qualitative research title stem

161+ Exciting Qualitative Research Topics For STEM Students

Are you doing Qualitative research? Looking for the best qualitative research topics for stem students? It is a most interesting and good field for research. Qualitative research allows STEM (Science, Technology, Engineering, and Mathematics) students to delve deeper into complex issues, explore human behavior, and understand the intricacies of the world around them.

In this article, we’ll provide you with an extensive list of 161+ qualitative research topics tailored to STEM students. We’ll also explore how to find and choose good qualitative research topics, and why these topics are particularly beneficial for students, including those in high school.

Also Like To Read: 171+ Brilliant Quantitative Research Topics For STEM Students

Table of Contents

What Are Qualitative Research Topics for STEM Students

Qualitative research topics for stem students are questions or issues that necessitate an in-depth exploration of people’s experiences, beliefs, and behaviors. STEM students can use this approach to investigate societal impacts, ethical dilemmas, and user experiences related to scientific advancements and innovations.

Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research delves into the ‘whys’ and ‘hows’ of a particular phenomenon.

How to Find and Choose Good Qualitative Research Topics

Selecting qualitative research topics for stem students is a crucial step in the research process. Here are some tips to help you find and choose a suitable topic:

Passion and Interest: Start by considering your personal interests and passions. What topics within STEM excite you? Research becomes more engaging when you’re genuinely interested in the subject.
Relevance: Choose qualitative research topics for stem students. Look for gaps in the existing knowledge or unanswered questions.
Literature Review: Conduct a thorough literature review to identify the latest trends and areas where qualitative research is lacking. This can guide you in selecting a topic that contributes to the field.
Feasibility: Ensure that your chosen topic is feasible within the resources and time constraints available to you. Some research topics may require extensive resources and funding.
Ethical Considerations: Be aware of ethical concerns related to your qualitative research topics for stem students, especially when dealing with human subjects or sensitive issues.

Here are the most exciting and very interesting Qualitative Research Topics For STEM Students, high school students, nursing students, college students, etc.

Biology Qualitative Research Topics

Impact of Ecosystem Restoration on Biodiversity
Ethical Considerations in Human Gene Editing
Public Perceptions of Biotechnology in Agriculture
Coping Mechanisms and Stress Responses in Marine Biologists
Cultural Perspectives on Traditional Herbal Medicine
Community Attitudes Toward Wildlife Conservation Efforts
Ethical Issues in Animal Testing and Research
Indigenous Knowledge and Ethnobotany
Psychological Well-being of Conservation Biologists
Attitudes Toward Endangered Species Protection

Chemistry Qualitative Research Topics For STEM Students

Adoption of Green Chemistry Practices in the Pharmaceutical Industry
Public Perception of Chemical Safety in Household Products
Strategies for Improving Chemistry Education
Art Conservation and Chemical Analysis
Consumer Attitudes Toward Organic Chemistry in Everyday Life
Ethical Considerations in Chemical Waste Disposal
The Role of Chemistry in Sustainable Agriculture
Perceptions of Nanomaterials and Their Applications
Chemistry-Related Career Aspirations in High School Students
Cultural Beliefs and Traditional Chemical Practices

Physics Qualitative Research Topics

Gender Bias in Physics Education and Career Progression
Philosophical Implications of Quantum Mechanics
Public Understanding of Renewable Energy Technologies
Influence of Science Fiction on Scientific Research
Perceptions of Dark Matter and Dark Energy in the Universe
Student Experiences in High School Physics Classes
Physics Outreach Programs and Their Impact on Communities
Cultural Variations in the Perception of Time and Space
Role of Physics in Environmental Conservation
Public Engagement with Science Through Astronomy Events

Engineering Qualitative Research Topics For STEM Students

Ethics in Artificial Intelligence and Robotics
Human-Centered Design in Engineering
Innovation and Sustainability in Civil Engineering
Public Perception of Self-Driving Cars
Engineering Solutions for Climate Change Mitigation
Experiences of Women in Male-Dominated Engineering Fields
Role of Engineers in Disaster Response and Recovery
Ethical Considerations in Technology Patents
Perceptions of Engineering Education and Career Prospects
Students Views on the Role of Engineers in Society

Computer Science Qualitative Research Topics

Gender Diversity in Tech Companies
Ethical Implications of AI-Powered Decision-Making
User Experience and Interface Design
Cybersecurity Awareness and Behaviors
Digital Privacy Concerns and Practices
Social Media Use and Mental Health in College Students
Gaming Culture and its Impact on Social Interactions
Student Attitudes Toward Coding and Programming
Online Learning Platforms and Student Satisfaction
Perceptions of Artificial Intelligence in Everyday Life

Mathematics Qualitative Research Topics For STEM Students

Gender Stereotypes in Mathematics Education
Cultural Variations in Problem-Solving Approaches
Perception of Math in Everyday Life
Math Anxiety and Coping Mechanisms
Historical Development of Mathematical Concepts
Attitudes Toward Mathematics Among Elementary School Students
Role of Mathematics in Solving Real-World Problems
Homeschooling Approaches to Teaching Mathematics
Effectiveness of Math Tutoring Programs
Math-Related Stereotypes in Society

Environmental Science Qualitative Research Topics

Local Communities’ Responses to Climate Change
Public Understanding of Conservation Practices
Sustainable Agriculture and Farmer Perspectives
Environmental Education and Behavior Change
Indigenous Ecological Knowledge and Biodiversity Conservation
Conservation Awareness and Behavior of Tourists
Climate Change Perceptions Among Youth
Perceptions of Water Scarcity and Resource Management
Environmental Activism and Youth Engagement
Community Responses to Environmental Disasters

Geology and Earth Sciences Qualitative Research Topics For STEM Students

Geologists’ Risk Perception and Decision-Making
Volcano Hazard Preparedness in At-Risk Communities
Public Attitudes Toward Geological Hazards
Environmental Consequences of Extractive Industries
Perceptions of Geological Time and Deep Earth Processes
Use of Geospatial Technology in Environmental Research
Role of Geology in Disaster Preparedness and Response
Geological Factors Influencing Urban Planning
Community Engagement in Geoscience Education
Climate Change Communication and Public Understanding

Astronomy and Space Science Qualitative Research Topics

The Role of Science Communication in Astronomy Education
Perceptions of Space Exploration and Colonization
UFO and Extraterrestrial Life Beliefs
Public Understanding of Black Holes and Neutron Stars
Space Tourism and Future Space Travel
Impact of Space Science Outreach Programs on Student Interest
Cultural Beliefs and Rituals Related to Celestial Events
Space Science in Indigenous Knowledge Systems
Public Engagement with Astronomical Phenomena
Space Exploration in Science Fiction and Popular Culture

Medicine and Health Sciences Qualitative Research Topics

Patient-Physician Communication and Trust
Ethical Considerations in Human Cloning and Genetic Modification
Public Attitudes Toward Vaccination
Coping Strategies for Healthcare Workers in Pandemics
Cultural Beliefs and Health Practices
Health Disparities Among Underserved Communities
Medical Decision-Making and Informed Consent
Mental Health Stigma and Help-Seeking Behavior
Wellness Practices and Health-Related Beliefs
Perceptions of Alternative and Complementary Medicine

Psychology Qualitative Research Topics

Perceptions of Body Image in Different Cultures
Workplace Stress and Coping Mechanisms
LGBTQ+ Youth Experiences and Well-Being
Cross-Cultural Differences in Parenting Styles and Outcomes
Perceptions of Psychotherapy and Counseling
Attitudes Toward Medication for Mental Health Conditions
Psychological Well-being of Older Adults
Role of Cultural and Social Factors in Psychological Well-being
Technology Use and Its Impact on Mental Health

Social Sciences Qualitative Research Topics

Political Polarization and Online Echo Chambers
Immigration and Acculturation Experiences
Educational Inequality and School Policy
Youth Engagement in Environmental Activism
Identity and Social Media in the Digital Age
Social Media and Its Influence on Political Beliefs
Family Dynamics and Conflict Resolution
Social Support and Coping Strategies in College Students
Perceptions of Cyberbullying Among Adolescents
Impact of Social Movements on Societal Change

Interesting Sociology Qualitative Research Topics For STEM Students

Perceptions of Racial Inequality and Discrimination
Aging and Quality of Life in Elderly Populations
Gender Roles and Expectations in Relationships
Online Communities and Social Support
Cultural Practices and Beliefs Related to Marriage
Family Dynamics and Coping Mechanisms
Perceptions of Community Safety and Policing
Attitudes Toward Social Welfare Programs
Influence of Media on Perceptions of Social Issues
Youth Perspectives on Education and Career Aspirations

Anthropology Qualitative Research Topics

Traditional Knowledge and Biodiversity Conservation
Cultural Variation in Parenting Practices
Indigenous Language Revitalization Efforts
Social Impacts of Tourism on Indigenous Communities
Rituals and Ceremonies in Different Cultural Contexts
Food and Identity in Cultural Practices
Traditional Healing and Healthcare Practices
Indigenous Rights and Land Conservation
Ethnographic Studies of Marginalized Communities
Cultural Practices Surrounding Death and Mourning

Economics and Business Qualitative Research Topics

Small Business Resilience in Times of Crisis
Workplace Diversity and Inclusion
Corporate Social Responsibility Perceptions
International Trade and Cultural Perceptions
Consumer Behavior and Decision-Making in E-Commerce
Business Ethics and Ethical Decision-Making
Innovation and Entrepreneurship in Startups
Perceptions of Economic Inequality and Wealth Distribution
Impact of Economic Policies on Communities
Role of Economic Education in Financial Literacy

Good Education Qualitative Research Topics For STEM Students

Homeschooling Experiences and Outcomes
Teacher Burnout and Coping Strategies
Inclusive Education and Special Needs Integration
Student Perspectives on Online Learning
High-Stakes Testing and Its Impact on Students
Multilingual Education and Bilingualism
Perceptions of Educational Technology in Classrooms
School Climate and Student Well-being
Teacher-Student Relationships and Their Effects on Learning
Cultural Diversity in Education and Inclusion

Environmental Engineering Qualitative Research Topics

Sustainable Transportation and Community Preferences
Ethical Considerations in Waste Reduction and Recycling
Public Attitudes Toward Renewable Energy Projects
Environmental Impact Assessment and Community Engagement
Sustainable Urban Planning and Neighborhood Perceptions
Water Quality and Conservation Practices in Residential Areas
Green Building Practices and User Experiences
Community Resilience in the Face of Climate Change
Role of Environmental Engineers in Disaster Preparedness

Why Qualitative Research Topics Are Good for STEM Students

Deeper Understanding: Qualitative research encourages STEM students to explore complex issues from a human perspective. This deepens their understanding of the broader impact of scientific discoveries and technological advancements.
Critical Thinking: Qualitative research fosters critical thinking skills by requiring students to analyze and interpret data, consider diverse viewpoints, and draw nuanced conclusions.
Real-World Relevance: Many qualitative research topics have real-world applications. Students can address problems, inform policy, and contribute to society by investigating issues that matter.
Interdisciplinary Learning: Qualitative research often transcends traditional STEM boundaries, allowing students to draw on insights from psychology, sociology, anthropology, and other fields.
Preparation for Future Careers: Qualitative research skills are valuable in various STEM careers, as they enable students to communicate complex ideas and understand the human and social aspects of their work.

Qualitative Research Topics for High School STEM Students

High school STEM students can benefit from qualitative research by honing their critical thinking and problem-solving skills. Here are some qualitative research topics suitable for high school students:

Perceptions of STEM Education: Investigate students’ and teachers’ perceptions of STEM education and its effectiveness.
Environmental Awareness: Examine the factors influencing high school students’ environmental awareness and eco-friendly behaviors.
Digital Learning in the Classroom: Explore the impact of technology on learning experiences and student engagement.
STEM Gender Gap: Analyze the reasons behind the gender gap in STEM fields and potential strategies for closing it.
Science Communication: Study how high school students perceive and engage with popular science communication channels, like YouTube and podcasts.
Impact of Extracurricular STEM Activities: Investigate how participation in STEM clubs and competitions influences students’ interest and performance in science and technology.

In essence, these are the best qualitative research topics for STEM students in the Philippines and are usable for other countries students too. Qualitative research topics offer STEM students a unique opportunity to explore the multifaceted aspects of their fields, develop essential skills, and contribute to meaningful discoveries. With the right topic selection, a strong research design, and ethical considerations, STEM students can easily get the best knowledge on exciting qualitative research that benefits both their career growth. So, choose a topic that resonates with your interests and get best job in your interest field.

55 Brilliant Research Topics For STEM Students

Primarily, STEM is an acronym for Science, Technology, Engineering, and Mathematics. It’s a study program that weaves all four disciplines for cross-disciplinary knowledge to solve scientific problems. STEM touches across a broad array of subjects as STEM students are required to gain mastery of four disciplines.

As a project-based discipline, STEM has different stages of learning. The program operates like other disciplines, and as such, STEM students embrace knowledge depending on their level. Since it’s a discipline centered around innovation, students undertake projects regularly. As a STEM student, your project could either be to build or write on a subject. Your first plan of action is choosing a topic if it’s written. After selecting a topic, you’ll need to determine how long a thesis statement should be .

Given that topic is essential to writing any project, this article focuses on research topics for STEM students. So, if you’re writing a STEM research paper or write my research paper , below are some of the best research topics for STEM students.

List of Research Topics For STEM Students

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Several research topics can be formulated in this field. They cut across STEM science, engineering, technology, and math. Here is a list of good research topics for STEM students.

The effectiveness of online learning over physical learning
The rise of metabolic diseases and their relationship to increased consumption
How immunotherapy can improve prognosis in Covid-19 progression

For your quantitative research in STEM, you’ll need to learn how to cite a thesis MLA for the topic you’re choosing. Below are some of the best quantitative research topics for STEM students.

A study of the effect of digital technology on millennials
A futuristic study of a world ruled by robotics
A critical evaluation of the future demand in artificial intelligence

There are several practical research topics for STEM students. However, if you’re looking for qualitative research topics for STEM students, here are topics to explore.

An exploration into how microbial factories result in the cause shortage in raw metals
An experimental study on the possibility of older-aged men passing genetic abnormalities to children
A critical evaluation of how genetics could be used to help humans live healthier and longer.

Experimental research in STEM is a scientific research methodology that uses two sets of variables. They are dependent and independent variables that are studied under experimental research. Experimental research topics in STEM look into areas of science that use data to derive results.

Below are easy experimental research topics for STEM students.

A study of nuclear fusion and fission
An evaluation of the major drawbacks of Biotechnology in the pharmaceutical industry
A study of single-cell organisms and how they’re capable of becoming an intermediary host for diseases causing bacteria

Unlike experimental research, non-experimental research lacks the interference of an independent variable. Non-experimental research instead measures variables as they naturally occur. Below are some non-experimental quantitative research topics for STEM students.

Impacts of alcohol addiction on the psychological life of humans
The popularity of depression and schizophrenia amongst the pediatric population
The impact of breastfeeding on the child’s health and development

STEM learning and knowledge grow in stages. The older students get, the more stringent requirements are for their STEM research topic. There are several capstone topics for research for STEM students .

Below are some simple quantitative research topics for stem students.

How population impacts energy-saving strategies
The application of an Excel table processor capabilities for cost calculation
A study of the essence of science as a sphere of human activity

Correlations research is research where the researcher measures two continuous variables. This is done with little or no attempt to control extraneous variables but to assess the relationship. Here are some sample research topics for STEM students to look into bearing in mind how to cite a thesis APA style for your project.

Can pancreatic gland transplantation cure diabetes?
A study of improved living conditions and obesity
An evaluation of the digital currency as a valid form of payment and its impact on banking and economy

There are several science research topics for STEM students. Below are some possible quantitative research topics for STEM students.

A study of protease inhibitor and how it operates
A study of how men’s exercise impacts DNA traits passed to children
A study of the future of commercial space flight

If you’re looking for a simple research topic, below are easy research topics for STEM students.

How can the problem of Space junk be solved?
Can meteorites change our view of the universe?
Can private space flight companies change the future of space exploration?

For your top 10 research topics for STEM students, here are interesting topics for STEM students to consider.

A comparative study of social media addiction and adverse depression
The human effect of the illegal use of formalin in milk and food preservation
An evaluation of the human impact on the biosphere and its results
A study of how fungus affects plant growth
A comparative study of antiviral drugs and vaccine
A study of the ways technology has improved medicine and life science
The effectiveness of Vitamin D among older adults for disease prevention
What is the possibility of life on other planets?
Effects of Hubble Space Telescope on the universe
A study of important trends in medicinal chemistry research

Below are possible research topics for STEM students about plants:

How do magnetic fields impact plant growth?
Do the different colors of light impact the rate of photosynthesis?
How can fertilizer extend plant life during a drought?

Below are some examples of quantitative research topics for STEM students in grade 11.

A study of how plants conduct electricity
How does water salinity affect plant growth?
A study of soil pH levels on plants

Here are some of the best qualitative research topics for STEM students in grade 12.

An evaluation of artificial gravity and how it impacts seed germination
An exploration of the steps taken to develop the Covid-19 vaccine
Personalized medicine and the wave of the future

Here are topics to consider for your STEM-related research topics for high school students.

A study of stem cell treatment
How can molecular biological research of rare genetic disorders help understand cancer?
How Covid-19 affects people with digestive problems

Below are some survey topics for qualitative research for stem students.

How does Covid-19 impact immune-compromised people?
Soil temperature and how it affects root growth
Burned soil and how it affects seed germination

Here are some descriptive research topics for STEM students in senior high.

The scientific information concept and its role in conducting scientific research
The role of mathematical statistics in scientific research
A study of the natural resources contained in oceans

Final Words About Research Topics For STEM Students

STEM topics cover areas in various scientific fields, mathematics, engineering, and technology. While it can be tasking, reducing the task starts with choosing a favorable topic. If you require external assistance in writing your STEM research, you can seek professional help from our experts.

Qualitative research in STEM : studies of equity, access, and innovation

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Contributors

Description

Creators/contributors, contents/summary.

Contents Introduction Sherry Marx
*"I am an innovator:" Quahn's Counter-narrative of Becoming in STEM
Angela Calabrese Barton, Myunghwan Shin, and LaQuahn Johnson
*"I come because I make toy.": Examining Nodes of Criticality in an Afterschool Science & Engineering (SE) Club with Refugee Youth
Edna Tan and Beverly Faircloth
* Sociocultural Analysis of Engineering Design: Latino High School Students' Funds of Knowledge and Implications for Culturally Responsive Engineering Education
Joel Alejandro Mejia
* Bruised But Not Broken: African American Women Persistence in Engineering Degree Programs in Spite of Stereotype Threat
Sherry Marx
* Examining Academic Integrity in the Postmodern: Undergraduates' Use of Solutions to Complete Textbook-based Engineering Coursework
Angela Minichiello
* Engineering Dropouts: A Qualitative Examination of Why Undergraduates Leave Engineering
Matthew Meyer and Sherry Marx
* nitacimowinis: A research story in Indigenous Science Education
* From Ambivalences toward Self-Efficacy: Bilingual Teacher Candidates' Shifting Sense of Knowing as Conocimiento with STEM
Anita Bright and G. Sue Kasun
* Examining the Non-Rational in Science Classrooms: Girls, Sustainability, and Science Education
Kim Haverkos
* Seven Types of Subitizing Activity Characterizing Young Children's Mental Activity
Beth L. MacDonald and Jesse L. M. Wilkins
* Orienting Students to One Another and to the Mathematics During Discussions
Elham Kazemi and Adrian Cunard List of Contributors Index.
(source: Nielsen Book Data)

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The Dr. Frank Y. Chuck and Dr. Bernadine Chuck Fong Family Book Fund

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QUALITATIVE RESEARCH IN STEM EDUCATION: Studies of Equity, Access and Innovation

Qualitative Research in STEM Education examines the ground-breaking potential of qualitative research methods to address issues of social justice, equity, and sustainability in STEM. A collection of empirical studies conducted by prominent STEM researchers, this book examines the experiences and challenges faced by traditionally marginalized groups in STEM, most notably minority students and women. Investigations ito these issues, as well as the high dropout rate among engineering students and issues of academic integrity in STEM, come with detailed explanations of the study methodologies used in each case. Contributors also provide personal narratives that share their perspectives on the benefits of qualitative research methodologies for the topics explored. Through a variety of qualitative methodologies, including participatory action research, indigenous research, and critical ethnography, this volume aims to reveal and remedy the inequalities within STEM education today.

Research and trends in STEM education: a systematic analysis of publicly funded projects

Yeping Li 1 ,
Ke Wang 2 ,
Yu Xiao 1 ,
Jeffrey E. Froyd 3 &
Sandra B. Nite 1

International Journal of STEM Education volume 7 , Article number: 17 ( 2020 ) Cite this article

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Taking publicly funded projects in STEM education as a special lens, we aimed to learn about research and trends in STEM education. We identified a total of 127 projects funded by the Institute of Education Sciences (IES) of the US Department of Education from 2003 to 2019. Both the number of funded projects in STEM education and their funding amounts were high, although there were considerable fluctuations over the years. The number of projects with multiple principal investigators increased over time. The project duration was typically in the range of 3–4 years, and the goals of these projects were mostly categorized as “development and innovation” or “efficacy and replication.” The majority of the 127 projects focused on individual STEM disciplines, especially mathematics. The findings, based on IES-funded projects, provided a glimpse of the research input and trends in STEM education in the USA, with possible implications for developing STEM education research in other education systems around the world.

Introduction

The rapid development of science, technology, engineering, and mathematics (STEM) education and research since the beginning of this century has benefited from strong, ongoing support from many different entities, including government agencies, professional organizations, industries, and education institutions (Li, 2014 ). Typically, studies that summarized the status of research in STEM education have used publications as the unit of their analyses (e.g., Li et al., 2019 ; Li et al., 2020 ; Margot & Kettler, 2019 ; Minichiello et al., 2018 ; Otten, Van den Heuvel-Panhuizen, & Veldhuis, 2019 ; Schreffler et al., 2019 ). Another approach, which has been used less frequently, is to study research funding. Although not all research publications were generated from funded projects and not all funded projects have been equally productive, as measured by publications, research funding and publications present two different, but related perspectives on the state of research in STEM education. Our review focuses on research funding.

Types of funding support to education research

There are different types of sources and mechanisms in place to allocate, administer, distribute, and manage funding support to education. In general, there are two sources of funding: public and private.

Public funding sources are commonly government agencies that support education program development and training, project evaluation, and research. For example, multiple state and federal agencies in the USA provide and manage funding support to education research, programs and training, including the US Department of Education (ED), the National Science Foundation (NSF), and the National Endowment for the Humanities—Division of Education Programs. Researchers seeking support from public funding sources often submit proposals that are vetted through a well-structured peer-review process. The process is competitive, and the decision to fund a project validates both its importance and alignment with the funding agency’s development agenda. Changes in the agencies’ agendas and funding priorities can reflect governmental intentions and priorities for education and research.

Private funding sources have played a very important role in supporting education programs and research with a long history. Some private funding sources in the USA can be sizeable, such as the Bill & Melinda Gates Foundation ( https://www.gatesfoundation.org ), while many also have specific foci, such as the Howard Hughes Medical Institute ( https://www.hhmi.org ) that is dedicated to advancing science through research and science education. At the same time, private funding sources often have their own development agendas, flexibility in deciding funding priorities, and specific mechanisms in making funding decisions, including how funds can be used, distributed, and managed. Indeed, private funding sources differ from public funding sources in many ways. Given many special features associated with private funding sources, including the lack of transparency, we chose to examine projects that were supported by public funding sources in this review.

Approaches to examining public research funding support

One approach to studying public research funding support to STEM education would be to examine requests-for-proposals (RFPs) issued by different government agencies. However, those RFPs tend to provide guidelines, which are not sufficiently concrete to learn about specific research that is funded. In contrast, reviewing those projects selected for funding can provide more detailed information on research activity. Figure 1 shows a flowchart of research activity and distinguishes how funded projects and publications might provide different perspectives on research. In this review, we focus on the bolded portion of the flowchart, i.e., projects funded to promote STEM education.

A general flowchart of RFPs to publications

Current review

Why focus on research funding in the usa.

Recent reviews of journal publications in STEM education have consistently revealed that scholars in the USA played a leading role in producing and promoting scholarship in STEM education, with about 75% of authorship credits for all publications in STEM education either in the International Journal of STEM Education alone from 2014 to 2018 (Li et al., 2019 ) or in 36 selected journals published from 2000 to 2018 (Li et al., 2020 ). The strong scholarship development in the USA is likely due to a research environment that is well supported and conducive to high research output. Studying public funding support for STEM education research in the USA will provide information on trends and patterns, which will be valuable both in the USA and in other countries.

The context of policy and public funding support to STEM education in the USA

The tremendous development of STEM education in the USA over the past decades has benefited greatly from both national policies and strong funding support from the US governmental agencies as well as private funding sources. Federal funding for research and development in science, mathematics, technology, and engineering-related education in the USA was restarted in the late 1980s, in the latter years of the Reagan administration, which had earlier halted funding. In recent years, the federal government has strongly supported STEM education research and development. For example, the Obama administration in the USA (The White House, 2009 ) launched the “Educate to Innovate” campaign in November 2009 for excellence in STEM education as a national priority, with over 260 million USD in financial and in-kind support commitment. The Trump administration has continued to emphasize STEM education. For example, President Trump signed a memorandum in 2017 to direct ED to spend 200 million USD per year on competitive grants promoting STEM (The White House, 2017 ). In response, ED awarded 279 million USD in STEM discretionary grants in Fiscal Year 2018 (US Department of Education, 2018 ). The Trump administration took a step further to release a report in December 2018 detailing its five-year strategic plan of boosting STEM education in the USA (The White House, 2018 ). The strategic plan envisions that “All Americans will have lifelong access to high-quality STEM education and the USA will be the global leader in STEM literacy, innovation, and employment.” (Committee on STEM Education, 2018 , p. 1). Consistently, current Secretory of Education DeVos in the Trump administration has taken STEM as a centerpiece of her comprehensive education agenda (see https://www.ed.gov/stem ). The consistency in national policies and public funding support shows that STEM education continues to be a strategic priority in the USA.

Among many federal agencies that funded STEM education programs, the ED and NSF have functioned as two primary agencies. For ED, the Institute of Education Sciences (Institute of Education Sciences (IES), n.d. , see https://ies.ed.gov/aboutus/ ) was created by the Education Sciences Reform Act of 2002 as its statistics, research, and evaluation arm. ED’s support to STEM education research has been mainly administered and managed by IES since 2003. In contrast to the focus of ED on education, NSF (see https://www.nsf.gov/about/ ) was created by Congress in 1950 to support basic research in many fields such as mathematics, computer sciences, and social sciences. Education and Human Resources is one of its seven directorates that provides important funding support to STEM education programs and research. In addition to these two federal agencies, some other federal agencies also provide funding support to STEM education programs and research from time to time.

Any study of public funding support to STEM education research in the USA would need to limit its scope, given the complexity of various public funding sources available in the system, the ambiguity associated with the meaning of STEM education across different federal agencies (Li et al., 2020 ), and the number of programs that have funded STEM education research over the years. For the purpose of this review, we have chosen to focus on the projects in STEM education funded by IES.

Research questions

Given the preceding research approach decision to focus on research projects funded by IES, we generated the following questions:

What were the number of projects, total project funding, and the average funding per project from 2003 to 2019 in STEM education research?

What were the trends of having single versus multiple principal investigator(s) in STEM education?

What were the types of awardees of the projects?

What were the participant populations in the projects?

What were the types of projects in terms of goals for program development and research in STEM education?

What were the disciplinary foci of the projects?

What research methods did projects tend to use in conducting STEM education research?

Based on the above discussion to focus on funding support from IES, we first specified the time period, and then searched the IES website to identify STEM education research projects funded by IES within the specified time period.

Time period

As discussed above, IES was established in 2002 and it did not start to administer and manage research funding support for ED until 2003. Therefore, we considered IES funded projects from 2003 to the end of 2019.

Searching and identifying IES funded projects in STEM education

Given the diverse perspectives about STEM education across different agencies and researchers (Li et al., 2020 ), we did not discuss and define the meaning of STEM education. Instead, we used the process described in the following paragraph to identify STEM education research projects funded by IES.

On the publicly accessible IES website ( https://ies.ed.gov ), one menu item is “FUNDING OPPORTUNITIES”, and there is a list of choices within this menu item. One choice is “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS.” On this web search page, we can choose “Program” under “ADDITIONAL SEARCH OPTIONS.” There are two program categories related to STEM under the option of “Program.” One is “Science, Technology, Engineering, and Mathematics (STEM) Education” under one large category of “Education Research” and the other is “Science, Technology, Engineering, and Mathematics” under another large category of “Special Education Research.” We searched for funded projects under these two program categories, and the process returned 98 funded projects in “Science, Technology, Engineering, and Mathematics (STEM) Education” under “Education Research” and 29 funded projects in “Science, Technology, Engineering, and Mathematics” under “Special Education Research,” for a total of 127 funded projects in these two programs designated for STEM education by IES Footnote 1 .

Data analysis

To address questions 1, 2, 3, and 4, we collected the following information about these projects identified using above procedure: amount of funding, years of duration, information about the PI, types of awardees that received and administered the funding (i.e., university versus those non-university including non-profit organization such as WestEd, Educational Testing Service), and projects’ foci on school level and participants. When a project’s coverage went beyond one category, the project was then coded in terms of its actual number of categories being covered. For example, we used the five categories to classify project’s participants: Pre–K, grades 1–4, grades 5–8, grades 9–12, and adult. If a funded project involved participants from Pre-school to grade 8, then we coded the project as having participants in three categories: Pre-K, grades 1–4, and grades 5–8.

To address question 5, we analyzed projects based on goal classifications from IES. IES followed the classification of research types that was produced through a joint effort between IES and NSF in 2013 (Institute of Education Sciences (IES) and National Science Foundation (NSF), 2013 ). The effort specified six types of research that provide guidance on the goals and level of funding support: foundational research, early-stage or exploratory research, design and development research, efficacy research, effectiveness research, and scale-up research. Related to these types, IES classified goals for funded projects: development and innovation, efficacy and replication, exploration, measurement, and scale-up evaluation, as described on the IES website.

To address question 6, we coded the disciplinary focus using the following five categories: mathematics, science, technology, engineering, and integrated (meaning an integration of any two or more of STEM disciplines). In some cases, we coded a project with multiple disciplinary foci into more than one category. The following are two project examples and how we coded them in terms of disciplinary foci:

The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008, https://ies.ed.gov/funding/grantsearch/details.asp?ID=601 ) was to test the efficacy of the Quantum Chemistry Tutors, a suite of computer-based cognitive tutors that are designed to give individual tutoring to high school students on 12 chemistry topics. Therefore, we coded this project as having three categories of disciplinary foci: science because it was chemistry, technology because it applied instructional technology, and integrated because it integrated two or more of STEM disciplines.

The project of “Applications of Intelligent Tutoring Systems (ITS) to Improve the Skill Levels of Students with Deficiencies in Mathematics” (2009, https://ies.ed.gov/funding/grantsearch/details.asp?ID=827 ) was coded as having three categories of disciplinary foci: mathematics, technology because it used intelligent tutoring systems, and integrated because it integrated two or more of STEM disciplines.

To address question 7, all 127 projects were coded using a classification category system developed and used in a previous study (Wang et al., 2019 ). Specifically, each funded project was coded in terms of research type (experimental, interventional, longitudinal, single case, correlational) Footnote 2 , data collection method (interview, survey, observation, researcher designed tests, standardized tests, computer data Footnote 3 ), and data analysis method (descriptive statistics, ANOVA*, general regression, HLM, IRT, SEM, others) Footnote 4 . Based on a project description, specific method(s) were identified and coded following a procedure similar to what we used in a previous study (Wang et al., 2019 ). Two researchers coded each project’s description, and the agreement between them for all 127 projects was 88.2%. When method and disciplinary focus-coding discrepancies occurred, a final decision was reached after discussion.

Results and discussion

In the following sections, we report findings as corresponding to each of the seven research questions.

Question 1: the number of projects, total funding, and the average funding per project from 2003 to 2019

Figure 2 shows the distribution of funded projects over the years in each of the two program categories, “Education Research” and “Special Education Research,” as well as combined (i.e., “STEM” for projects funded under “Education Research,” “Special STEM” for projects funded under “Special Education Research,” and “Combined” for projects funded under both “Education Research” and “Special Education Research”). As Fig. 2 shows, the number of projects increased each year up to 2007, with STEM education projects started in 2003 under “Education Research” and in 2006 under “Special Education Research.” The number of projects in STEM under “Special Education Research” was generally less than those funded under the program category of “Education Research,” especially before 2011. There are noticeable decreases in combined project counts from 2009 to 2011 and from 2012 to 2014, before the number count increased again in 2015. We did not find a consistent pattern across the years from 2003 to 2019.

The distribution of STEM education projects over the years. (Note: STEM refers to projects funded under “Education Research,” Special STEM refers to projects funded under “Special Education Research,” and “Combined” refers to projects funded under both “Education Research” and “Special Education Research.” The same annotations are used in the rest of the figures.)

A similar trend can be observed in the total funding amount for STEM education research (see Fig. 3 ). The figure shows noticeably big year-to-year swings from 2003 to 2019, with the highest funding amount of more than 33 million USD in 2007 and the lowest amount of 2,698,900 USD in 2013 from these two program categories. Although it is possible that insufficient high-quality grant proposals were available in one particular year to receive funding, the funded amount and the number of projects (Fig. 2 ) provide insights about funding trends over the time period of the review.

Annual funding totals

As there are diverse perspectives and foci about STEM education, we also wondered if STEM education research projects might be funded by IES but in program options other than those designated options of “Science, Technology, Engineering, and Mathematics (STEM) Education.” We found a total of 54 funded projects from 2007 to 2019, using the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction. Only 2 (3.7%) out of these 54 projects were in the IES designated program options of STEM education in the category of “Education Research.” Further information about these 54 projects and related discussion can be found as additional notes at the end of this review.

Results from two different approaches to searching for IES-funded projects will likely raise questions about what kinds of projects were funded in the designated program option of “Science, Technology, Engineering, and Mathematics (STEM) Education,” if only two funded projects under this option contained the acronym “STEM” in a project’s title and/or description. We shall provide further information in the following sub-sections, especially when answering question 6 related to projects’ disciplinary focus.

Figure 4 illustrates the trend of average funding amount per project each year in STEM education research from 2003 to 2019. The average funding per project varied considerably in the program category “Special Education Research,” and no STEM projects were funded in 2014 and 2017 in this category. In contrast, average funding per project was generally within the range of 1,132,738 USD in 2019 to 3,475,975 USD in 2014 for the projects in the category of “Education Research” and also for project funding in the combined category.

The trend of average funding amount per project funded each year in STEM education research

Figure 5 shows the number of projects in different funding amount categories (i.e., less than 1 million USD, 1–2 million USD, 2–3 million USD, 3 million USD or more). The majority of the 127 projects obtained funding of 1–2 million USD (77 projects, 60.6%), with 60 out of 98 projects (61.2%) under “Education Research” program and 17 out of 29 projects (58.6%) in the program category “Special Education Research.” The category with second most projects is funding of 3 million USD or more (21 projects, 16.5%), with 15 projects (15.3% of 98 projects) under “Education Research” and 6 projects (20.7% of 29 projects) under “Special Education Research.”

The number of projects in terms of total funding amount categories

Figure 6 shows the average amount of funding per project funded across these different funding amount and program categories. In general, the projects funded under “Education Research” tended to have a higher average amount than those funded under “Special Education Research,” except for those projects in the total funding amount category of “less than 1 million USD.” Considering all 127 funded projects, the average amount of funding was 1,960,826.3 USD per project.

The average amount of funding per project across different total funding amount and program categories

Figure 7 shows that the vast majority of these 127 projects were 3- or 4-year projects. In particular, 59 (46.5%) projects were funded as 4-year projects, with 46 projects (46.9%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.” This category is followed closely by 3-year projects (54 projects, 42.5%), with 41 projects (41.8%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.”

The number of projects in terms of years of project duration. (Note, 2: 2-year projects; 3: 3-year projects; 4: 4-year projects; 5: 5-year projects)

Question 2: trends of single versus multiple principal investigator(s) in STEM education

Figure 8 shows the distribution of projects over the years grouped by a single PI or multiple PIs where the program categories of “Education Research” and “Special Education Research” have been combined. The majority of projects before 2009 had a single PI, and the trend has been to have multiple PIs for STEM education research projects since 2009. The trend illustrates the increased emphases on collaboration in STEM education research, which is consistent with what we learned from a recent study of journal publications in STEM education (Li et al., 2020 ).

The distribution of projects with single versus multiple PIs over the years (combined)

Separating projects by program categories, Fig. 9 shows projects funded in the program category “Education Research.” The trends of single versus multiple PIs in Fig. 9 are similar to the trends shown in Fig. 8 for the combined programs. In addition, almost all projects in STEM education funded under this regular research program had multiple PIs since 2010.

The distribution of projects with single versus multiple PIs over the years (in “Education Research” program)

Figure 10 shows projects funded in the category “Special Education Research.” The pattern in Fig. 10 , where very few projects funded under this category had multiple PIs before 2014, is quite different from the patterns in Figs. 8 and 9 . We did not learn if single PIs were appropriate for the nature of these projects. The trend started to change in 2015 as the number of projects with multiple PIs increased and the number of projects with single PIs declined.

The distribution of projects with single versus multiple PIs over the years (in “Special Education Research” program)

Question 3: types of awardees of these projects

Besides the information about the project’s PI, the nature of the awardees can help illustrate what types of entity or organization were interested in developing and carrying out STEM education research. Figure 11 shows that the university was the main type of awardee before 2012, with 80 (63.0%) projects awarded to universities from 2003 to 2019. At the same time, non-university entities received funding support for 47 (37.0%) projects and they seem to have become even more active and successful in obtaining research funding in STEM education over the past several years. The result suggests that diverse organizations develop and conduct STEM education research, another indicator of the importance of STEM education research.

The distribution of projects funded to university versus non-university awardees over the years

Question 4: participant populations in the projects

Figure 12 indicates that the vast majority of projects were focused on student populations in preschool to grade 12. This is understandable as IES is the research funding arm of ED. Among those projects, middle school students were the participants in the most projects (70 projects), followed by student populations in elementary school (48 projects), and high school (38 projects). The adult population (including post-secondary students and teachers) was the participant group in 36 projects in a combined program count.

The number of projects in STEM education for different groups of participants (Note: Pre-K: preschool-kindergarten; G1–4: grades 1–4; G5–8: grades 5–8; G9–12: grades 9–12; adult: post-secondary students and teachers)

If we separate “Education Research” and “Special Education Research” programs, projects in the category “Special Education Research” focused on student populations in elementary and middle school most frequently, and then adult population. In contrast, projects in the category “Education Research” focused most frequently on middle school student population, followed by student populations in high school and elementary school.

Given the importance of funded research in special education Footnote 5 at IES, we considered projects focused on participants with disabilities. Figure 13 shows there were 28 projects in the category “Special Education Research” for participants with disabilities. There were also three such projects funded in the category “Education Research,” which together accounted for a total of 31 (24.4%) projects. In addition, some projects in the category “Education Research” focused on other participants, including 11 projects focused on ELL students (8.7%) projects and 37 projects focused on low SES students (29.1%).

The number of funded projects in STEM education for three special participant populations (Note: ELL: English language learners, Low SES: low social-economic status)

Figure 14 shows the trend of projects in STEM education for special participant populations. Participant populations with ELL and/or Low SES gained much attention before 2011 among these projects. Participant populations with disabilities received relatively consistent attention in projects on STEM education over the years. Research on STEM education with special participant populations is important and much needed. However, related scholarship is still in an early development stage. Interested readers can find related publications in this journal (e.g., Schreffler et al., 2019 ) and other journals (e.g., Lee, 2014 ).

The distribution of projects in STEM education for special participant populations over the years

Question 5: types of projects in terms of goals for program development and research

Figure 15 shows that “development and innovation” was the most frequently funded type of project (58 projects, 45.7%), followed by “efficacy and replication” (34 projects, 26.8%), and “measurement” (21 projects, 16.5%). The pattern is consistent across “Education Research,” “Special Education Research,” and combined. However, it should be noted that all five projects with the goal of “scale-up evaluation” were in the category “Education Research” Footnote 6 and funding for these projects were large.

The number of projects in terms of the types of goals

Examining the types of projects longitudinally, Fig. 16 shows that while “development and innovation” and “efficacy and replication” types of projects were most frequently funded in the “Education Research” program, the types of projects being funded changed longitudinally. The number of “development and innovation” projects was noticeably fewer over the past several years. In contrast, the number of “measurement” projects and “efficacy and replication” projects became more dominant. The change might reflect a shift in research development and needs.

The distribution of projects in terms of the type of goals over the years (in “Education Research” program)

Figure 17 shows the distribution of project types in the category “Special Education Research.” The pattern is different from the pattern shown in Fig. 16 . The types of “development and innovation” and “efficacy and replication” projects were also the dominant types of projects under “Special Education Research” program category in most of these years from 2007 to 2019. Projects in the type “measurement” were only observed in 2010 when that was the only type of project funded.

The distribution of projects in terms of goals over the years (in “Special Education Research” program)

Question 6: disciplinary foci of projects in developing and conducting STEM education research

Figure 18 shows that the majority of the 127 projects under such specific programs included disciplinary foci on individual STEM disciplines: mathematics in 88 projects, science in 51 projects, technology in 43 projects, and engineering in 2 projects. The tremendous attention to mathematics in these projects is a bit surprising, as mathematics was noted as being out of balance in STEM education (English, 2016 ) and also in STEM education publications (Li, 2018b , 2019 ). As noted above, each project can be classified in multiple disciplinary foci. However, of the 88 projects with a disciplinary focus on mathematics, 54 projects had mathematics as the only disciplinary focus (38 under “Education Research” program and 16 under “Special Education Research” program). We certainly hope that there will be more projects that further scholarship where mathematics is included as part of (integrated) STEM education (see Li & Schoenfeld, 2019 ).

The number of projects in terms of disciplinary focus

There were also projects with specific focus on integrated STEM education (i.e., combining any two or more disciplines of STEM), with a total of 55 (43.3%) projects in a combined program count. The limited number of projects on integrated STEM in the designated STEM funding programs further confirms the common perception that the development of integrated STEM education and research is still in its initial stage (Honey et al., 2014 ; Li, 2018a ).

In examining possible funding trends, Fig. 19 shows that mathematics projects were more frequently funded before 2012. Engineering was a rare disciplinary focus. Integrated STEM was a disciplinary focus from time to time among these projects. No other trends were observed.

The distribution of projects in terms of disciplinary focus over the years

Question 7: research types and methods that projects used

Figure 20 indicates that “interventional” (in 104 projects, 81.9%) and “experimental research” (in 89 projects, 70.1%) were the most frequently funded types of research. The percentages of projects funded under the regular education research program were similar to those funded under “Special Education Research” program, except that projects funded under “Special Education Research” tended to utilize correlational research more often.

The number of projects in terms of the type of research conducted

Research in STEM education uses diverse data collection and analysis methods; therefore, we wanted to study types of methods (Figs. 21 and 22 , respectively). Among the six types of methods used for data collection, Fig. 21 indicates that “standardized tests” and “designed tests” were the most commonly used methods for data collection, followed by “survey,” “observation,” and “interview.” The majority of projects used three quantitative methods (“standardized tests,” “researcher designed tests,” and “survey”). The finding is consistent with the finding from analysis of journal publications in STEM education (Li et al., 2020 ). Data collected through “interview” and “observation” were more likely to be analyzed using qualitative methods as part of a project’s research methodology.

The number of projects categorized by the type of data collection methods

The number of projects categorized by the type of data analysis methods

Figure 22 shows the use of seven (including others) data analysis methods among these projects. The first six methods (i.e., descriptive, ANOVA*, general regression, HLM, IRT, and SEM) as well as some methods in “others” are quantitative data analysis methods. The number of projects that used these quantitative methods is considerably larger than the number of projects that used qualitative methods (i.e., included in “others” category).

Concluding remarks

The systematic analysis of IES-funded research projects in STEM education presented an informative picture about research support for STEM education development in the USA, albeit based on only one public funding agency from 2003 to 2019. Over this 17-year span, IES funded 127 STEM education research projects (an average of over seven projects per year) in two designated STEM program categories. Although we found no discernable longitudinal funding patterns in these two program categories, both the number of funded projects in STEM education and their funding amounts were high. If we included an additional 52 projects with the acronym “STEM” funded by many other programs from 2007 to 2019 (see “ Notes ” section below), the total number of projects in STEM education research would be even higher, and the number of projects with the acronym “STEM” would also be larger. The results suggested the involvement of many researchers with diverse expertise in STEM education research was supported by a broad array of program areas in IES.

Addressing the seven questions showed several findings. Funding support for STEM education research was strong, with an average of about 2 million USD per project for a typical 3–4 year duration. Also, our analysis showed that the number of projects with multiple PIs over the years increased over the study time period, which we speculate was because STEM education research increasingly requires collaboration. STEM education research is still in early development stage, evidenced by the predominance of project goals in either “development and innovation” or “efficacy and replication” categories. We found very few projects (5 out of 127 projects, 4.0%) that were funded for “scale-up evaluation.” Finally, as shown by our analysis of project participants, IES had focused on funding projects for students in grades 1–12. Various quantitative research methods were frequently used by these projects for data collection and analyses.

These results illustrated how well STEM education research was supported through both the designated STEM education and many other programs during the study time period, which helps to explain why researchers in the USA have been so productive in producing and promoting scholarship in STEM education (Li et al., 2019 ; Li et al., 2020 ). We connected several findings from this study to findings from recent reviews of journal publications in STEM education. For example, publications in STEM education appeared in many different journals as many researchers with diverse expertise were supported to study various issues related to STEM education, STEM education publications often have co-authorship, and there is heavy use of quantitative research methods. The link between public funding and significant numbers of publications in STEM education research from US scholars offers a strong argument for the importance of providing strong funding support to research and development in STEM education in the USA and also in many other countries around the world.

The systematic analysis also revealed that STEM education, as used by IES in naming the designated programs, did not convey a clear definition or scope. In fact, we found diverse disciplinary foci in these projects. Integrated STEM was not a main focus of these designated programs in funding STEM education. Instead, many projects in these programs had clear subject content focus in individual disciplines, which is very similar to discipline-based education research (DBER, National Research Council, 2012 ). Interestingly enough, STEM education research had also been supported in many other programs of IES with diverse foci Footnote 7 , such as “Small Business Innovation Research,” “Cognition and Student Learning,” and “Postsecondary and Adult Education.” This funding reality further suggested the broad scope of issues associated with STEM education, as well as the growing need of building STEM education research as a distinct field (Li, 2018a ).

Inspired by our recent review of journal publications as research output in STEM education, this review started with an ambitious goal to study funding support as research input for STEM education. However, we had to limit the scope of the study for feasibility. We limited funding sources to one federal agency in the USA. Therefore, we did not analyze funding support from private funding sources including many private foundations and corporations. Although public funding sources have been one of the most important funding supports available for researchers to develop and expand their research work, the results of this systematic analysis suggest the importance future studies to learn more about research support and input to STEM education from other sources including other major public funding agencies, private foundations, and non-profit professional organizations.

Among these 54 funded projects containing the acronym “STEM” from 2007 to 2019, Table 1 shows that only 2 (3.7%) were in the IES designated program option of STEM education in the category of “Education Research.” Forty-nine projects were in 13 other program options in the category of “Education Research,” with surprisingly large numbers of projects under the “Small Business Innovation Research” option (17, 31.5%) and “Cognition and Student Learning” (11, 20.4%). Three of the 54 funded projects were in the program category of “Special Education Research.” To be specific, two of the three were in the program of “Small Business Innovation Research in Special Education,” and one was in the program of “Special Topic: Career and Technical Education for Students with Disabilities.”

The results suggest that many projects, focusing on various issues and questions directly associated with STEM education, were funded even when researchers applied for funding support in program options not designated as “Science, Technology, Engineering, and Mathematics (STEM) Education.” It implies that issues associated with STEM education had been generally acknowledged as important across many different program areas in education research and special education research. The funding support available in diverse program areas likely allowed numerous scholars with diverse expertise to study many different questions and publish their research in diverse journals, as we noted in the recent review of journal publications in STEM education (Li et al., 2020 ).

A previous study identified and analyzed a total of 46 IES funded projects from 2007 to 2018 (with an average of fewer than 4 projects per year) that contain the acronym “STEM” in a project’s title and/or description (Wang et al., 2019 ). Finding eight newly funded projects in 2019 suggested a growing interest in research on issues directly associated with STEM education in diverse program areas. In fact, five out of these eight newly funded projects specifically included the acronym “STEM” in the project’s title to explicitly indicate the project’s association with STEM education.

Availability of data and materials

The data and materials used and analyzed for the review are publicly available at the IES website, White House website, and other government agency websites.

In a previous study (Wang, Li, & Xiao, 2019), we used the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction, and identified and analyzed 46 funded projects from 2007 to 2018 that contain “STEM” in a project’s title and/or description after screening out unrelated key words containing “stem” such as “system”. To make comparisons when needed, we did the same search using the acronym “STEM” and found 8 more funded projects in 2019 for a total of 54 funded projects across many different program categories from 2007 to 2019.

The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008). In the project description, its subtitle shows intervention information. We coded this project as “interventional.” Then, the project also included the treatment group and the control group. We coded this project as “experimental.” Finally, this project was to test the efficacy of computer-based cognitive tutors. This was a correlational study. We thus coded it as “correlational.”

Computer data means that the project description indicated this kind of information, such as log data on students.

Descriptive means “descriptive statistics.” General regression means multiple regression, linear regression, logistical regression, except hierarchical linear regression model. ANOVA* is used here as a broad term to include analysis of variance, analysis of covariance, multivariate analysis of variance, and/or multivariate analysis of variance. Others include factor analysis, t tests, Mann-Whitney tests, and binomial tests, log data analysis, meta-analysis, constant comparative data analysis, and qualitative analysis.

Special education originally was about students with disabilities. It has broadened in scope over the years.

The number of students under Special Education was 14% of students in public schools in the USA in 2017–2018. https://nces.ed.gov/programs/coe/indicator_cgg.asp

For example, “Design Environment for Educator-Student Collaboration Allowing Real-Time Engineering-centric, STEM (DESCARTES) Exploration in Middle Grades” (2017) was funded as a 2-year project to Parametric Studios, Inc. (awardee) under the program option of “Small Business Innovation Research” (here is the link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=1922 ). “Exploring the Spatial Alignment Hypothesis in STEM Learning Environments” (2017) was funded as a 4-year project to WestEd (awardee) under the program option of “Cognition and Student Learning” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2059 ). “Enhancing Undergraduate STEM Education by Integrating Mobile Learning Technologies with Natural Language Processing” (2018) was funded as a 4-year project to Purdue University (awardee) under the program option of “Postsecondary and Adult Education” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2130 ).

Abbreviations

Analysis of variance

Discipline-based education research

Department of Education

Hierarchical linear modeling

Institute of Education Sciences

Item response theory

National Science Foundation

Pre-school–grade 12

Requests-for-proposal

Structural equation modeling

Science, technology, engineering, and mathematics

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This review was supported by a grant from the National Science Foundation (DUE-1852942). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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YL conceptualized the study and drafted the manuscript. KW contributed with data collection, coding, analyses, and manuscript reviews. YX contributed to data collection, coding, and manuscript reviews. JEF and SBN contributed to manuscript improvement through manuscript reviews and revisions. All authors read and approved the final manuscript.

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Li, Y., Wang, K., Xiao, Y. et al. Research and trends in STEM education: a systematic analysis of publicly funded projects. IJ STEM Ed 7 , 17 (2020). https://doi.org/10.1186/s40594-020-00213-8

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Revisiting critical STEM interventions: a literature review of STEM organizational learning

Norma lópez.

1 Institute for Democracy and Higher Education, Tufts University, Medford, USA

Demetri L. Morgan

2 Department of Higher Education, Loyola University Chicago, Chicago, USA

Quortne R. Hutchings

3 Department of Counseling and Higher Education, Northern Illinois University, DeKalb, USA

Kendrick Davis

4 Center for Race and Equity, University of Southern California, Los Angeles, USA

Associated Data

Not applicable.

There is inconclusive evidence on the ability of scientific research in science, technology, engineering, and mathematics (STEM) education to scale-up from one context to another and ultimately become institutionalized. The dearth of evidence draws focus on how organizations change and evolve or the process of organizational learning. We designed this systematic review of the literature to uncover to what extent and how organizational theory has been leveraged within STEM interventions or as a research tool to inform the policies and practices of STEM education organizations. Unlike previous reviews, we explicitly focused on how organizational learning informs cultural transformation toward the success of racially and ethnically underrepresented minority (URM) students in STEM. The research question was: How has organizational theory and learning informed the potential for STEM education to center the success of URM students? Our results reveal that STEM entities that did not leverage organizational theory consistently fell into either the “decision trap” identified by Langely et al. created by ignoring temporal structures or deemed the innovation threatening, as Kezar suggested. We conclude with practical recommendations for the design of STEM education interventions.

Introduction

A defining feature of the Louis Stokes Alliance for Minority Participation (LSAMP), a signature National Science Foundation (NSF) program, is contributing to the success of underrepresented minorities (URMs) in science, technology, engineering, and mathematics (STEM). LSAMP initiatives promote collaboration among institutions in a geographic region to achieve collaborative outcomes for racial and ethnic URMs in STEM (James & Singer, 2016 ). Specifically, the LSAMP program provides support to URM students in STEM by establishing mentoring and research opportunities and implementing pedagogical innovations in STEM classrooms that promote student-centered learning. The goal of the LSAMP initiative is that, eventually, the programs and knowledge produced by the alliances become institutionalized, self-perpetuating, and self-supported. To spur this outcome, LSAMP programs funded for ten consecutive years, when applying for new funds, are required to

address the institutionalization and sustainability of LSAMP-supported activities by stating the progress they have made towards sustainability. They must also detail the institutionalized components and describe any systemic changes in STEM departments or alliance institutions that have resulted from the NSF LSAMP investment. (NSF, n.d.-b, p. 7)

Additionally, LSAMP programs must promote knowledge production and dissemination. We are part of the research team on the Illinois LSAMP. This systematic review of the literature was prompted by the dearth of published literature at the intersection of STEM education research and organizational theory to illuminate the role of organizational learning in institutionalization of URM success. Furthermore, though the Urban Institute concluded that alliances should seek to replicate the LSAMP framework based on the identified characteristics (Clewell et al., 2006 ), the report authors provided little guidance on the best strategies to encourage the process of scaling-up the identified successful components of the model from one context to another, such as mentoring, undergraduate research, and professional development for faculty. Nor is there any guidance on what institutions should or could be doing to make LSAMP programs sustainable if federal funding were to cease or critical personnel was no longer affiliated with the project (i.e., the process of institutionalization).

To understand and guide organizational learning, scholars often turn to insights from organizational theory to help provide awareness of how people interact with, shape, and become influenced by the culture, norms, and policies of an entity (Bastedo, 2012 ; Birnbaum, 1989 ; Bolman & Deal, 2017 ). Popova-Nowak and Cseh ( 2015 ) defined organizational learning as “a social process of individuals participating in collective situated practices and discourses that reproduce and simultaneously expand organizational knowledge… that results in adapting to the environment” (pp. 316–317). Scholars have leveraged organizational theory to explore a diverse array of topics, including faculty socialization (Gonzales, 2018 ; Tierney, 1997 ), institutional dynamics related to equity and inclusion initiatives (Baker & Blissett, 2018 ; LePeau et al., 2016 ), and civic engagement efforts (Barnhardt, 2015 ; Morgan, 2019 ).

Building on these studies, the purpose of this systematic literature review was to interrogate how organizational theory has been leveraged within STEM interventions or as a research tool to inform the policies and practices of STEM education organizations that advance cultural transformation toward the specific success of racially and ethnically URMs in STEM. Due to the lack of research specifically focused on organizational theory related to URM STEM institutionalization efforts, we focused this systematic literature review on both STEM interventions that specifically target URM populations and STEM interventions that LSAMP is implementing to diversify the STEM pipeline (e.g., research and mentoring opportunities, curricular innovations, etc.). The guiding question for this study was: How has organizational theory and learning informed the potential for STEM education to center the success of URM students?

Literature background

The scant research at the intersection of STEM education and scale-up efforts required the inclusion of a combination of organizational literatures. Two considerations that guided this literature review were the collective nature of LSAMP programs and the assumption that innovation, once tried and tested, is beneficial in any context (Kezar, 2011 ).

Given the inherently relational nature of the LSAMP model and other STEM networks, one way to situate our study was to leverage an ecological approach to understanding organizations. A tenet within the ecological lens is that organizations change in response to their environments (Hannan & Freeman, 1977 ). Hannan and Freeman ( 1984 ) distinguished ecological changes within organizations from adaptive change processes by noting that adaptive mechanisms to change face numerous limitations, including structural inertia, historical legacies, and political constraints. Therefore, a more precise and relevant way to explain or generate change is to ascertain how an entity is positioned relative to similar entities. To this end, organizational ecology researchers invoke the concept of isomorphism to describe why organizations change in response to internal and external environmental factors (Fumasoli & Stensaker, 2013 ). Isomorphism has been broadly defined as a “constraining process that forces one unit in a population to resemble other units that face the same set of environmental conditions” (DiMaggio & Powell, 1983 , p. 149)

Kezar ( 2011 ) highlighted that many scaling-up efforts in educational organizations had not reached successful replication because the efforts "often involve a static innovation that is considered to work in different contexts, even as circumstances change over time” (p. 240). Institutionalization and scaling-up efforts may be limited because scholars and educators do not attend to the dynamic organizational features that encompass educational interventions designed to promote the success of URMs in STEM. The following section situates our study in a broader body of knowledge related to organizational change efforts and the diffusion of ideas as one avenue to conceptualize organizational change processes related to efforts to realize broader impacts within STEM education research.

Isomorphism

DiMaggio and Powell ( 1983 ) extended the concept of isomorphism to focus on institutional isomorphism, which they argued acclimates researchers to how “organizations compete not just for resources and customers [students], but for political power and institutional legitimacy, for social as well as economic fitness” (p. 150). Isomorphic change occurs in three ways: (a) coercive isomorphism, of political or administrative pressure for change; (b) mimetic isomorphism, or the pressure to change in light of ambiguous circumstances; and (c) normative isomorphism or change associated with the expansion of training and education as well as the diffusion of ideas through a network.

Many higher education scholars have leveraged this framework to study different dimensions of institutional change efforts (Garcia, 2017 ; Pusser & Marginson, 2013 ; Slaughter & Rhoades, 2004 ). Nevertheless, these studies did not establish how institutional isomorphism operates more collaboratively and synergistically. Much of the ecological and isomorphism research on institutional change presupposes a lack of resources, competition from similar entities, bureaucratic impediments, and a dearth of consensus around goals. Isomorphism researchers have clarified the need to identify and address factors in the environment that surround STEM entities in the organizational learning processes. The following section highlights organizational dynamics within a network that complement the external focus of isomorphism.

Social cognition

Over the years, scholars have explained the need to embed individual cognitive processes within organizational dynamics to account for characteristics like an entity’s organizational culture, which simultaneously shapes and is shaped by individuals in the organization (Allard-Poesi, 2005 ; Bolman & Deal, 2017 ). Given the relational nature of LSAMP programs, we looked to Akgün and colleagues (2003) 10 distinct but related factors that constitute an intra-organizational learning process:

(1) information acquisition; (2) information implementation; (3) information dissemination; (4) unlearning (i.e., discarding information); (5) thinking (i.e., manipulation of memory); (6) intelligence (i.e., ability and capability to process information); (7) improvisation (or autonomous behavior) (i.e., learning with actions or reflection); (8) sensemaking (i.e., giving meaning to information); (9) emotions; and (10) memory (i.e., information storage). (p. 844)

These features relate to Birnbaum’s ( 1988 ) foundational work in higher education governance that focused on cybernetic, or self-correcting, processes within institutions. Birnbaum’s work built on Weick’s ( 1976 ) idea that higher education institutions are unique in their organizational design in that numerous organizational cultures can be present at any given moment. Thus, to manage change and lead these organizations well, Birnbaum ( 1989 ) called for administrators to engage in organizational learning by using “multiple frames to develop richer behavioral repertoires, increase[ing] the sensitivity of institutional monitoring systems, and focus[ing] attention on important issues through systems that report data and create forums for interaction” (p. 239).

With this brief review, we tried to make evident how the isomorphism literature highlights the external environment as the main issue when seeking to understand organizational change processes within a network. However, the literature is not nuanced enough to highlight intra-organizational change dimensions, hence our focus on social cognition. Ultimately, institutions are complex social organizations, and numerous factors contribute to organizational learning. Our research will contribute to the extant literature by providing a framework that URM STEM alliances could utilize in categorizing and implementing an intentional change process. We now turn to our theoretical framework designed to help braid the isomorphic and social cognition bodies of knowledge together to help unearth the intersection between STEM education empirical studies and organizational learning.

Theoretical framework

Fiol and Lyles ( 1985 ) distinguished between two aspects of organizational learning: acquiring new awareness or knowledge and creating new systems or structures. Specifically, “organizational learning means the process of improving actions through better knowledge and understanding” (Fiol & Lyles, 1985 , p. 803). Fiol and Lyles recognized that change is not always about organizational learning but can be attributed to “defensive adjustment” (p. 805). Specifically, defensive adjustment refers to not understanding causal relationships. Therefore, our theoretical framework draws on Langley et al.’s ( 2013 ) categorization of change studies of organizations, which characterizes new insights, and Kezar’s ( 2011 ) three mechanisms for scaling-up outcomes from NSF-funded work, which illustrates systems or structures. In other words, Langley and associates work allowed us to focus the analysis on the dynamic nature of and motivation for change. In contrast, Kezar’s research drew our attention to the systems and procedures needed for that change.

Langley’s categorizations

Langley et al. ( 2013 ) reviewed responses to a call for papers focused on addressing questions “about how and why things emerge, develop, grow, or terminate over time” (p. 1). Of relevance to us, they intently focused on temporality as a way to operationalize the environmental realities organizations face as they change. Put in their terms:

Knowing that organizational practice B is generally more effective than organizational practice A reveals almost nothing about how to move from A to B. Moreover, depending on the nature of the practices concerned and the context of their application, it could be that the very process of moving between A and B itself engages resources, political dynamics, and organizational upheaval that could place the original evidence supporting the need for change in an entirely different light. (Langley et al., 2013 , p. 4)

The first concept is the ontological stance one takes in unpacking change processes. Langley et al. argued that change is either conceptualized as a successive progression with an outcome (e.g., You do X until you achieve Y) or a never-ending process (e.g., You must keep doing X while moving closer to realizing Y). The latter focuses on changes in processes, whereas the former focuses on the change in things. As a result, trying to distill the ontological underpinnings of the studies is critical in conceptualizing the ecosystem of organizational theory and STEM. The second concept is the idea of tensions or paradoxes in change processes rather than change described as a cycle or linear. Focusing on how entities wrestle with obstacles helps highlight potentially transferable aspects to other contexts. Finally, Langley et al. described the notion of stability in terms that are dynamic to account for the “active work [that] is required to maintain practices, organizations, and institutions” (p. 10). This claim highlights the importance of studies to address the aftermath of change and how entities can maintain new realities while evolving.

Kezar’s mechanisms

Regarding the mechanisms for scaling-up change efforts in an organization, Kezar ( 2011 ) first highlighted nine persistent critiques of scale-up efforts from the literature: incentives, implementation context/flexibility, depth, ownership, underlying norms, sustainability, spread, static nature, and motivation. Kezar highlighted deliberation, networks, and external support and incentives as the main levers to facilitate lasting change or operationalize institutionalized change. Deliberation refers to “a process whereby people come to an understanding and learn” (Kezar, 2011 , p. 242). Deliberation draws attention to an interactive process rooted in dialogue and the affirmation of one’s voice in the change process. On the other hand, networks draw attention to the flow of information that spurs adaptation, as “networks connect people to others with similar ideas and also provide change agents with the information needed to help move the change process along” (Kezar, 2011 , p. 242). Finally, external supports and incentives refer to “the funding, awards, and recognition necessary in order to help sustain change agents in the face of entropy and even negative dynamics” (Kezar, 2011 , p. 243). Kezar’s three mechanisms are all predicated on an organizational learning perspective of change and draw attention to specific dimensions of how individuals within an organization position themselves to approach their work.

Six dimensions of organizational change in STEM

Building on the reviewed literature and grounded in the two frameworks presented above, our operating proposition is that studies must address six dimensions of organizational change. Table Table1 1 provides an overview of the dimensions and main questions we leveraged when operationalizing the framework.

Organizational change in STEM

Combining these frameworks highlights the knowledge and structures needed for scaling-up efforts and exposes tensions within each framework that can lead to failure to change. Langley et al.’s ( 2013 ) conceptualization emphasized the importance of time and points to how ignoring temporal structures, for example, “how decisions that looked good at one time turn catastrophic at another” (p.4), can cause innovations to falter or fail. Kezar ( 2011 ) further notes that not only are innovations “inherently context based” (p. 239), but they also have to adapt to the innovation to reflect their context; without these measures, innovations that are externally introduced are perceived as threats to internal interests and resources. The figure below captures the dynamic nature of our framework to better orient the reader to the harmony and tensions created by the combination (Fig. 1 ).

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Dimensions of organizational change

Research design

Tranfield et al. ( 2003 ) noted systematic reviews of literature (SRLs) differ from traditional narrative reviews “by adopting a replicable, scientific and transparent process... by providing an audit trail of the reviewer’s decisions, procedures and conclusions guidelines” (p. 209). Specifically, we leveraged Rew’s ( 2011 ) 13-step process for clarity and concern translating research insights into practice. The first two steps in the process are identifying a research question to be answered and stating the purpose of the review, both of which we did in the introduction.

Setting up the SRL

Inclusion criterion.

The third step entails stringent and clearly defined inclusion criteria based on the research question (Rew, 2011 ). Using the collective insight of members of the research team (Norton, 2008 ), we focused database construction around articles that explicitly dealt with concepts of sustainability of educational interventions or taking educational interventions to scale in STEM––meaning how educational interventions go from one organizational context to another. By educational interventions, we mean initiatives identified in the Urban Institute report (Clewell et al., 2006 ), such as mentoring, undergraduate research, internships, and academic advising. This approach builds on but is different from Henderson et al.’s ( 2011 ) review of change efforts within a unit or program. Finally, given the research question, we narrowly focused on empirical studies, meaning we only included studies that had articulated research questions, study designs, and findings in the final analysis.

Search terms

Step 4 calls for demarcating search terms (Rew, 2011 ). We used STEM in different configurations accompanied by other concepts such as “organizational theory”, “curricular reform”, “academic advising”, and “research.” We used these terms as LSAMP's innovations seek to achieve URM STEM success through targeted programs focused on curricular reform, creating research opportunities, and providing advising through this additional resource. We also used terms such as “critical” and “bias” to capture articles focused on people of color in STEM.

Identifying databases

Step 5 calls for identifying the appropriate databases to search within. Following other higher education SRLs (Harper, 2012 ; Mitchell et al., 2014 ; Renn, 2010 ), we decided to focus on STEM education and higher education research journals (see Table Table2 2 for a list). We delimited the search timeframe in these repositories to articles published since 1970 when calls for broadening participation in STEM commenced (James & Singer, 2016 ).

List of target journals

Data collection in an SRL

Steps 6–9 relate to searching and include specifying and reviewing the search outcomes to match the inclusion/exclusion criteria, systematically extracting data, and determining the quality of the reviewed studies. A research team member was assigned a series of journals to review. After setting the search parameters to match the inclusion/exclusion criteria, a team member read the title and abstract of any study that came up. If it was deemed worthy of inclusion in the database, the following pertinent study information was captured in an online form:

Title of Journal
Name of Article
Year Published (1970–Present)
What are the study’s research questions or hypothesis?
What is the study’s approach to data analysis?
What is the focus of the article? (Type of STEM Educational Intervention)
What is the unit of analysis? (Who is the study about?)
o If yes, what organizational theory is mentioned?

Using this process, we captured 24 manuscripts. We also reviewed Henderson et al.’s ( 2011 ) publicly available database from their literature review to see if we missed any studies that we could include but perhaps were not in our initially identified journals. We identified three additional studies from their database, added the journals to our list, and reviewed the journals to ensure no new studies fell outside the 2008 timeframe of the Henderson et al. database. In sum, 27 manuscripts met our inclusion criteria and were relevant to our research question. In step 10, we compiled critical information and the findings from each study into a table to prepare for analysis (Rew, 2011 ).

Analytical plan

The 11th consideration is a straightforward procedure for analysis (Rew, 2011 ). Phase 1 of our analysis included dividing the studies between two team members and descriptively categorizing the studies into groups based on the methodological approach, level of analysis (student, faculty, or institution), and whether an explicit organizational theory guided the work. The second author reviewed each of the categories. In the few cases where there was a discrepancy in the study’s categorization, the research team reached a consensus on how to code the study (Tracy, 2010 ).

Phase 2 of the analysis leveraged our dimensions of organizational change in the STEM framework (Table (Table1) 1 ) as deductive codes (Saldaña, 2016 ) to explore how and to what extent the literature connects to these dimensions. In this phase, two team members reviewed each study again, and portions of the study were assigned codes based on the six dimensions. The first three authors then met to engage in a constant comparison discussion to explore nuances that emerged within each dimension connected to examples from the research articles (Saldaña, 2016 ).

In Phase 3, the research team distilled themes from the emerging nuances and crafted descriptive summaries of how the different studies engaged the dimensions of the theoretical framework.

Limitations and trustworthiness

The 12th step of Rew’s ( 2011 ) SRL acknowledges any inherent limitations and biases in the process.

Source limitations

A significant point of caution is that STEM studies exist in various venues and formats that we did not include in the corpus of research journals. Therefore, some studies may be relevant to the research question that we did not assess. Future researchers should expand the venues and types of formats included in the corpus to include books, monographs, reports, and website resources.

Theory limitations

Another limitation is that we focused narrowly on two related aspects of organizational learning, scaling-up interventions and making interventions institutionalized, to the exclusion of the entire change process. As a result, our analyses missed important precursors and more person-centric (e.g., identity-related) factors that are critical in both social cognition and isomorphic processes.

Identifying and addressing bias

In terms of biases, we followed suggestions in making plain the seen, unseen, and to the best extent, unforeseen issues in our collective approach to the topic (Milner, 2007 ). Our research team is diverse across race (e.g., Black and Latinx), ethnicity (e.g., Mexican), gender identity (e.g., cis-males and cis-female), sexuality (e.g., Queer), positioning in and outside the academy (e.g., faculty member, graduate students, non-profit think tank), and affiliation with the STEM community (e.g., former engineer, former STEM academic advisor, STEM mentor). These multiple vantage points meant we interacted with and coded the studies in our database differently. We resolved disagreements through critical inquiry dialogues among the team members participating in the data analysis (Ravitch & Carl, 2016 ). Nonetheless, our positionality and social locations informed our decisions throughout the study. Consequently, though we made evident the steps we took to conduct the SRL, researchers with different identities may reach different conclusions.

Enhancing credibility

To strengthen the study's trustworthiness, we addressed different aspects of Tracy’s ( 2010 ) “big-tent” criteria for excellent qualitative research. To enhance the credibility of the research, the fourth author reviewed, probed, and critiqued the data analysis and presentation of the findings to support our effort to “crystalize” the present understanding of organizational learning in STEM education. Tracy noted that the goal of crystallization is to “open up a more complex, in-depth, but still thoroughly partial, understanding of the issue” (p. 844). We enhanced sincerity through our transparent account of how our theoretical framework informed our research design and how we sought to carry out the research design with authenticity by remaining open to how our narratives informed our analysis and interpretation of the data. Finally, we sought resonance by (re)presenting the results to highlight transferable findings in future research and implications for policy and practice––aided by thick descriptions of themes with concrete and representative examples from the studies in the database (Tracy, 2010 ).

We first present the descriptive results of our SRL to provide a sense of the types of studies in the database. Table Table3 3 summarizes the databases' studies by their approach to research and presents a representative study from each category. Research approaches included qualitative research, quantitative research, literature/discourse analysis, and mixed/multiple methods. Table Table4 4 highlights the unit of analysis and research questions and leverages a different set of illustrative studies. Units of analysis included studies concerned with the entire field of STEM education, the college/university as an entity, programs/departments, or students. Table Table5 5 showcases categories that focused on different types of explicit organizational theories used in the studies in the database.

Research design examples

Unit of analysis and research question examples

Theory families examples

Nuancing organizational learning theory in STEM

To present the results of the second phase of analysis, we highlight an overarching theme and textual evidence for each dimension of organizational change identified from the theoretical framework.

Ontological stance

The first dimension focused on whether the study articulated an intentional ontological stance on change. The overarching theme in the articles was that change is vital and desirable in terms of realizing STEM educational interventions. In other words, change is invoked and called for because the status quo does not realize intended goals or outcomes. Studies fell into two categories in terms of how they operationalized this sentiment. One group displayed some gradation in how change is articulated within a process, from explicitly stated (Kezar et al., 2015 ) to implied based on contextual information from other parts of the study (Su & Bozeman, 2016 ). For instance, Su and Bozeman ( 2016 ) focused on enhancing the representation of female and minority faculty members through structural changes. They acknowledged that “self-assessment is at least an important early step toward more comprehensive changes in departments” (p. 1004). This implicit approach contrasts with explicit and necessary components of the change process outlined in other studies.

Moreover, other studies simply highlighted changing elements without narrating a change process (Ong et al., 2011 ). In their synthesis of research on factors that contribute to the persistence and success of women of color in STEM fields, Ong et al. ( 2011 ) called for transformative and “cultural changes that would improve overall faculty support for and increase the enrollment and retention of minority women” (p. 195). They identified several factors influencing their call for diversifying STEM, including globalization and representation (i.e., external forces), but not a process for this change. However, they recognized the salience “to individual colleges and universities… that support the need to address STEM pedagogy and curriculum for diverse populations as well as research on the relationship between pedagogical changes and cognitive outcomes for women of color” (Ong et al., 2011 , p. 198). Ong et al. note the significance of improving the outcomes for women of color in STEM without outlining how to arrive at that desired outcome.

Tensions and paradoxes

The second dimension identifies tensions or paradoxical experiences in change processes. Given the nature of paradoxes, it may not be surprising that the dominant theme for this dimension was inconsistency. The tensions or paradoxes identified varied between studies. Most studies did identify tensions/paradoxes in the change process. However, the change was not always nuanced as to what it meant for different stakeholders (Carlisle & Weaver, 2018 ). Some articles named the role of higher education environments and tensions related to the environment (Rodriguez et al., 2017 ). Others specified tensions in the research itself versus tensions in the change process (Armstrong & Jovanovic, 2017 ). The change appeared to bring about a protective nature in wanting to retain previous practices or possession of resources and support, which were concerns specifically emphasized by Kezar ( 2011 ). Colbeck ( 2002 ) acknowledged that “this planning process is likely to involve considerable conflict as different groups maneuver to ensure their interests are represented” (p. 398). Finally, internal struggles and conflicts often related to infrastructure (Kezar et al., 2015 ; Lounsbury, 2001 ). In particular, bottom-up change often had considerable problems with alignment, incentives, involvement, and resources (Kezar et al., 2015 ).

Sustainable change

The third dimension addresses the stability of the change process over time. In examining this dimension, studies described the change as either time-bound or cyclical. Time-bound studies identified a specific period wherein change activities took place, and then the change process came to a resolution. For example, Carlisle and Weaver ( 2018 ) described how STEM education “centers [SECs] engage institutions and departments in processes that foster change in undergraduate STEM education, which, if sustained, could lead to the adaptation of traditional norms” (p. 3). This approach implied the changes are not cyclical but end once the centers are a part of institutional norms. Additionally, the purpose of SECs was described as enhancing teaching and learning to broaden participation. In seeking to establish SECs, institutions presumed that establishing centers would provide “a home, as well as resources, for previously funded successful STEM programs and initiatives, thereby contributing to their continuation” (Carlisle & Weaver, 2018 , p. 14). This statement again alludes to protecting resources and interests.

The studies that described change as cyclical highlighted adaptive structures to different levels and created the impetus for on-going change. Gehrke and Kezar ( 2019 ) suggested that this process should “nurture a regular rhythm for the community, which ensures a continuous cycle of events and involvement opportunities so members can anticipate what is to come through their regular involvement” (p. 849). Kezar and Holcolmbe ( 2019 ) pointed out that sustainable change is influenced by multiple levels of change occurring.

Stakeholder engagement

The fourth dimension reflects that the change process involves deliberation among stakeholders. The primary theme was that the deliberation process is as much about who is involved as it is about where it happens and how it unfolds. In researching STEM faculty communities of practice, Gehrke and Kezar ( 2017 ) were explicitly interested in “focusing on organizationally related outcomes such as department and institutional change” and identifying “the ways in which individual faculty involvement in these communities is related to localized efforts at STEM reform and can thus be leveraged to scale-up reform efforts” (p. 806). Institutional type and structural distinctions also require nuance in the change process deliberation. For example, structural differences might limit collective work (Kezar & Holcombe, 2019 ) due to timing in collaboration and institutional type contributing to competition for resources.

Reinholz and Apkarian ( 2018 ) described “collective goals that attend to, and include, individual goals and concerns”, and a “shared vision for the department can help shape the direction of future change initiatives to align with the needs of individual members as well as build coherence among those goals and ideals” (p. 4). Though some might feel limited in doing collective work, others are deliberately working toward a shared vision. Armstrong and Jovanovic ( 2017 ) cited structures or venues that bring URM women together and empower them to promote “community structures” versus deliberation.

Network expansion

The fifth dimension explores whether the change process involves expanding the stakeholder’s social network. This theme indicates that the change process is tied to helping stakeholders tap into resources beyond their immediate locus of control. Our findings show this can be prompted in response to external isomorphic pressures or encouraged by what the expanded network could do to support change. The expansion is often related to connections with funding agencies, people with specific expertise, and professional development opportunities. Carlisle and Weaver ( 2018 ) ascertained that “a network of partners contributed to STEM education centers’ unique ability to expand institutional capacity” (p. 12). This building of partnerships “consisted of (1) connecting faculty with similar and complementary interests, (2) connecting faculty to available resources, as well as (3) connecting upper administrators to faculty efforts” (p. 12). External agencies identified were related to funding agencies, consultants for their expertise, and professional development opportunities (Gehrke & Kezar, 2017 ; Henderson et al., 2011 ). Focusing on external pressure to expand networks comes from isomorphic influences and could relate to intentional efforts versus responding to contextual realities (Su & Bozeman, 2016 ). Kezar and Holcombe ( 2019 ) highlighted what building networks within the institution could do to support the change effort, particularly in terms of espoused versus enacted efforts.

Timely access to support and incentives

The sixth and final dimension considers whether the articles narrated timely access to support or incentives. The theme of this dimension focused on structural incentives versus other incentives. Reinholz and Apkarian ( 2018 ) indicated that “the inclusion of incentives and rewards for participation in the DAT [Department Action Team] and coordination system as part of the process is aligned with the structural frame” (p. 7). Their structural frame recognizes a culture that includes structures, symbols, power, and people and acknowledges the pressure to maintain power and resources. Rewards and incentives are related to the institutional type and setting and connect to how well-supported faculty and staff feel (Gehrke & Kezar, 2019 ). The role of stakeholders can help continue to support student success holistically by providing interpersonal support between stakeholders (Rodriguez et al., 2017 ). It is unclear how timely the support/incentives were within the studies and assumes that support/incentives are ever-present. For example, Carlisle and Weaver ( 2018 ) write, “through their scholarship, [STEM education centers] contribute to the knowledge base and provide funding, which adds resources and incentives for the implementation of [evidence-based instructional practices] and educational research” (p. 1).

Much of the research within STEM education focuses on student-level insights (Harper, 2010 ; Sax et al., 2017 ) rather than organizational or macro-level dynamics (Bastedo, 2012 ). In response to our research question, we contend that STEM entities that did not leverage organizational theory and consistently fell into either the “decision trap” identified by Langley et al. ( 2013 ) created by ignoring temporal structures or deemed the innovation as threatening as Kezar ( 2011 ) suggested. Although not all the innovations focused on URM STEM success, they did attend to innovations that LSAMP is engaging. Hence, the results of our SRL presents two relevant takeaways that complement and challenge the existing knowledge related to the process of scaling an intervention from one context to another and making change sustainable via the institutionalization of structures and supports.

Can mimetic isomorphism go beyond creating pressure?

A tension we identified in the theoretical framework was that we might be working with “faulty assumptions of change/innovation” (Kezar, 2011 , p. 236). Kezar argued that some “practices or ideas that may be perceived as threatening to a community” and, therefore, mutual adaptation is more common (p. 242). Mutual adaption is described as a process whereby an external group or force that views an innovation as beneficial to students persuades and encourages internal groups who may see this innovation as a threat. The findings demonstrate that broadening the impact of innovations that help URM students achieve success in STEM can be viewed as a threat to resources and power within higher education. Creating inclusive practices for STEM URM students has undoubtedly been an external force, often without enough internal champions within various institutions (Ong et al., 2011 ).

Furthermore, evoking the literature on mimetic isomorphism, the call for a more diverse STEM pipeline has helped to make URM STEM success a priority. Yet ironically, URM STEM success has been so elusive (Ong et al., 2011 ) that it may allow universities to avoid making real investments and progress in this area. In other words, universities want to imitate their peers in innovating toward URM STEM success. However, if no one achieves this success, there is no real pressure to achieve genuine progress. The work of broadening impacts, specifically concerning URM STEM success, becomes about appearing to scale-up innovations rather than effectively institutionalizing innovative best practices. An interest in the appearance of, rather than a real commitment to innovation, is primarily due to considering innovations a threat to power and resources that could also be influenced by timely decision-making.

Can organizational frameworks deliver meaningful change in STEM?

Half of the studies that used organizational frameworks focused on organizational learning/theories of change. The other half focused on neo-institutional theories that are more broadly concerned with environmental dynamics. As our second phase of analysis highlighted, some representative studies (Carlisle & Weaver, 2018 ; Gehrke & Kezar, 2017 ) attended to both internal and external dynamics of scaling-up an intervention and institutionalized it. This emergent issue highlights the extent to which organizational frameworks help to address whether change efforts are sustainable over time. It is difficult to say definitively since studies in our sample were not designed to answer the question of longevity. The commonality is that change was typically going well at the moment of data collection and journal submission. However, it is much more challenging to determine whether that same success was realized 1 year, 5 years, or 10 years beyond the data collection period and publication time frame.

This raises two additional questions on the utility of organizational frameworks. First, is change meaningful or consequential if it exists for a moment but dissipates over time? Another way of asking the question is how lasting must a change be for it to be meaningful and to what extent does an organizational framework account for the temporal nature of change? The second consideration is how embedded within the organizational framework are insights that prompt reflection or re-engagement with topics after time has passed? As noted, we did not readily identify organizational frameworks or studies that dealt with this issue well beyond Kezar’s ( 2011 ) assertion that sustainability of interventions matter.

Consequently, our framework draws attention to the need for stakeholders to reconsider these dimensions in the design and implementation of their change efforts. Hence, we translate these insights into suggestions for STEM organizations within networks that are charged with scaling their innovations and making them sustainable.

Implications for practice and future research

We distinguish between adaptation and learning in our framework because adaptation refers to defensively changing rather than learning how to apply knowledge and awareness “beyond the immediate event” (Fiol & Lyles, 1985 , p. 805). Adapting is not necessarily excluded from organizational learning but is lower-level learning. Fiol and Lyles ( 1985 ) contended that higher-level learning “aims at adjusting overall rules and norms rather than specific activities or behaviors” (p. 808). More telling is that lower-level organizational learning is contained within specific areas of an organization, adjusts rules but continues to work within them, and prefers the routine of known and controlled environments.

Unfortunately, STEM initiatives for URMs often function precisely this way. For example, Gomez et al. ( 2021 ) researched STEM program directors and described two types of leaders: grassroots leaders and institutional agents. The challenges they identified for each were related to lower-level organizational learning. Grassroots leaders lacked the power and authority to change structures, policies, and resources. Nevertheless, institutional agents also operated within known and controlled environments and often sought to change student behavior rather than their institutions’ rules and norms. Ultimately, they recommended that institutional agents who have the power and authority to change structures become transformation leaders. Doing so requires understanding how their behavior contributes to lower-level rather than higher-level organizational learning. Higher-level organizational learning requires changing mission and direction rather than simply behaviors and rules. Further research could apply lower and higher-level organizational learning to our framework.

The realization that lower-level learning may be acutely linked to URM STEM programs is not unexpected, given what Ray ( 2019 ) called the racialization of organizations. Ray asserted that segregation and hierarchy within organizations shape agency, which means there is a disproportionate allocation of resources, specifically for people with minoritized identities. Conversely, Whiteness becomes a “credential providing access to organizational resources, legitimizing work hierarchies, and expanding White agency” (p. 41). This leads to organizations disassociating from even official commitments to equity and inclusion (Ray, 2019 ). Decoupling from commitments to changing structures, norms, and missions is crucial to organizational higher learning. Leaders and stakeholders should examine how our theoretical framework can be applied in specific ways to consider how racialized organizations influence timely decisions or threats to power. For example, LSAMP alliances could ask if they explicitly understand the process of change and how race influences that process?

Given the evolving landscape of higher education due to the COVID-19 pandemic, intentional steps that safeguard the STEM ecosystem for URMs (Lord et al., 2019 ) and access to external resources to support research are critical (Bozeman & Youtie, 2017 ). We contend that now more than ever, an organizing theory, such as the dimensions of our theoretical framework, is necessary to bring added coherency and credibility to efforts that attend to the needs of marginalized URM students, faculty, and administration who will be differentially impacted by the crisis (Gonzales & Griffin, 2020 ). Further research and practice are needed to critically, intentionally, and continually examine how this global pandemic can limit how individuals and collective entities engage in organizational learning and change within higher education institutions. By leveraging our key considerations informed by research on this topic to date, we envision a new line of inquiry and practice can be generated to guide how colleges and universities function under this difficult, complex, and challenging time in our history.

Acknowledgements

We would like to also acknowledge the editing services of Amy Gralewski.

Author contributions

DM, NL, and KD conceptualized the project. DM and NL created the analysis scheme. NL and QH collected, organized, and analyze the study reviews. All authors participated in identifying findings. NL, DM, and QH wrote an initial draft of the manuscript. KD provided feedback and editing. NL revised the manuscript for submission. All authors approved the manuscript for submission. All authors read and approved the final manuscript.

The authors disclose receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by the National Science Foundation Grant [#1911341].

Availability of data and materials

Declarations.

We have no competing interests to disclose.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

200 Hot And Innovative Qualitative Research Topics for STEM Students

Explore intriguing qualitative research topics for STEM students, delving beyond equations and experiments into the human side of science, technology, engineering, and mathematics.

Hey STEM buddies! Ready for some cool research talk? We’re diving into why qualitative research is a blast for us STEM students and how we can pick the coolest topics.

So, grab a seat, and let’s roll into the fun world of qualitative research topics for STEM students. Ready? Let’s do this!

Importance of Qualitative Research for STEM Students

Check out the importance of qualitative research for STEM students:-

Getting the Full Picture

Qualitative research is like putting on special glasses for STEM students. It helps us see beyond numbers and dive into the real-world stuff – social vibes, cultures, and human stories. This way, we cook up solutions that cover all the bases.

Thinking Like a Detective

Qualitative research makes us STEM detectives, training our brains to question everything.

We’re not just crunching numbers; we’re digging into meanings, facing our own biases, and looking at problems from all angles. It’s like a mental gym for smart thinking.

Letting Ideas Fly

Imagine qualitative research as a brainstorming session. It lets us fly our creativity flags high.

We connect the dots, discover hidden factors, and come up with fresh ideas to crack problems wide open.

Talking the Talk

Ever wanted to be a smooth talker? Qualitative research gives us the chance. We learn to chat with people, run focus groups, and observe like pros.

These are not just skills; they’re like secret communication weapons for us STEM folks.

Learning from the Social Side

Step into the world of social sciences! Qualitative research takes us on a tour, showing how anthropologists and sociologists do their thing.

It’s like having a backstage pass to see different methods in action, making us research rockstars.

Rooting Research in Reality

No more living in a research bubble! Qualitative research plants our STEM studies firmly in real life.

We study real people in their real habitats, making sure our solutions aren’t just sci-fi dreams but actual answers to real-world needs.

Jack-of-All-Trades

Picture us as STEM superheroes. With qualitative research in our tool belt, we’re not just one-trick ponies.

We can dive into different fields, mix it up, and take on jobs that need a bit of science and a bit of heart.

In a nutshell, qualitative research is our cool sidekick, making STEM studies more exciting.

It’s about seeing the big picture, thinking sharp, being creative, talking the talk, learning from the social pros, keeping it real, and being versatile.

It’s the secret sauce that turns us into well-rounded STEM champs!

How do you choose a research topic in stem?

Check out how to choose a research topic in STEM:-

Follow Your Heart

Pick a topic that gets you excited! When you care about what you’re researching, it makes the whole process way more interesting.

Explore Your World

Look around where you are! Your community or school might have cool topics hiding in plain sight. Local stuff tends to give you juicier research material.

Keep It Real

Choose a topic you can actually dive into. Make sure you can easily talk to people or check out places for your research without too much hassle.

Play to Your Strengths

Stick to what you’re good at! Pick a topic that matches your skills. This way, you can show off your best work and get awesome results.

Fill in the Blanks

Find the missing pieces in what’s already out there. What questions haven’t been answered yet? Focusing on those gaps makes your research super important.

Stay Important

Make sure your topic matters! Whether it’s to your science world, the big wide world, or folks who really need help, aim for research that can make a difference.

Be You-nique

Put your own spin on things! Make your research stand out by bringing in your fresh ideas or trying a cool new way of looking at stuff.

Connect the Dots

Think big! Choose a topic that lets you link things from different STEM areas. This way, you get a big-picture view and some super cool insights.

Start Small

Don’t go too crazy at first! Keep your topic nice and focused so you can really dig in and find some awesome stuff without getting overwhelmed.

Picking a rad qualitative research topic is all about going with what you love, keeping it down-to-earth, and making a real impact.

Find something that’s your jam, has a purpose, and is doable, and you’ll rock it!

200 Qualitative Research Topics for STEM Students

Check out 200 qualitative research topics in STEM students:-

Attitudes towards genetic engineering
Ethical implications of cloning
Biodiversity conservation perceptions
Bioethics in STEM education
Experiences in fieldwork research
Biotechnology advancements perceptions
Attitudes towards animal experimentation
Gender roles in biology-related careers
Conservation biology relevance perceptions
Views on biodiversity loss consequences
Green chemistry principles attitudes
Nanotechnology applications perceptions
Experiences in research laboratories
Chemical safety practices attitudes
Gender disparities in chemistry education
Chemistry’s role in environmental sustainability
Interdisciplinary approaches in chemistry
Underrepresented minorities in chemistry
Synthetic biology perceptions
Virtual laboratories impact perceptions
Quantum mechanics perceptions
Space exploration attitudes
Experiences in physics research
Gender stereotypes in physics
Renewable energy technologies perceptions
Women representation in physics
Computational physics attitudes
Minority experiences in physics
Philosophy of physics attitudes
Physics in medical technology perspectives

Mathematics

Applied mathematics perceptions
Mathematical modeling attitudes
Minority experiences in mathematics
Gender roles in mathematics
Technology integration in mathematics
Pure mathematics attitudes
Mathematics and computer science integration
Attitudes towards mathematical competitions
Transfer students’ experiences in mathematics
Mathematical research opportunities perceptions

Computer Science

Artificial intelligence ethics perspectives
Cybersecurity practices attitudes
Female experiences in computer science
Impact of coding boot camps
Blockchain technology perceptions
Computer science’s role in social justice
Open-source software development attitudes
Minority experiences in computer science
Gender disparities in computer science careers
Future computing technologies perspectives

Engineering

Sustainable engineering design perceptions
Robotics applications attitudes
Female experiences in engineering
Ethical engineering practices attitudes
Gender stereotypes in engineering
3D printing integration attitudes
Engineering entrepreneurship perspectives
Minority experiences in engineering
Biomedical engineering perceptions
Engineering’s role in climate change

Environmental Science

Environmental policy perspectives
Renewable energy technologies attitudes
Field research experiences
Climate change education impact
Gender disparities in environmental science
Environmental justice perspectives
Sustainable agriculture practices attitudes
Minority experiences in environmental science
Environmental ethics perceptions
Environmental sustainability perspectives

Health Sciences

Healthcare disparities perceptions
Medical ethics attitudes
Minority experiences in healthcare
Cultural competence training impact
Gender disparities in healthcare careers
Telemedicine integration attitudes
Healthcare accessibility perceptions
Clinical research experiences
Public health initiatives attitudes
Global health issues perspectives
Space exploration ethics perspectives
Astrobiology attitudes
Minority experiences in astronomy
Impact of astronomy outreach programs
Gender disparities in astronomy careers
Cultural representation in astronomy
Citizen science initiatives attitudes
Observatory research experiences
Space colonization perceptions
Future space exploration perspectives

Geology/Earth Science

Natural disaster preparedness perceptions
Climate change education attitudes
Minority experiences in geology
Impact of geology education on environmental attitudes
Gender disparities in geology careers
Sustainable resource management attitudes
Geological engineering perceptions
Laboratory research experiences
Environmental geology perceptions
Geological hazards perspectives

Oceanography/Marine Biology

Marine conservation efforts perspectives
Ocean acidification research attitudes
Minority experiences in marine biology
Impact of oceanography education on environmental stewardship
Gender disparities in marine biology careers
Sustainable fisheries management attitudes
Marine pollution research perceptions
Coral reef conservation perceptions
Climate change impacts on marine ecosystems

Neuroscience

Brain-computer interfaces perceptions
Neuroethics attitudes
Minority experiences in neuroscience
Impact of neuroscience education on mental health perspectives
Gender disparities in neuroscience careers
Diverse populations representation in neuroscience
Neuroimaging techniques attitudes
Neuroplasticity perceptions
Neurodiversity perspectives

Agricultural Science

Sustainable agriculture practices perceptions
GMOs attitudes
Minority experiences in agriculture
Impact of agriculture education on food security perspectives
Gender disparities in agriculture careers
Precision agriculture technologies attitudes
Organic farming practices perceptions
Agricultural biotechnology perceptions
Agricultural sustainability perspectives

Bioinformatics

Personalized medicine perceptions
Data privacy attitudes
Minority experiences in bioinformatics
Impact of bioinformatics education on computational skills
Gender disparities in bioinformatics careers
Machine learning integration attitudes
Genomic data sharing perceptions
Computational research experiences
Pharmacogenomics perceptions
Ethical considerations in bioinformatics

Materials Science

Sustainable materials perspectives
Nanomaterials safety attitudes
Minority experiences in materials science
Impact of materials science education on recycling perspectives
Gender disparities in materials science careers
Biomaterials applications attitudes
Additive manufacturing perceptions
Smart materials perceptions
Materials science sustainability perspectives

Energy Science

Energy conservation practices attitudes
Minority experiences in energy science
Impact of energy science education on sustainability perspectives
Gender disparities in energy science careers
Nuclear energy attitudes
Energy storage solutions perceptions
Hydrogen fuel cells perceptions
Energy policy perspectives
Ethical considerations in robotics research
Autonomous systems attitudes
Minority experiences in robotics
Impact of robotics education on problem-solving skills
Gender disparities in robotics careers
Human-robot interaction attitudes
Robot ethics perceptions
Research and development project experiences
Artificial intelligence in robotics perceptions
Future robotics perspectives

Cognitive Science

Consciousness studies perceptions
Cognitive biases attitudes
Minority experiences in cognitive science
Impact of cognitive science education on critical thinking skills
Gender disparities in cognitive science careers
Interdisciplinary approaches attitudes
Cognitive neuroscience research perceptions
Artificial intelligence in cognitive science perceptions
Philosophy of mind perspectives

Environmental Engineering

Sustainable infrastructure perspectives
Water resource management attitudes
Minority experiences in environmental engineering
Impact of environmental engineering education on pollution control perspectives
Gender disparities in environmental engineering careers
Green building technologies attitudes
Environmental impact assessment perceptions
Wastewater treatment technologies perceptions
Environmental remediation perspectives

Renewable Energy Engineering

Solar energy technologies perceptions
Wind energy development attitudes
Minority experiences in renewable energy engineering
Impact of renewable energy engineering education on sustainability perspectives
Gender disparities in renewable energy engineering careers
Bioenergy production attitudes
Geothermal energy technologies perceptions
Policy implications for renewable energy perspectives

These topics provide a range of options for qualitative research across various STEM disciplines, focusing on student perspectives and experiences.

Tips for Conducting Qualitative Research

Check out the tips for conducting qualitative research:-

Practice really listening without jumping to conclusions when chatting with people or observing stuff. Let them steer the conversation.

Instead of just reading about it, get your hands dirty with interviews, focus groups, and hands-on research. Learning by doing is the way to go.

Roll with it!

Qualitative research is like a rollercoaster; it changes. Be ready to adapt your methods as you learn new things, and keep that curious mindset.

Take time to build real connections with the folks you’re studying. Trust and honesty go a long way and give you way cooler insights.

Spot the little things!

Train your eyes to notice tiny behaviors, interactions, and things around you. They often tell a much bigger story.

Ask cool questions!

Instead of yes or no stuff, go for questions that get people talking and sharing their stories. Keep it chill and let the conversation flow.

Check things out from different angles!

Don’t rely on just one source or method. Mix it up to make sure you’re on the right track and not missing anything.

Think about it!

Keep a journal to jot down your thoughts and check how your own ideas might affect your research. It’s like having a conversation with yourself.

Stay organized!

Keep your interviews, notes, and everything in order. Even the small details matter, so don’t skip the nitty-gritty.

Tech is cool, but…

Use recording tools and tech, but don’t forget about connecting with people face-to-face. Balance is key.

Tell a good story!

Work on your storytelling skills. Make your research findings interesting and easy to understand for everyone.

Patience is key!

It takes time and practice to feel confident in digging out the cool human insights that qualitative research brings. Stick with it!

What is the best research topic for stem students qualitative?

Check out the best research topic for STEM students qualitative

Tech and Society Vibes

Check out what regular folks think about cool stuff like AI or gene editing.
See the tough choices scientists face when working on game-changing tech.

STEM for Everyone

Dig into the challenges people from different backgrounds face in the STEM world.
Check if programs meant to make STEM more diverse are hitting the mark.

What STEM Schooling Does for Us

Figure out how different ways of teaching STEM affect what we learn and how much we care.
Explore how STEM learning helps us prep for jobs and tackle big problems.

How Science Talks to Us

See if the ways scientists explain things to regular folks actually make sense.
Check out how fake news might mess with what people think about science.

Remember, these are just starting points. The best topic is the one that gets you amped up and lets you dive deep into something you love in STEM, using cool methods like interviews. Enjoy the ride!

What are the 10 examples of research title qualitative?

Check out the 10 examples of research title qualitative:-

“The Stories of LGBTQ+ Students in STEM Classes”
“How Mentors Help Women in Engineering”
“How Your Culture Affects Learning Science and Math”
“What Parents Have to Say about Their Kids Choosing Science Careers”
“The Tough Choices Researchers Face in Biomedical Science”
“Indigenous Voices in Environmental Science”
“Is Online Learning Making Math More Fun?”
“Why Some People Don’t Choose Computer Science”
“Learning Physics by Doing Projects: Does It Work?”
“First-Gen Students in STEM: What’s Hard and What Helps”

These titles aim to make the research topics sound more accessible and interesting.

What topics can be studied using qualitative research?

Hey there! Qualitative research is like a detective, diving deep into the world of experiences, opinions, and stories. Check out some awesome topics perfect for this type of investigation:

Cultural Adventures

Dig into how different groups do their thing—rituals, traditions, the whole shebang!

Inequality Chronicles

Uncover the real stories of people facing discrimination, poverty, or social unfairness.

Tech and Social Fun

Find out how social media affects our moods, relationships, and what we think about the world.

Edu-Exploration

See if educational programs are making a real impact on students—Are they learning, loving it, and feeling good?

Health Heroes’ Tales

Peek into the lives of folks dealing with healthcare. What’s the real deal with treatments and how does it affect their daily grind?

Business Buzz

Get the scoop on why we buy things! What makes us tick, and how do marketing tricks play into our decisions?

Workplace Wonders

Explore how the vibe at work—how the boss acts, the office culture—shapes how employees feel and perform.

Learn-O-Rama

Find out how students tackle learning. What works for them, what doesn’t, and what makes school a cool place to be.

Personal Stories Rock

Everyone’s got a story. Dive into individual journeys, how historical events affect us, and how personal experiences shape who we are.

Remember, these topics let us peek into the juicy details of human life that numbers alone can’t quite capture. Get ready for some fascinating discoveries!

Alright, let’s break it down in human terms. Imagine being a STEM student, right? Well, there’s more to it than just crunching numbers or running experiments in a lab. We can dive into real-life stories and experiences.

Think about how tech impacts our daily lives or how students actually deal with learning STEM stuff. It’s not just about formulas; it’s about understanding the people behind the science.

And hey, when we explore the culture in scientific communities, it’s like peeking behind the curtain to see how things really work.

Qualitative research opens up this whole new world for STEM students, letting them tackle real-world problems with a fresh, human perspective.

It’s not just about data; it’s about making STEM better for everyone by understanding the people it affects. Cool, right?

Frequently Asked Questions

What is the difference between qualitative and quantitative research in stem.

Qualitative research emphasizes in-depth exploration and understanding of phenomena through methods like interviews and observations, while quantitative research focuses on numerical data and statistical analysis.

How can STEM students ensure the validity and reliability of qualitative research data?

Strategies such as triangulation, member checking, and peer debriefing can help ensure the validity and reliability of qualitative research data by enhancing trustworthiness and credibility.

STEM Education Related Dissertation Abstracts: A Bounded Qualitative Meta-study

Published: 05 January 2012
Volume 21 , pages 730–741, ( 2012 )

Cite this article

James Banning 1 &
James E. Folkestad 1

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This article utilizes a bounded qualitative meta-study framework to examine the 101 dissertation abstracts found by searching the ProQuest Dissertation and Theses™ digital database for dissertations abstracts from 1990 through 2010 using the search terms education, science, technology, engineer, and STEM/SMET. Professional search librarians established the search criteria used to establish the database. The overarching research question for this study was: What can we learn from the examination of doctoral dissertations abstracts that focus on the STEM education found from 1990 through 2010? The study’s findings provide an overview of doctoral research related to STEM education and the discussion section focuses on quality of abstracts, questions regarding the use of the pipeline metaphor, and location of instructional innovation.

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Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020

Ying-Shao Hsu, Kai-Yu Tang & Tzu-Chiang Lin

A review of STEM education with the support of visualizing its structure through the CiteSpace software

Wei Wei Chu, Nur Rosliana Mohd Hafiz, … Siew Wei Tho

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Recruitment and Retention Issues

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Recruitment of the Underrepresented (Community College)

Geist, M. (2008). A methodological examination of a focus group informed Delphi: A mixed methods investigation of female community college science, technology, engineering, and mathematics students. Unpublished 3318405, University of Northern Colorado, United States—Colorado.

Recruitment of the Underrepresented (K-12)

Fadigan, K. A. (2003). A longitudinal study of the educational and career trajectories of female participants of an urban informal science education program. Unpublished 3097691, Temple University, United States—Pennsylvania.

Grisham, A. (2006). Science education for girls: A partnership between Girl Scouts and NASA. Unpublished 3226615, University of Nevada, Las Vegas, United States—Nevada.

Mussey, S. (2009). Navigating the transition to college: First - generation undergraduates negotiate identities and search for success in STEM and non - STEM fields. Unpublished 3355909, University of California, San Diego, United States—California.

Recruitment of the Underrepresented (Federal)

Adolfie, L. (2009). Women scientists and engineers managing national security federal research programs. Unpublished 3359203, Capella University, United States—Minnesota.

Recruitment of the Non-underrepresented (College and University)

Delaney, J. (2007). The academic consequences of state merit aid: The case of Kentucky. Unpublished 3281827, Stanford University, United States—California.

Recruitment of the Non-underrepresented (K-12)

Stanton, R. (2010). State high school graduation requirements and access to postsecondary education. Unpublished 3390456, New York University, United States—New York.

Retention of the Underrepresented (College and University)

Carmichael, K. (2007). Plugging the leaky pipeline: How academic deans support the persistence of underrepresented minority students in science and mathematics. A case study. Unpublished 3287129, University of Southern California, United States—California.

Chen, Y. (2009). East Asian American educational pursuits: Examing effect of racial barriers and cultural factors for college students. Unpublished 3373857, The University of Wisconsin—Milwaukee, United States—Wisconsin.

Espinosa, L. (2009). Pipelines and pathways: Women of color in STEM majors and the experiences that shape their persistence. Unpublished 3394926, University of California, Los Angeles, United States—California.

Galloway, R. (2008). Support resources utilized by minority students majoring in science, technology, engineering, and mathematics disciplines. Unpublished 3322301, University of Pittsburgh, United States—Pennsylvania.

George-Jackson, C. (2009). Rethinking the STEM fields: The importance of definitions in examining women’s participation and success in the sciences. Unpublished 3406828, University of Illinois at Urbana-Champaign, United States—Illinois.

Goldman, E. (2010). Lipstick and labcoats: Undergraduate women’s gender negotiation in STEM fields. Unpublished 3404540, New York University, United States—New York.

Heilbronner, N. (2009). Pathways in STEM: Factors affecting the retention and attrition of talented men and women from the STEM pipeline. Unpublished 3367359, University of Connecticut, United States—Connecticut.

Jackson, D. (2010). Transfer students in STEM majors: Gender differences in the socialization factors that influence academic and social adjustment. Unpublished 3418232, Iowa State University, United States—Iowa.

Jacquot, C. (2009). Gender differences in science, math, and engineering doctoral candidates’ mental models regarding intent to pursue an academic career. Unpublished 3369353, The University of Texas at Arlington, United States—Texas.

Johnson, D. (2007). Sense of belonging among women of color in science, technology, engineering, and math majors: Investigating the contributions of campus racial climate perceptions and other college environments. Unpublished 3297338, University of Maryland, College Park, United States—Maryland.

Lange, S. E. (2006). The master degree: A critical transition in STEM doctoral education. Unpublished 3205862, University of Washington, United States—Washington.

Lee, J. (2006). Getting out the gates: Underrepresented minority students’ search for success in introductory chemistry courses to continue on the STEM path. Unpublished 3250277, University of Illinois at Urbana-Champaign, United States—Illinois.

Lowery, S. E. (2004). Gender Equity Options in Science: Effect on attitudes and behaviors of college women. Unpublished 3135266, Arizona State University, United States—Arizona.

Malcom, L. (2008). Accumulating (dis)advantage? Institutional and financial aid pathways of Latino STEM baccalaureates. Unpublished 3325041, University of Southern California, United States—California.

McAdoo, M. F. (2005). A study of the persistence of science, technology, engineering, and mathematics majors at five southeastern institutions of higher education. Unpublished 3173502, Auburn University, United States—Alabama.

Price, J. (2010). Essays on the economics of education and health. Unpublished 3429846, Cornell University, United States—New York.

Reid, E. (2010). Exploring the experiences of African American women in an undergraduate summer research program designed to address the underrepresentation of women and minorities in neuroscience: A qualitative analysis. Unpublished 3411031, Georgia State University, United States—Georgia.

Robinson, J. (2007). Closing the race and gender gaps in computer science education. Unpublished 3291632, Rowan University, United States—New Jersey.

Rutherford, B. (2007). Interests and attitudes of engineering students. Unpublished 3279580, Utah State University, United States—Utah.

Singh, A. (2008). Beyond gender: Taking a multi - status approach to understanding students’ positioning in STEM. Unpublished 3328730, University of Rhode Island, United States—Rhode Island.

Snead-McDaniel, K. (2010). Exploration of the lived experiences of undergraduate science, technology, engineering, and mathematics minority students. Unpublished 3436670, University of Phoenix, United States—Arizona.

Stone, D. (2008). African - American males in computer science — Examining the pipeline for clogs. Unpublished 3341359, The George Washington University, United States—District of Columbia.

Thoman, D. (2008). How socially rejecting discrimination influences academic motivation, interest, and choices. Unpublished 3312100, The University of Utah, United States—Utah.

Vogt, K. E. (2005). Asian American women in science, engineering, and mathematics: Background contextual and college environment influences on self - efficacy and academic achievement. Unpublished 3202039, University of Maryland, College Park, United States—Maryland.

White, J. L. (2005). Persistence of interest in science, technology, engineering and mathematics: An analysis of persisting and non - persisting students. Unpublished 3169266, The Ohio State University, United States—Ohio.

Williamson, S. (2007). Academic, institutional, and family factors affecting the persistence of Black male STEM majors. Unpublished 3269188, Rutgers The State University of New Jersey—New Brunswick, United States—New Jersey.

Wyss, V. (2008). Questioning the gender critical mass theory in physics. Unpublished 3312127, University of Virginia, United States—Virginia.

Yohannes-Reda, S. (2010). STEMming the tide: Understanding the academic success of Black male college students in science, technology, engineering, and mathematics majors. Unpublished 3422057, University of California, Irvine and California State University, Long Beach, United States—California.

Retention of the Underrepresented (Community College)

Martinez, D. (2007). The manifestation of social capital within the Mathematics, Engineering, and Science Achievement (MESA) program. Unpublished 3291803, University of Southern California, United States—California.

Pina Houde, A. (2007). Portraits of Hispanic females participating in technical programs: Bridging the gap to science, technology, engineering, and mathematics careers. Unpublished 3273253, New Mexico State University, United States—New Mexico.

Retention of the Underrepresented (K-12)

Notter, K. (2010). Is competition making a comeback? Discovering methods to keep female adolescents engaged in STEM: A phenomenological approach. Unpublished 3412882, The University of Nebraska—Lincoln, United States—Nebraska.

Retention of the Underrepresented (Federal)

Graham, E. M. (2006). The impact of the NASA Administrator’s Fellowship Program on fellows’ career choices. Unpublished 3236502, University of Southern California, United States—California.

Retention of the Non-underrepresented (College and University)

Dickerson, J. (2008). The factors that influence the graduation rates of community college transfer students and native students at a four - year public state university. Unpublished 3331220, Mississippi State University, United States—Mississippi.

Eagan, M., Jr. (2010). Moving beyond frontiers: How institutional context affects degree production and student aspirations in STEM. Unpublished 3405569, University of California, Los Angeles, United States—California.

Gresham, P. (2010). An exploratory study of the career aspirations and self - perceptions of university honors program students. Unpublished 3404438, Indiana State University, United States—Indiana.

Rion, C. (2007). Major changes: Student shifts among liberal arts, S.T.E.M. and occupational majors. Unpublished 3270276, State University of New York at Albany, United States—New York.

Yang, X. (2005). A quantitative analysis of factors that influence and predict students’ intention to major in and complete an undergraduate program in STEM or non - STEM. Unpublished 3201832, Kansas State University, United States—Kansas.

Retention of the Non-underrepresented (K-12)

Nicholls, G. (2008). An integrated multiple statistical technique for predicting post - secondary educational degree outcomes based primarily on variables available in the 8th grade. Unpublished 3349215, University of Pittsburgh, United States—Pennsylvania.

Stem Academic Focus

Stem instructional issues (college and university).

Blikstein, P. (2009). An atom is known by the company it keeps: Content, representation and pedagogy within the epistemic revolution of the complexity sciences. Unpublished 3355751, Northwestern University, United States—Illinois.

Bouwma-Gearhart, J. (2008). Teaching professional development of science and engineering professors at a research - extensive university: Motivations, meaningfulness, obstacles, and effects. Unpublished 3327743, The University of Wisconsin—Madison, United States—Wisconsin.

Donawa, A. (2009). Critical thinking instruction and minority engineering students at a public urban higher education institution. Unpublished 3396399, Morgan State University, United States—Maryland.

Gilmour, D. (2008). Effective use of technology in classrooms: Electronic interactive text and integrated technological/pedagogical environment. Unpublished 3326509, Temple University, United States—Pennsylvania.

Hernandez, J. (2007). Examining the value faculty search committee chairpersons place on formal teacher training in the sciences, technology, engineering, and mathematics fields: Results of a national study. Unpublished 3282117, Michigan State University, United States—Michigan.

Hsu, Y. (2009). The effects of self - explanation and metacognitive instruction on undergraduate students’ learning of statistics materials containing multiple external representations in a web - based environment. Unpublished 3399659, The Pennsylvania State University, United States—Pennsylvania.

Maye, M. C. (2003). Study - group collaboration among high - achieving students of African descent studying mathematics at selective United States colleges. Unpublished 3091277, Columbia University Teachers College, United States—New York.

Moakler, M., Jr. (2010). The influence of self - confidence on college freshmen science, technology, engineering, and mathematics major choice. Unpublished 3426942, The George Washington University, United States—District of Columbia.

Schell, J. (2009). Venturing toward better teaching: STEM professors’ efforts to improve their introductory undergraduate pedagogy at major research universities. Unpublished 3368259, Teachers College, Columbia University, United States—New York.

Younkin, W. (2009). The intersection of discipline and roles: Dr. Pauline Mack’s story as an instrumental case study with implications for leadership in science, technology, engineering, and mathematics. Unpublished 3359977, Indiana University of Pennsylvania, United States—Pennsylvania.

STEM Instructional Issues (Community College)

Landon, M. (2009). Emerging workforce trends and issues impacting the Virginia Community College System. Unpublished 3405745, Old Dominion University, United States—Virginia.

Maguire, K. (2009). Post - college earnings of Iowa community college career and technical education students: Analysis of selected career clusters. Unpublished 3355518, Iowa State University, United States—Iowa.

Moriarty, M. A. (2006). Inclusive pedagogy for diverse learners: Science instruction, disability, and the community college. Unpublished 3212745, University of Massachusetts Amherst, United States—Massachusetts.

STEM Instructional Issues (K-12)

Avery, Z. (2010). Effects of profesional development on infusing engineering design into high school science, technology, engineering, and math (STEM) curricula. Unpublished 3397144, Utah State University, United States—Utah.

Boe, J. (2010). Strategies for science, technology, engineering and math in technology education. Unpublished 3420004, North Dakota State University, United States—North Dakota.

Chen, J. (2010). Implicit theories of ability, epistemic beliefs, and academic motivation: A person - centered approach. Unpublished 3423047, Emory University, United States—Georgia.

Clanton, B. L. (2004). The effects of a project - based mathematics curriculum on middle school students’ intended career paths related to science, technology, engineering and mathematics. Unpublished 3243483, University of Central Florida, United States—Florida.

Cruz-Duran, E. (2009). Stereotype threat in mathematics: Female high school students in all - girl and coeducation schools. Unpublished 3365692, St. John’s University (New York), United States—New York.

Degenhart, H. (2007). Relationship of inquiry - based learning elements on changes in middle school students’ science, technology, engineering, and mathematics (STEM) beliefs and interests. Unpublished 3270326, Texas A&M University, United States—Texas.

Donna, J. (2009). Surviving and thriving as a new science teacher: Exploring the role of comprehensive online induction. Unpublished 3360339, University of Minnesota, United States—Minnesota.

Flowers, R. (2008). After - school enrichment and the activity theory: How can a management service organization assist schools with reducing the achievement gap among minority and non - minority students in science, technology, engineering, and mathematics (STEM) during the after - school hours? Unpublished 3356894, Union Institute and University, United States—Ohio.

Gonzales, A. (2010). Toward achievement in the “ knowledge economy ” of the 21st century: Preparing students through T - STEM academies. Unpublished 3398579, Walden University, United States—Minnesota.

Huelskamp, L. (2009). The impact of problem - based learning with computer simulation on middle level educators’ instructional practices and understanding of the nature of middle level learners. Unpublished 3367883, The Ohio State University, United States—Ohio.

Jimarez, T. (2005). Does alignment of constructivist teaching, curriculum, and assessment strategies promote meaningful learning? Unpublished 3208658, New Mexico State University, United States—New Mexico.

Johnson, P. D. (2004). Girls and science: A qualitative study on factors related to success and failure in science. Unpublished 3130607, Western Michigan University, United States—Michigan.

Liu, F. (2010). Factors influencing success in online high school algebra. Unpublished 3436346, University of Florida, United States—Florida.

Maltese, A. (2008). Persistence in STEM: An investigation of the relationship between high school experiences in science and mathematics and college degree completion in STEM fields. Unpublished 3326999, University of Virginia, United States—Virginia.

Miller, M. D. (2006). Science self - efficacy in tenth grade Hispanic female high school students. Unpublished 3210371, University of Central Florida, United States—Florida.

Mowen, D. (2007). Impacts of graduate student content specialists serving in middle school classrooms on teachers and graduate students. Unpublished 3270372, Texas A&M University, United States—Texas.

Moye, J. (2009). Technology education teacher supply and demand in the United States. Unpublished 3371499, Old Dominion University, United States—Virginia.

Norman, K. (2008). High school mathematics curriculum and the process and accuracy of initial mathematics placement for students who are admitted into one of the science, technology, engineering, and mathematics programs at a research institution. Unpublished 3321924, University of Minnesota, United States—Minnesota.

Obarski, K. (2007). Life after National Science Foundation fellowships: The implications for a graduate student’s professional endeavors. Unpublished 3280094, University of Cincinnati, United States—Ohio.

Oware, E. (2008). Examining elementary students’ perceptions of engineers. Unpublished 3344179, Purdue University, United States—Indiana.

Preston, S. (2009). Investigating minority student participation in an authentic science research experience. Unpublished 3380983, The Pennsylvania State University, United States—Pennsylvania.

Ricks, M. M. (2006). A study of the impact of an informal science education program on middle school students’ science knowledge, science attitude, STEM high school and college course selections, and career decisions. Unpublished 3245344, The University of Texas at Austin, United States—Texas.

Scott, C. (2009). A comparative case study of the characteristics of science, technology, engineering, and mathematics (STEM) focused high schools. Unpublished 3365600, George Mason University, United States—Virginia.

Terry, R. (2010). The high school redesign initiative: Teachers’ perspectives. Unpublished 3412676, Mississippi State University, United States—Mississippi.

Veeragoudar Harrell, S. (2009). Second chance at first life: Fostering the mathematical and computational agency of at - risk youth. Unpublished 3369140, University of California, Berkeley, United States—California.

STEM Program Evaluation (College and University)

Greenseid, L. (2008). Using citation analysis methods to assess the influence of STEM education evaluation. Unpublished 3310625, University of Minnesota, United States—Minnesota.

Ivie, C. (2009). National Aeronautics and Space Administration (NASA) education 1993 – 2009. Unpublished 3394583, George Fox University, United States—Oregon.

Lee, Y.-F. (2005). Effects of multiple group involvement on identifying and interpreting perceived needs. Unpublished 3177181, The Ohio State University, United States—Ohio.

STEM Graduate Students (College and University)

DeChenne, S. (2010). Learning to teach effectively: Science, technology, engineering, and mathematics graduate teaching assistants’ teaching self - efficacy. Unpublished 3414593, Oregon State University, United States—Oregon.

Woods, R. (2008). Training culturally responsive remedial math instructors. Unpublished 3325085, University of Southern California, United States—California.

Wyse, S. (2010). Breaking the mold: Preparing graduate teaching assistants to teach as they are taught to teach. Unpublished 3417668, Michigan State University, United States—Michigan.

STEM Mentoring (College and University)

Byington, T. C. (2006). Post - DVM educational intentions among third - year veterinary medical students: A hierarchical analysis of mentoring, gender, and organizational context. Unpublished 3218239, Washington State University, United States—Washington.

Harris Watkins, P. G. (2005). Mentoring in the scientific disciplines: Presidential Awards for Excellence in Science, Mathematics Engineering Mentoring. Unpublished 3164230, The Claremont Graduate University, United States—California.

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Banning, J., Folkestad, J.E. STEM Education Related Dissertation Abstracts: A Bounded Qualitative Meta-study. J Sci Educ Technol 21 , 730–741 (2012). https://doi.org/10.1007/s10956-011-9361-9

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Published : 05 January 2012

Issue Date : December 2012

DOI : https://doi.org/10.1007/s10956-011-9361-9

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Qualitative research is a methodological approach that involves gathering and analyzing non-numerical data to understand and interpret social phenomena. Unlike quantitative research , which emphasizes the collection of numerical data through surveys and experiments, qualitative research is concerned with exploring the subjective experiences, perspectives, and meanings of individuals and groups. As such, qualitative research topics can be diverse and encompass a wide range of social issues and phenomena. From exploring the impact of culture on identity formation to examining the experiences of marginalized communities, qualitative research offers a rich and nuanced perspective on complex social issues. In this post, we will explore some of the most compelling qualitative research topics and provide some tips on how to conduct effective qualitative research.