficient of + 0.1 is considered a small effect, + 0.3 a moderate effect, and + 0.5 a large effect (Field, 2009 ; Kline, 2016 ). The default model and the related standardized path coefficients resulting from the path analysis are shown in Fig.  3 . Six factors in the model, namely duration of social media usage, smartphone addiction, cyberloafing, academic procrastination, external academic locus of control, and internal academic locus of control, explained 73% of the variance in academic achievement. All six factors in the model showed a significant total effect on academic achievement. Duration of social media usage (β = -0.701, p < 0.05) and external academic locus of control (β = -0.509, p < 0.05) have a strong total effect on academic achievement, while smartphone addiction (β = -0.350, p < 0.05) has a moderate total effect. However, internal academic locus of control (β = 0.168, p < 0.05), academic procrastination (β = -0.129, p < 0.05), and cyberloafing (β = -0.044, p < 0.05) have a small total effect. Duration of social media usage, smartphone addiction, and academic procrastination showed significant direct effects on academic achievement. Duration of social media usage (β = -0.425, p < 0.05) has the strongest direct effect on academic achievement among high school students. In addition, smartphone addiction (β = -0.305, p < 0.05) has a moderate direct effect, while academic procrastination (β = -0.129, p < 0.05) has a low direct effect on academic achievement. Although the direct effect of external academic locus of control on academic achievement is not significant, its indirect (β = -0.455, p < 0.05) and total effects are high (β = -0.509, p < 0.05). Similarly, internal academic locus of control does not have a direct effect on academic achievement, but its indirect (β = 0.134, p < 0.05) and total effects (β = 0.168, p < 0.05) are considered low. Despite its weak direct effect, external academic locus of control has shown the strongest indirect effect on academic achievement because it partially mediated the relationship between external academic locus of control and academic achievement, including duration of social media usage, smartphone addiction, cyberloafing, and academic procrastination. Additionally, duration of social media usage (β = -0.276, p < 0.05), smartphone addiction (β = -0.045, p < 0.05), cyberloafing (β = -0.044, p < 0.05), and internal academic locus of control (β = 0.134, p < 0.05) have significant indirect effects on academic achievement.

Path model and standardized path coefficients

Three factors in the model, namely duration of social media usage, external academic locus of control, and internal academic locus of control, explained 71% of the variance in smartphone addiction. The total effect of all three factors on smartphone addiction is significant. Duration of social media usage (β = 0.680, p < 0.05) has the greatest total effect on smartphone addiction among high school students. Additionally, external academic locus of control (β = 0.536, p < 0.05) has a large total effect, while internal academic locus of control (β = -0.164, p < 0.05) has a low negative total effect.

Two factors in the model, namely external academic locus of control and internal academic locus of control, explained 40% of the variance in social media usage. Both of these factors have significant direct and total effects. External academic locus of control (β = 0.513, p < 0.05) has a strong direct and total effect on duration of social media usage, while internal academic locus of control (β = -0.164, p < 0.05) has a low direct and negative total effect.

Four factors in the model, namely duration of social media usage, smartphone addiction, external academic locus of control, and internal academic locus of control, explained 64% of the variance in cyberloafing. All four factors in the model showed a significant total effect on cyberloafing. External academic locus of control (β = 0.564, p < 0.05) and duration of social media usage (β = 0.535, p < 0.05) have a large total effect on cyberloafing, while smartphone addiction (β = 0.384, p < 0.05) has a moderate total effect. However, internal academic locus of control (β = -0.122, p < 0.05) has a low total effect. Except for internal academic locus of control, all other factors have significant direct effects on cyberloafing. Smartphone addiction (β = 0.384, p < 0.05) has the strongest direct effect on cyberloafing among high school students. Additionally, duration of social media usage (β = 0.274, p < 0.05) and external academic locus of control (β = 0.217, p < 0.05) have low direct effects on cyberloafing. The direct effect of internal academic locus of control on cyberloafing is not significant. However, despite its weak direct effect, internal academic locus of control has a significant small negative indirect effect on cyberloafing (β = -0.108, p < 0.05). Duration of social media usage and external academic locus of control factors have significant indirect effects on cyberloafing. External academic locus of control (β = 0.346, p < 0.05) has a moderate indirect effect, while duration of social media usage (β = 0.261, p < 0.05) has a small indirect effect on cyberloafing.

Five factors in the model, namely duration of social media usage, smartphone addiction, cyberloafing, external academic locus of control, and internal academic locus of control, explained 67% of the variance in academic procrastination. All five factors in the model showed a significant total effect on academic procrastination. External academic locus of control (β = 0.572, p < 0.05) and duration of social media usage (β = 0.531, p < 0.05) have a large total effect on academic procrastination, while smartphone addiction (β = 0.350, p < 0.05) and cyberloafing (β = 0.343, p < 0.05) have a moderate total effect. However, internal academic locus of control (β = -0.111, p < 0.05) has a small negative total effect. Except for internal academic locus of control, all other factors have significant direct effects on academic procrastination. Cyberloafing (β = 0.343, p < 0.05) has a moderate direct effect on academic procrastination, while smartphone addiction (β = 0.218, p < 0.05), duration of social media usage (β = 0.200, p < 0.05), and external academic locus of control (β = 0.159, p < 0.05) have low direct effects. Except for internal academic locus of control, the other four factors have significant indirect effects on academic procrastination. External academic locus of control (β = 0.413, p < 0.05) has a large indirect effect, duration of social media usage (β = 0.331, p < 0.05) has a moderate indirect effect, and smartphone addiction (β = 0.132, p < 0.05) and internal academic locus of control (β = -0.111, p < 0.05) have small indirect effects on academic procrastination.

7 Discussion

This study, utilizing a two-stage exploratory sequential mixed-methods design, aims to propose and test a path model that comprehensively examines the relationships between high school students' academic achievements, smartphone addiction, and associated factors. According to the qualitative findings of the study, the most common factors influencing high school students' academic achievements in relation to smartphone addiction were found to be social media usage, academic locus of control, academic procrastination, and cyberloafing. Based on these qualitative findings and relevant literature, a hypothesis model illustrating the relationships between academic achievement, smartphone addiction, associated factors have been developed. Subsequently, in the quantitative phase of the study, this model has been tested using the path analysis method. This model, which fitted well with the data, explained academic achievement with a high variance (73%). It was observed that all factors included in the model significantly influence high school students' academic achievements in terms of total effects. Additionally, although there was no direct impact on high school students' academic achievements, external academic locus of control, internal academic locus of control, and cyberloafing had indirect effects.

The most important factor explaining academic achievement in the model is identified as duration of social media usage. Findings from the qualitative phase of the study, based on interviews with students and teachers, support the notion that daily social media usage can adversely affect academic achievement. Indeed, negative and significant relationships have been found in the literature between the general use of social networks and students' academic achievements (Abu-Snieneh et al., 2020 ; Azizi et al., 2019 ; Kumcağız et al., 2019 ; Paul et al., 2012 ). Another study showed a strong and negative association between the time spent socializing on a social network service and the overall grade average (Junco, 2012 ). Moreover, excessive use of social media has been reported to reduce the time allocated for learning activities and negatively affect academic performance (Nwazor & Godwin-Maduike, 2015 ). Similarly, the quantitative findings of this research also indicated that the duration of social media usage negatively influences academic achievement. This result aligns with numerous research findings in the literature, indicating that an increase in daily social media usage may lead to a decrease in students’ academic performance levels (Giunchiglia et al., 2018 ; Kumcağız et al., 2019 ; Paul et al., 2012 ; Yorulmaz & Yorulmaz, 2020 ). Additionally, the quantitative findings of this study suggest that the duration of social media usage has a moderate indirect impact on academic achievement through smartphone addiction. In other words, excessive use of social media may indirectly and negatively affect academic achievement by causing smartphone addiction. This inference is consistent with various research findings demonstrating that social media is most commonly used through smartphones and that this usage increases smartphone addiction (Alkın et al., 2020 ; Işık & Kaptangi̇l, 2018 ; Roberts et al., 2014 ; Sözbilir & Dursun, 2018 ). Therefore, according to the model obtained in this study, controlled use of smartphones and social media is considered important for avoiding adverse effects on students' academic achievements.

According to the model, the second most important factor explaining academic achievement is external academic locus of control. Based on the analysis of opinions of student and teacher participants relying on qualitative data, it is understood that when the academic locus of control is externalized, students tend to use their smartphones more for social media and entertainment purposes. Moreover, students in this situation try to maintain these behaviors during classes, do not fulfill academic tasks, and/or postpone them, allocate less time to learning, and consequently face the risk of low academic achievement. This inference is consistent with various studies in the literature demonstrating that external academic locus of control has a negative impact on academic achievement (Abid et al., 2016 ; Bahçekapili & Karaman, 2020 ; Jain et al., 2018 ). For example, Bahçekapılı and Karaman ( 2020 ) found that external academic locus of control has a negative effect on the academic achievements of university students. However, the model indicates that external academic locus of control does not directly affect academic achievement but indirectly influences it through duration of social media usage, smartphone addiction, academic procrastination, and cyberloafing. Indeed, Lee, Chang, et al. ( 2014 ) emphasized that individuals with an external locus of control may compulsively use their smartphones due to a decrease in self-control powers. Similarly, Kuo et al. ( 2019 ) found that individuals with external control personality traits tend to use social media for longer periods. Hou et al. ( 2017 ) also showed that excessive use of a smartphone-based social network application, WeChat, is associated with higher external control focus and more online social interaction skills. According to the model, being inclined toward external academic locus of control positively affects social media usage, smartphone addiction, academic procrastination, and cybersloafing among high school students. Indeed, in a study conducted with university students (undergraduate, graduate, and doctoral) by Meena et al. ( 2021 ), a positive relationship was found between external locus of control and smartphone addiction. Therefore, especially for students inclined toward external academic locus of control, psychoeducational studies on social media and smartphone use conducted by guidance services can prevent the risk of low academic achievement.

According to the model, the third most important factor explaining academic achievement is smartphone addiction. One of the results that can be inferred from the qualitative research findings is that smartphone addiction can directly and indirectly and negatively affect academic achievement. Qualitative findings support the idea that excessive or problematic use of a smartphone can lead to concentration problems, cause academic tasks to be postponed, distract during classes when used, and hinder learning, ultimately negatively affecting academic achievement. Similarly, in a meta-analysis study including 44 different research studies, Sunday et al. ( 2021 ) concluded that smartphone addiction negatively affects students' learning and academic performance. Considering that smartphone addiction is characterized by excessive use of smartphones (Jin Jeong et al., 2020 ), Lepp et al. ( 2015 ) emphasized that it would not be surprising to associate increased mobile phone use with decreased academic achievement. In fact, it has been shown that mobile phone addiction has a negative impact on learning motivation and learning performance (Tian et al., 2021 ). Moreover, most of the teachers participating in our study think that the smartphone and social media use of high school students may be associated with addiction, students may use social media and smartphones generally in an uncontrolled manner, the time students allocate to learning may decrease due to social media and smartphone use, and this situation may negatively affect academic achievement. Indeed, the quantitative findings of this study in the path model show that smartphone addiction is one of the most significant factors affecting high school students' academic achievements negatively. Thus, the qualitative and quantitative findings of this study overlap with various research findings in the literature, indicating that negative influences of smartphone addiction on academic achievement (Baert et al., 2020 ; Chaudhury & Tripathy, 2018 ; Felisoni & Godoi, 2018 ; Khan et al., 2019 ; Ozer, 2020 ; Rathakrishnan et al., 2021 ; Samaha & Hawi, 2016 ). Therefore, it can be argued that uncontrolled use of smartphones, especially for high school students showing addictive characteristics, is an important risk factor for low academic performance (Amez & Baert, 2020 ; Domoff et al., 2020 ; Kates et al., 2018 ; Sunday et al., 2021 ). In this context, taking measures to ensure that students use their phones in a controlled manner is considered important. According to the regulations of the Ministry of National Education for secondary education institutions, the use of phones by students without the knowledge of the teacher during the lesson is not considered appropriate (MNE, 2023 ). Indeed, when considered as a tool for relaxation for students, it is deemed important to guide students in adopting a balanced approach to smartphone usage in order to reduce the prevalence of smartphone addiction (Zhang & Zeng, 2024 ).

According to the findings obtained in the study, the factor that most influences the levels of smartphone addiction among high school students is the use of social media. Qualitative findings directed towards students indicate that the majority of high school students use their smartphones more for social media purposes. Most teachers strongly believe that high school students are at risk of smartphone addiction due to excessive and uncontrolled use of smartphones for social media. The quantitative findings of this study have confirmed, in line with qualitative findings, a positive relationship between social media usage and smartphone addiction, supporting various studies in the literature that indicate social media usage contributes to increased smartphone addiction (Alkın et al., 2020 ; Basu et al., 2021 ; Mazhar et al., 2020 ; Sözbilir & Dursun, 2018 ). According to relevant literature, social media usage is considered a strong predictor of smartphone addiction (Alkın et al., 2020 ; Jeong et al., 2016 ; Yanık & Özçiçek, 2021 ). Excessive social media usage, driven by addiction, has a distracting effect on academic performance (Sharma & Behl, 2022 ). Considering the risk of social media usage turning into social media addiction and/or smartphone addiction, it can be argued that it may reduce time allocated for learning activities, lead to the postponement of academic tasks, and negatively impact academic performance (Muslikah et al., 2018 ; Nwazor & Godwin-Maduike, 2015 ). Therefore, understanding the purposes for which high school students use smartphones may be beneficial in preventing low academic achievement. For instance, parents and teachers can consult school guidance services, especially for students who use smartphones more for social media purposes. Additionally, an individual's personality traits play a significant role in analyzing the impact of social media on academic performance (Sharma & Behl, 2022 ). Although personality traits and locus of control are separate theoretical concepts, they share common features in terms of certain behavioral patterns (Filipiak & Łubianka, 2021 ). Thus, it can be suggested that students' academic locus of control, whether inclined towards internal or external, may influence their social media usage (Ye & Lin, 2015 ).

The study results also indicate that the risk of addiction and its negative impact on academic achievement increase when smartphones are used for social media purposes. This finding could also be interpreted as smartphones have a potential to enhance academic achievement when used for educational purposes rather than entertainment purposes via social media. This conclusion aligns with the study by Wang et al. ( 2023 ), which demonstrates that smartphone behavior (interpersonal communication, leisure and entertainment, information retrieval) serves as a mediating variable affecting academic performance. Thus, the kinds of smart phone applications and the ways how they are used may have an influence on the academic achievement (Salvation, 2017 ). According to the qualitative findings of this study, a small number of students believe that controlled and conscious use of smartphones for preparation or research purposes can positively impact academic achievement. Thus, the positive influences of effective smartphone use by students with high self-control, as found in the study by Troll et al. ( 2021 ), should not be overlooked in terms of academic success. Consequently, some future studies should be conducted to investigate how to use smartphones in a controlled and conscious way for educational purposes as the smartphones have a potential to motivate better academic performance when used appropriately (Lin et al., 2021 ).

According to the model, the fourth important factor influencing high school students' academic achievements is their internal academic locus of control levels. The qualitative findings related to students and teachers in the study indicate that internal academic locus of control can positively influence academic achievement. Therefore, it is believed that internal locus of control plays a significant role in the development of academic success (Kumaravelu, 2018 ). In line with this, Abid et al. ( 2016 ) found in their study that university students with internal locus of control had higher learning performances compared to students with external locus of control. Another study found a negative relationship between internal locus of control and academic procrastination (Sari & Fakhruddiana, 2019 ). Additionally, qualitative research findings suggest that high school students with an inclination towards internal academic locus of control use their smartphones more controlled and spend less time on social media. Consistent with a previous study, students with a high internal locus of control were found to have fewer online interactions, be less dependent on the internet, and take fewer risks in sharing information on social networks (Ahadzadeh et al., 2021 ). Similarly, Li et al. ( 2015 ) found that individuals with a high internal locus of control could better control inappropriate use of mobile phones at inappropriate times (such as at night or during class). In the model, in terms of total effect size, the internal academic locus of control has a low positive impact on academic achievement. While its direct effect may not be significant, there is a negative relationship between internal academic locus of control and duration of social media usage. Therefore, internal academic locus of control indirectly affects academic achievement through duration of social media usage, academic procrastination, and smartphone addiction.

The fifth most important factor influencing students' academic achievements according to the model is the level of academic procrastination. According to both qualitative findings from students and teachers in our study, there is a negative relationship between academic procrastination and academic achievement. Considering models in the literature, smartphone use has been shown to negatively affect academic achievement (Lepp et al., 2014 ), and in cases of addiction or problematic use, it leads to academic anxiety and academic procrastination (Liu et al., 2018 ; Yang et al., 2019 ). A model study by Üztemur ( 2020 ) also indicated that academic procrastination mediated the relationship between social media addiction and academic achievement, negatively affecting academic success. Similarly, another model study found that academic procrastination was a mediating variable between internet addiction and academic achievement, showing that internet addiction negatively affected academic success through academic procrastination (Kutlu & Demir, 2017 ). The quantitative findings of the study confirmed that academic procrastination negatively affects academic achievement. According to the path model, in terms of total effect size, the academic procrastination factor has a low negative impact on academic achievement, and this effect is direct and not indirect. The qualitative and quantitative findings of the study parallel various research findings in the literature, demonstrating the negative relationship between academic procrastination and academic achievement (Batool, 2020 ; Gareau et al., 2019 ; Goroshit, 2018 ; Kutlu & Demir, 2017 ; Sop, 2020 ; Üztemur, 2020 ). Therefore, interventions aimed at encouraging students to use their phones in a controlled manner through the school counseling service can eliminate obstacles to students' learning and academic success by reducing both addiction and academic procrastination risks.

The study findings indicate that smartphone addiction significantly and meaningfully affects the academic procrastination levels of high school students, both directly and indirectly. According to the results of the path analysis, smartphone addiction has a direct medium positive effect on academic procrastination. These results are consistent with various research findings in the literature, indicating that smartphone addiction positively influences academic procrastination (Kutlu & Demir, 2017 ; Liu et al., 2018 ; Özçelik Bozkurt, 2020 ; Yang et al., 2019 ). For instance, Özçelik Bozkurt ( 2020 ) found a moderate relationship between social media addiction levels and academic procrastination in a study with tourism department undergraduate students. Similarly, Kutlu and Demir ( 2017 ) showed in their study with adolescents that as internet addiction levels increased, academic procrastination behaviors also increased, and as academic procrastination increased, academic achievement decreased. However, Liu et al. ( 2018 ) concluded that the relationship between addictive behaviors and procrastination was more pronounced in males. Moreover, Yang et al. ( 2019 ) showed that students were more likely to procrastinate when they could not control their smartphone use or when they felt anxious about this situation. Şahin ( 2014 ) found in a study with students aged 14–25 that individuals who used social media (Facebook) in line with their social relationships had higher tendencies of academic procrastination than those who used it for daily purposes, and individuals who used it for academic purposes did not show tendencies of academic procrastination.

According to the model, the sixth important factor influencing high school students' academic achievements is their levels of cyberloafing. The qualitative findings of the study suggest that high school students' cyberloafing behaviors may negatively affect their academic achievements. The quantitative results of the study also confirm that cyberloafing has a low negative impact on academic achievement. In terms of total effect size according to the path model, the cyberloafing factor has a low negative impact on academic achievement. Although its direct effect may not be significant, cyberloafing indirectly affects academic achievement. These findings are consistent with a limited number of studies in the literature indicating a negative relationship between cyberloafing and academic achievement (Ravizza et al., 2017 ; Wu et al., 2018 ). However, as the qualitative findings of this study also suggest, using a mobile phone during class as a distractive behavior may not only affect the user but also negatively impact the academic performance of other students. Therefore, efforts to encourage students to use their phones in a controlled manner through school counseling services can not only reduce the risk of addiction but also minimize the risk of cyberloafing, contributing to a more productive learning environment.

According to the model, the variable that has the highest influences on high school students' cyberloafing behaviors is the external academic locus of control. The qualitative findings of the study indicate that students who associate their success or failure with factors beyond themselves tend to use social media or smartphones more during the class. Accordingly, it can be suggested that external academic locus of control may directly and indirectly positively affect cyberloafing. Indeed, the quantitative findings of this study also confirmed that the external academic locus of control may have a direct and indirect impact on the cyberloafing levels of high school students. This finding is consistent with the results of some previous studies (Blanchard & Henle, 2008 ; Yaşar, 2013 ). For example, Blanchard and Henle ( 2008 ) showed that the external academic locus of control was associated with cyberloafing behaviors in work environments. Yaşar ( 2013 ) found a positive relationship between external locus of control and cyberloafing in a study with undergraduate students. Therefore, interventions to make students more internally focused and to encourage them to use their phones in a controlled manner may not only reduce the risk of addiction but also minimize the risk of cyberloafing.

In conclusion, this study provides valuable insights into various factors influencing high school students' academic achievements. The findings highlight the importance of addressing the issues such as social media usage, internal academic locus of control, academic procrastination, smartphone addiction, and cyberloafing to enhance students' academic performance. The study suggests the need for targeted interventions and educational programs to promote healthier technology use and study habits among high school students. Additionally, efforts to encourage students to develop internal academic locus of control can positively impact their academic performance. School counseling services can play a crucial role in providing guidance to students and parents on responsible smartphone usage and effective time management. Ultimately, understanding and addressing these factors can contribute to creating a more conducive learning environment for high school students.

8 Conclusion and implication

In conclusion, according to the model developed in this study, students with a tendency toward an external academic locus of control may have a higher likelihood of experiencing various learning-related problems, including smartphone addiction, excessive use of social media, cyberloafing behaviors, and academic procrastination. This situation could pose a significant risk for low academic achievement. However, since the direct impact of the external academic locus of control on academic achievement is not significant, this risk may be higher in students who use social media more, engage in cyberloafing during classes, exhibit smartphone addiction behavior, and procrastinate academically.

Contrary to these findings, students with high scores on the internal academic locus of control seem to be more advantageous in terms of risks such as excessive social media use, smartphone addiction, cyberloafing, academic procrastination, and low academic achievement compared to students with a tendency toward an external academic locus of control. However, as the internal academic locus of control does not directly affect academic achievement, having an internal academic locus of control may not be sufficient for high academic achievement. In this context, it is believed that examining factors such as general intelligence, academic self-efficacy, effort, and learning strategies in explaining the impact of the internal academic locus of control on academic achievement would contribute to the literature.

In future research, qualitative findings can be redesigned with larger groups, including students, teachers, and parents. Additionally, similar studies can be conducted with different types of schools and different age groups. Moreover, experimental studies with groups consisting of students with high levels of smartphone addiction, cyberloafing, and academic procrastination can be conducted to explore different methods and strategies that can be used to improve academic achievement. In other words, approaches that teachers can adopt to enhance the learning motivations and academic achievements of students exhibiting cyberloafing and academic procrastination behaviors and prone to smartphone addiction can be investigated. New models can be developed regarding the relationships between teacher behaviors, parental attitudes, teaching methods used by teachers, and the relationships between students' cyberloafing, smartphone addiction, and academic procrastination. Furthermore, applications similar to the "Smartphone Addiction Management System (SAMS)" suggested by Lee, Ahn, et al. ( 2014 ) for students at high risk of smartphone addiction can be developed. Informing families, installing applications on students' phones with their consent, and assessing students can be effective in using the correct intervention technique. Additionally, seminars and psychoeducation programs can be planned by school administrations and guidance services to address students struggling with usage control. Evaluation of the effectiveness of programs may require measuring smartphone addiction, cyberloafing, academic procrastination, and academic achievement levels before and after the program.

Data availability

The data analyzed during the current study are not publicly available due to privacy reasons but can be obtained from the corresponding author upon reasonable request.

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This study was produced from the doctoral thesis prepared by the first author under the supervision of the second author.

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Kutluay, E., Karaca, F. A model proposal explaining the influence of smartphone addiction related factors on high school students’ academic success. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12947-x

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DOI : https://doi.org/10.1007/s10639-024-12947-x

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