active learning literature review

Active Learning: An Integrative Review

• First Online: 10 December 2022

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• Gillian Kidman 4 &
• Minh Nguyet Nguyen 4  

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This chapter provides an integrated review of active learning in higher education over the past two decades. The research during this time was scrutinised, revealing four methodological approaches (students’ behaviour and how they engage in their studies, activities/tasks/strategies developed/used to generate/nurture/promote active learning, the theoretical approach to active learning, and the impact of the physical learning environment). Creating an Active Learning Framework of student engagement facilitated a deeper analysis of the 30 articles against four engagement indicators (reflective and integrative learning, learning strategies, quantitative reasoning, and collaborative learning). An adjacency analysis of the methodological approaches and engagement indicators revealed that the active learning field is a maturing research area where researched areas indicate high influence relationships. However, the research focuses on the student undertaking active learning and the materials used. Devoid of research attention is the Lecturer/tutor, their identity as a facilitator of active learning, and as a learner in this area.

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Kidman, G., Nguyen, M.N. (2022). Active Learning: An Integrative Review. In: Chang-Tik, C., Kidman, G., Tee, M.Y. (eds) Collaborative Active Learning. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-4383-6_2

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Instructor strategies to aid implementation of active learning: a systematic literature review

• Kevin A. Nguyen 1 ,
• Maura Borrego 2 ,
• Cynthia J. Finelli   ORCID: orcid.org/0000-0001-9148-1492 3 ,
• Matt DeMonbrun 4 ,
• Caroline Crockett 3 ,
• Sneha Tharayil 2 ,
• Prateek Shekhar 5 ,
• Cynthia Waters 6 &
• Robyn Rosenberg 7  

International Journal of STEM Education volume  8 , Article number:  9 ( 2021 ) Cite this article

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Despite the evidence supporting the effectiveness of active learning in undergraduate STEM courses, the adoption of active learning has been slow. One barrier to adoption is instructors’ concerns about students’ affective and behavioral responses to active learning, especially student resistance. Numerous education researchers have documented their use of active learning in STEM classrooms. However, there is no research yet that systematically analyzes these studies for strategies to aid implementation of active learning and address students’ affective and behavioral responses. In this paper, we conduct a systematic literature review and identify 29 journal articles and conference papers that researched active learning, affective and behavioral student responses, and recommended at least one strategy for implementing active learning. In this paper, we ask: (1) What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies? (2) What instructor strategies to aid implementation of active learning do the authors of these studies provide?

In our review, we noted that most active learning activities involved in-class problem solving within a traditional lecture-based course ( N = 21). We found mostly positive affective and behavioral outcomes for students’ self-reports of learning, participation in the activities, and course satisfaction ( N = 23). From our analysis of the 29 studies, we identified eight strategies to aid implementation of active learning based on three categories. Explanation strategies included providing students with clarifications and reasons for using active learning. Facilitation strategies entailed working with students and ensuring that the activity functions as intended. Planning strategies involved working outside of the class to improve the active learning experience.

To increase the adoption of active learning and address students’ responses to active learning, this study provides strategies to support instructors. The eight strategies are listed with evidence from numerous studies within our review on affective and behavioral responses to active learning. Future work should examine instructor strategies and their connection with other affective outcomes, such as identity, interests, and emotions.

Introduction

Prior reviews have established the effectiveness of active learning in undergraduate science, technology, engineering, and math (STEM) courses (e.g., Freeman et al., 2014 ; Lund & Stains, 2015 ; Theobald et al., 2020 ). In this review, we define active learning as classroom-based activities designed to engage students in their learning through answering questions, solving problems, discussing content, or teaching others, individually or in groups (Prince & Felder, 2007 ; Smith, Sheppard, Johnson, & Johnson, 2005 ), and this definition is inclusive of research-based instructional strategies (RBIS, e.g., Dancy, Henderson, & Turpen, 2016 ) and evidence-based instructional practices (EBIPs, e.g., Stains & Vickrey, 2017 ). Past studies show that students perceive active learning as benefitting their learning (Machemer & Crawford, 2007 ; Patrick, Howell, & Wischusen, 2016 ) and increasing their self-efficacy (Stump, Husman, & Corby, 2014 ). Furthermore, the use of active learning in STEM fields has been linked to improvements in student retention and learning, particularly among students from some underrepresented groups (Chi & Wylie, 2014 ; Freeman et al., 2014 ; Prince, 2004 ).

Despite the overwhelming evidence in support of active learning (e.g., Freeman et al., 2014 ), prior research has found that traditional teaching methods such as lecturing are still the dominant mode of instruction in undergraduate STEM courses, and low adoption rates of active learning in undergraduate STEM courses remain a problem (Hora & Ferrare, 2013 ; Stains et al., 2018 ). There are several reasons for these low adoption rates. Some instructors feel unconvinced that the effort required to implement active learning is worthwhile, and as many as 75% of instructors who have attempted specific types of active learning abandon the practice altogether (Froyd, Borrego, Cutler, Henderson, & Prince, 2013 ).

When asked directly about the barriers to adopting active learning, instructors cite a common set of concerns including the lack of preparation or class time (Finelli, Daly, & Richardson, 2014 ; Froyd et al., 2013 ; Henderson & Dancy, 2007 ). Among these concerns, student resistance to active learning is a potential explanation for the low rates of instructor persistence with active learning, and this negative response to active learning has gained increased attention from the academic community (e.g., Owens et al., 2020 ). Of course, students can exhibit both positive and negative responses to active learning (Carlson & Winquist, 2011 ; Henderson, Khan, & Dancy, 2018 ; Oakley, Hanna, Kuzmyn, & Felder, 2007 ), but due to the barrier student resistance can present to instructors, we focus here on negative student responses. Student resistance to active learning may manifest, for example, as lack of student participation and engagement with in-class activities, declining attendance, or poor course evaluations and enrollments (Tolman, Kremling, & Tagg, 2016 ; Winkler & Rybnikova, 2019 ).

We define student resistance to active learning (SRAL) as a negative affective or behavioral student response to active learning (DeMonbrun et al., 2017 ; Weimer, 2002 ; Winkler & Rybnikova, 2019 ). The affective domain, as it relates to active learning, encompasses not only student satisfaction and perceptions of learning but also motivation-related constructs such as value, self-efficacy, and belonging. The behavioral domain relates to participation, putting forth a good effort, and attending class. The affective and behavioral domains differ from much of the prior research on active learning that centers measuring cognitive gains in student learning, and systematic reviews are readily available on this topic (e.g., Freeman et al., 2014 ; Theobald et al., 2020 ). Schmidt, Rosenberg, and Beymer ( 2018 ) explain the relationship between affective, cognitive, and behavioral domains, asserting all three types of engagement are necessary for science learning, and conclude that “students are unlikely to exert a high degree of behavioral engagement during science learning tasks if they do not also engage deeply with the content affectively and cognitively” (p. 35). Thus, SRAL and negative affective and behavioral student response is a critical but underexplored component of STEM learning.

Recent research on student affective and behavioral responses to active learning has uncovered mechanisms of student resistance. Deslauriers, McCarty, Miller, Callaghan, and Kestin’s ( 2019 ) interviews of physics students revealed that the additional effort required by the novel format of an interactive lecture was the primary source of student resistance. Owens et al. ( 2020 ) identified a similar source of student resistance, which was to their carefully designed biology active learning intervention. Students were concerned about the additional effort required and the unfamiliar student-centered format. Deslauriers et al. ( 2019 ) and Owens et al. ( 2020 ) go a step further in citing self-efficacy (Bandura, 1982 ), mindset (Dweck & Leggett, 1988 ), and student engagement (Kuh, 2005 ) literature to explain student resistance. Similarly, Shekhar et al.’s ( 2020 ) review framed negative student responses to active learning in terms of expectancy-value theory (Wigfield & Eccles, 2000 ); students reacted negatively when they did not find active learning useful or worth the time and effort, or when they did not feel competent enough to complete the activities. Shekhar et al. ( 2020 ) also applied expectancy violation theory from physics education research (Gaffney, Gaffney, & Beichner, 2010 ) to explain how students’ initial expectations of a traditional course produced discomfort during active learning activities. To address both theories of student resistance, Shekhar et al. ( 2020 ) suggested that instructors provide scaffolding (Vygotsky, 1978 ) and support for self-directed learning activities. So, while framing the research as SRAL is relatively new, ideas about working with students to actively engage them in their learning are not. Prior literature on active learning in STEM undergraduate settings includes clues and evidence about strategies instructors can employ to reduce SRAL, even if they are not necessarily framed by the authors as such.

Recent interest in student affective and behavioral responses to active learning, including SRAL, is a relatively new development. But, given the discipline-based educational research (DBER) knowledge base around RBIS and EBIP adoption, we need not to reinvent the wheel. In this paper, we conduct a system review. Systematic reviews are designed to methodically gather and synthesize results from multiple studies to provide a clear overview of a topic, presenting what is known and what is not known (Borrego, Foster, & Froyd, 2014 ). Such clarity informs decisions when designing or funding future research, interventions, and programs. Relevant studies for this paper are scattered across STEM disciplines and in DBER and general education venues, which include journals and conference proceedings. Quantitative, qualitative, and mixed methods approaches have been used to understand student affective and behavioral responses to active learning. Thus, a systematic review is appropriate for this topic given the long history of research on the development of RBIS, EBIPs, and active learning in STEM education; the distribution of primary studies across fields and formats; and the different methods taken to evaluate students’ affective and behavioral responses.

Specifically, we conducted a systematic review to address two interrelated research questions. (1) What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies ? (2) What instructor strategies to aid implementation of active learning do the authors of these studies provide ? These two questions are linked by our goal of sharing instructor strategies that can either reduce SRAL or encourage positive student affective and behavioral responses. Therefore, the instructor strategies in this review are only from studies that present empirical data of affective and behavioral student response to active learning. The strategies we identify in this review will not be surprising to highly experienced teaching and learning practitioners or researchers. However, this review does provide an important link between these strategies and student resistance, which remains one of the most feared barriers to instructor adoption of RBIS, EBIPs, and other forms of active learning.

Conceptual framework: instructor strategies to reduce resistance

Recent research has identified specific instructor strategies that correlate with reduced SRAL and positive student response in undergraduate STEM education (Finelli et al., 2018 ; Nguyen et al., 2017 ; Tharayil et al., 2018 ). For example, Deslauriers et al. ( 2019 ) suggested that physics students perceive the additional effort required by active learning to be evidence of less effective learning. To address this, the authors included a 20-min lecture about active learning in a subsequent course offering. By the end of that course, 65% of students reported increased enthusiasm for active learning, and 75% said the lecture intervention positively impacted their attitudes toward active learning. Explaining how active learning activities contribute to student learning is just one of many strategies instructors can employ to reduce SRAL (Tharayil et al., 2018 ).

DeMonbrun et al. ( 2017 ) provided a conceptual framework for differentiating instructor strategies which includes not only an explanation type of instructor strategies (e.g., Deslauriers et al., 2019 ; Tharayil et al., 2018 ) but also a facilitation type of instructor strategies. Explanation strategies involve describing the purpose (such as how the activity relates to students’ learning) and expectations of the activity to students. Typically, instructors use explanation strategies before the in-class activity has begun. Facilitation strategies include promoting engagement and keeping the activity running smoothly once the activity has already begun, and some specific strategies include walking around the classroom or directly encouraging students. We use the existing categories of explanation and facilitation as a conceptual framework to guide our analysis and systematic review.

As a conceptual framework, explanation and facilitation strategies describe ways to aid the implementation of RBIS, EBIP, and other types of active learning. In fact, the work on these types of instructor strategies is related to higher education faculty development, implementation, and institutional change research perspectives (e.g., Borrego, Cutler, Prince, Henderson, & Froyd, 2013 ; Henderson, Beach, & Finkelstein, 2011 ; Kezar, Gehrke, & Elrod, 2015 ). As such, the specific types of strategies reviewed here are geared to assist instructors in moving toward more student-centered teaching methods by addressing their concerns of student resistance.

SRAL is a particular negative form of affective or behavioral student response (DeMonbrun et al., 2017 ; Weimer, 2002 ; Winkler & Rybnikova, 2019 ). Affective and behavioral student responses are conceptualized at the reactionary level (Kirkpatrick, 1976 ) of outcomes, which consists of how students feel (affective) and how they conduct themselves within the course (behavioral). Although affective and behavioral student responses to active learning are less frequently reported than cognitive outcomes, prior research suggests a few conceptual constructs within these outcomes.

Affective outcomes consist of any students’ feelings, preferences, and satisfaction with the course. Affective outcomes also include students’ self-reports of whether they thought they learned more (or less) during active learning instruction. Some relevant affective outcomes include students’ perceived value or utility of active learning (Shekhar et al., 2020 ; Wigfield & Eccles, 2000 ), their positivity toward or enjoyment of the activities (DeMonbrun et al., 2017 ; Finelli et al., 2018 ), and their self-efficacy or confidence with doing the in-class activity (Bandura, 1982 ).

In contrast, students’ behavioral responses to active learning consist of their actions and practices during active learning. This includes students’ attendance in the class, their participation , engagement, and effort with the activity, and students’ distraction or off-task behavior (e.g., checking their phones, leaving to use the restroom) during the activity (DeMonbrun et al., 2017 ; Finelli et al., 2018 ; Winkler & Rybnikova, 2019 ).

We conceptualize negative or low scores in either affective or behavioral student outcomes as an indicator of SRAL (DeMonbrun et al., 2017 ; Nguyen et al., 2017 ). For example, a low score in reported course satisfaction would be an example of SRAL. This paper aims to synthesize instructor strategies to aid implementation of active learning from studies that either address SRAL and its negative or low scores or relate instructor strategies to positive or high scores. Therefore, we also conceptualize positive student affective and behavioral outcomes as the absence of SRAL. For easy categorization of this review then, we summarize studies’ affective and behavioral outcomes on active learning to either being positive , mostly positive , mixed/neutral , mostly negative , or negative .

We conducted a systematic literature review (Borrego et al., 2014 ; Gough, Oliver, & Thomas, 2017 ; Petticrew & Roberts, 2006 ) to identify primary research studies that describe active learning interventions in undergraduate STEM courses, recommend one or more strategies to aid implementation of active learning, and report student response outcomes to active learning.

A systematic review was warranted due to the popularity of active learning and the publication of numerous papers on the topic. Multiple STEM disciplines and research audiences have published journal articles and conference papers on the topic of active learning in the undergraduate STEM classroom. However, it was not immediately clear which studies addressed active learning, affective and behavioral student responses, and strategies to aid implementation of active learning. We used the systematic review process to efficiently gather results of multiple types of studies and create a clear overview of our topic.

Definitions

For clarity, we define several terms in this review. Researchers refer to us, the authors of this manuscript. Authors and instructors wrote the primary studies we reviewed, and we refer to these primary studies as “studies” consistently throughout. We use the term activity or activities to refer to the specific in-class active learning tasks assigned to students. Strategies refer to the instructor strategies used to aid implementation of active learning and address student resistance to active learning (SRAL). Student response includes affective and behavioral responses and outcomes related to active learning. SRAL is an acronym for student resistance to active learning, defined here as a negative affective or behavioral student response. Categories or category refer to a grouping of strategies to aid implementation of active learning, such as explanation or facilitation. Excerpts are quotes from studies, and these excerpts are used as codes and examples of specific strategies.

Study timeline, data collection, and sample selection

From 2015 to 2016, we worked with a research librarian to locate relevant studies and conduct a keyword search within six databases: two multidisciplinary databases (Web of Science and Academic Search Complete), two major engineering and technology indexes (Compendex and Inspec), and two popular education databases (Education Source and Education Resource Information Center). We created an inclusion criteria that listed both search strings and study requirements:

Studies must include an in-class active learning intervention. This does not include laboratory classes. The corresponding search string was:

“active learning” or “peer-to-peer” or “small group work” or “problem based learning” or “problem-based learning” or “problem-oriented learning” or “project-based learning” or “project based learning” or “peer instruction” or “inquiry learning” or “cooperative learning” or “collaborative learning” or “student response system” or “personal response system” or “just-in-time teaching” or “just in time teaching” or clickers

Studies must include empirical evidence addressing student response to the active learning intervention. The corresponding search string was:

“affective outcome” or “affective response” or “class evaluation” or “course evaluation” or “student attitudes” or “student behaviors” or “student evaluation” or “student feedback” or “student perception” or “student resistance” or “student response”

Studies must describe a STEM course, as defined by the topic of the course, rather than by the department of the course or the major of the students enrolled (e.g., a business class for mathematics majors would not be included, but a mathematics class for business majors would).

Studies must be conducted in undergraduate courses and must not include K-12, vocational, or graduate education.

Studies must be in English and published between 1990 and 2015 as journal articles or conference papers.

In addition to searching the six databases, we emailed solicitations to U.S. National Science Foundation Improving Undergraduate STEM Education (NSF IUSE) grantees. Between the database searches and email solicitation, we identified 2364 studies after removing duplicates. Most studies were from the database search, as we received just 92 studies from email solicitation (Fig. 1 ).

PRISMA screening overview styled after Liberati et al. ( 2009 ) and Passow and Passow ( 2017 )

Next, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for screening studies with our inclusion criteria (Borrego et al., 2014 ; Petticrew & Roberts, 2006 ). From 2016 to 2018, a team of seven researchers conducted two rounds of review in Refworks: the first round with only titles and abstracts and the second round with the entire full-text. In both rounds, two researchers independently decided whether each study should be retained based on our inclusion criteria listed above. At the abstract review stage, if there was a disagreement between independent coders, we decided to pass the study on to the full text screening round. We screened a total of 2364 abstracts, and only 746 studies passed the first round of title and abstract verification (see PRISMA flow chart on Fig. 1 ). If there was still a disagreement between independent coders at the full text screening round, then the seven researchers met and discussed the study, clarified the inclusion criteria as needed to resolve potential future disagreements, and when necessary, took a majority vote (4 out of the 7 researchers) on the inclusion of the study. Due to the high number of coders, it was unusual to reach full consensus with all 7 coders, so a majority vote was used to finalize the inclusion of certain studies. We resolved these disagreements on a rolling basis, and depending on the round (abstract or full text), we disagreed about 10–15% of the time on the inclusion of a study. In both the first and second round of screening, studies were often excluded because they did not gather novel empirical data or evidence (inclusion criteria #2) or were not in an undergraduate STEM course (inclusion criteria #3 and #4). Only 412 studies met all our final inclusion criteria.

Coding procedure

From 2017 to 2018, a team of five researchers then coded these 412 studies for detailed information. To quickly gather information about all 412 studies and to answer the first part of our research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we developed an online coding form using Google Forms and Google Sheets. The five researchers piloted and refined the coding form over three rounds of pair coding, and 19 studies were used to test and revise early versions of the coding form. The final coding form (Borrego et al., 2018 ) used a mix of multiple choice and free response items regarding study characteristics (bibliographic information, type of publication, location of study), course characteristics (discipline, course level, number of students sampled, and type of active learning), methodology (main type of evidence collected, sample size, and analysis methods), study findings (types of student responses and outcomes), and strategy reported (if the study explicitly mentioned using strategies to implementation of active learning).

In the end, only 29 studies explicitly described strategies to aid implementation of active learning (Fig. 1 ), and we used these 29 studies as the dataset for this study. The main difference between these 29 studies and the other 383 studies was that these 29 studies explicitly described the ways authors implemented active learning in their courses to address SRAL or positive student outcomes. Although some readers who are experienced active learning instructors or educational researchers may view pedagogies and strategies as integrated, we found that most papers described active learning methods in terms of student tasks, while advice on strategies, if included, tended to appear separately. We chose to not over interpret passing mentions of how active learning was implemented as strategies recommended by the authors.

Analysis procedure for coding strategies

To answer our second research question (What instructor strategies to aid implementation of active learning do the authors of these studies provide?), we closely reviewed the 29 studies to analyze the strategies in more detail. We used Boyatzis’s ( 1998 ) thematic analysis technique to compile all mentions of instructor strategies to aid implementation of active learning and categorize these excerpts into certain strategies. This technique uses both deductive and inductive coding processes (Creswell & Creswell, 2017 ; Jesiek, Mazzurco, Buswell, & Thompson, 2018 ).

In 2018, three researchers reread the 29 studies, marking excerpts related to strategies independently. We found a total of 126 excerpts. The number of excerpts within each study ranged from 1 to 14 excerpts ( M = 4, SD = 3). We then took all the excerpts and pasted each into its own row in a Google Sheet. We examined the entire spreadsheet as a team and grouped similar excerpts together using a deductive coding process. We used the explanation and facilitation conceptual framework (DeMonbrun et al., 2017 ) and placed each excerpt into either category. We also assigned a specific strategy (i.e., describing the purpose of the activity, or encouraging students) from the framework for each excerpt.

However, there were multiple excerpts that did not easily match either category; we set these aside for the inductive coding process. We then reviewed all excerpts without a category and suggested the creation of a new third category, called planning . We based this new category on the idea that the existing explanation and facilitation conceptual framework did not capture strategies that occurred outside of the classroom. We discuss the specific strategies within the planning category in the Results. With a new category in hand, we created a preliminary codebook consisting of explanation, facilitation, and planning categories, and their respective specific strategies.

We then passed the spreadsheet and preliminary codebook to another researcher who had not previously seen the excerpts. The second researcher looked through all the excerpts and assigned categories and strategies, without being able to see the suggestions of the initial three researchers. The second researcher also created their own new strategies and codes, especially when a specific strategy was not presented in the preliminary codebook. All of their new strategies and codes were created within the planning category. The second researcher agreed on assigned categories and implementation strategies for 71% of the total excerpts. A researcher from the initial strategies coding met with the second researcher and discussed all disagreements. The high number of disagreements, 29%, arose from the specific strategies within the new third category, planning. Since the second researcher created new planning strategies, by default these assigned codes would be a disagreement. The two researchers resolved the disagreements by finalizing a codebook with the now full and combined list of planning strategies and the previous explanation and facilitation strategies. Finally, they started the last round of coding, and they coded the excerpts with the final codebook. This time, they worked together in the same coding sessions. Any disagreements were immediately resolved through discussion and updating of final strategy codes. In the end, all 126 excerpts were coded and kept.

Characteristics of the primary studies

To answer our first research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we report the results from our coding and systematic review process. We discuss characteristics of studies within our dataset below and in Table 1 .

Type of publication and research audience

Of the 29 studies, 11 studies were published in conference proceedings, while the remaining 18 studies were journal articles. Examples of journals included the European Journal of Engineering Education , Journal of College Science Teaching , and PRIMUS (Problems, Resources, and Issues in Mathematics Undergraduate Studies).

In terms of research audiences and perspectives, both US and international views were represented. Eighteen studies were from North America, two were from Australia, three were from Asia, and six were from Europe. For more details about the type of research publications, full bibliographic information for all 29 studies is included in the Appendix.

Types of courses sampled

Studies sampled different types of undergraduate STEM courses. In terms of course year, most studies sampled first-year courses (13 studies). All four course years were represented (4 second-year, 3 third-year, 2 fourth-year, 7 not reported). In regards to course discipline or major, all major STEM education disciplines were represented. Fourteen studies were conducted in engineering courses, and most major engineering subdisciplines were represented, such as electrical and computer engineering (4 studies), mechanical engineering (3 studies), general engineering courses (3 studies), chemical engineering (2 studies), and civil engineering (1 study). Thirteen studies were conducted in science courses (3 physics/astronomy, 7 biology, 3 chemistry), and 2 studies were conducted in mathematics or statistics courses.

For teaching methods, most studies sampled traditional courses that were primarily lecture-based but included some in-class activities. The most common activity was giving class time for students to do problem solving (PS) (21 studies). Students were instructed to either do problem solving in groups (16 studies) or individually (5 studies) and sometimes both in the same course. Project or problem-based learning (PBL) was the second most frequently reported activity with 8 studies, and the implementation of this teaching method ranged from end of term final projects to an entire project or problem-based course. The third most common activity was using clickers (4 studies) or having class discussions (4 studies).

Research design, methods, and outcomes

The 29 studies used quantitative (10 studies), qualitative (6 studies), or mixed methods (13 studies) research designs. Most studies contained self-made instructor surveys (IS) as their main source of evidence (20 studies). In contrast, only 2 studies used survey instruments with evidence of validity (IEV). Other forms of data collection included using institutions’ end of course evaluations (EOC) (10 studies), observations (5 studies), and interviews (4 studies).

Studies reported a variety of different measures for researching students’ affective and behavioral responses to active learning. The most common measure was students’ self-reports of learning (an affective outcome); twenty-one studies measured whether students thought they learned more or less due to the active learning intervention. Other common measures included whether students participated in the activities (16 studies, participation), whether they enjoyed the activities (15 studies, enjoyment), and if students were satisfied with the overall course experience (13 studies, course satisfaction). Most studies included more than one measure. Some studies also measured course attendance (4 studies) and students’ self-efficacy with the activities and relevant STEM disciplines (4 studies).

We found that the 23 of the 29 studies reported positive or mostly positive outcomes for their students’ affective and behavioral responses to active learning. Only 5 studies reported mixed/neutral study outcomes, and only one study reported negative student response to active learning. We discuss the implications of this lack of negative study outcomes and reports of SRAL in our dataset in the “Discussion” section.

To answer our second research question (What instructor strategies to aid implementation of active learning do the authors of these studies provide?), we provide descriptions, categories, and excerpts of specific strategies found within our systematic literature review.

Explanation strategies

Explanation strategies provide students with clarifications and reasons for using active learning (DeMonbrun et al., 2017 ). Within the explanation category, we identified two specific strategies: establish expectations and explain the purpose .

Establish expectations

Establishing expectations means setting the tone and routine for active learning at both the course and in-class activity level. Instructors can discuss expectations at the beginning of the semester, at the start of a class session, or right before the activity.

For establishing expectations at the beginning of the semester, studies provide specific ways to ensure students became familiar with active learning as early as possible. This included “introduc[ing] collaborative learning at the beginning of the academic term” (Herkert , 1997 , p. 450) and making sure that “project instructions and the data were posted fairly early in the semester, and the students were made aware that the project was an important part of their assessment” (Krishnan & Nalim, 2009 , p. 5).

McClanahan and McClanahan ( 2002 ) described the importance of explaining how the course will use active learning and purposely using the syllabus to do this:

Set the stage. Create the expectation that students will actively participate in this class. One way to accomplish that is to include a statement in your syllabus about your teaching strategies. For example: I will be using a variety of teaching strategies in this class. Some of these activities may require that you interact with me or other students in class. I hope you will find these methods interesting and engaging and that they enable you to be more successful in this course . In the syllabus, describe the specific learning activities you plan to conduct. These descriptions let the students know what to expect from you as well as what you expect from them (emphasis added, p. 93).

Early on, students see that the course is interactive, and they also see the activities required to be successful in the course.

These studies and excerpts demonstrate the importance of explaining to students how in-class activities relate to course expectations. Instructors using active learning should start the semester with clear expectations for how students should engage with activities.

Explain the purpose

Explaining the purpose includes offering students reasons why certain activities are being used and convincing them of the importance of participating.

One way that studies explained the purpose of the activities was by leveraging and showing assessment data on active learning. For example, Lenz ( 2015 ) dedicated class time to show current students comments from previous students:

I spend the first few weeks reminding them of the research and of the payoff that they will garner and being a very enthusiastic supporter of the [active learning teaching] method. I show them comments I have received from previous classes and I spend a lot of time selling the method (p. 294).

Providing current students comments from previous semesters may help students see the value of active learning. Lake ( 2001 ) also used data from prior course offerings to show students “the positive academic performance results seen in the previous use of active learning” on the first day of class (p. 899).

However, sharing the effectiveness of the activities does not have to be constrained to the beginning of the course. Autin et al. ( 2013 ) used mid-semester test data and comparisons to sell the continued use of active learning to their students. They said to students:

Based on your reflections, I can see that many of you are not comfortable with the format of this class. Many of you said that you would learn better from a traditional lecture. However, this class, as a whole, performed better on the test than my other [lecture] section did. Something seems to be working here (p. 946).

Showing students’ comparisons between active learning and traditional lecture classes is a powerful way to explain how active learning is a benefit to students.

Explaining the purpose of the activities by sharing course data with students appears to be a useful strategy, as it tells students why active learning is being used and convinces students that active learning is making a difference.

Facilitation strategies

Facilitation strategies ensure the continued engagement in the class activities once they have begun, and many of the specific strategies within this category involve working directly with students. We identified two strategies within the facilitation category: approach students and encourage students .

Approach students

Approaching students means engaging with students during the activity. This includes physical proximity and monitoring students, walking around the classroom, and providing students with additional feedback, clarifications, or questions about the activity.

Several studies described how instructors circulated around the classroom to check on the progress of students during an activity. Lenz ( 2015 ) stated this plainly in her study, “While the students work on these problems I walk around the room, listening to their discussions” (p. 284). Armbruster et al. ( 2009 ) described this strategy and noted positive student engagement, “During each group-work exercise the instructor would move throughout the classroom to monitor group progress, and it was rare to find a group that was not seriously engaged in the exercise” (p. 209). Haseeb ( 2011 ) combined moving around the room and approaching students with questions, and they stated, “The instructor moves around from one discussion group to another and listens to their discussions, ask[ing] provoking questions” (p. 276). Certain group-based activities worked better with this strategy, as McClanahan and McClanahan ( 2002 ) explained:

Breaking the class into smaller working groups frees the professor to walk around and interact with students more personally. He or she can respond to student questions, ask additional questions, or chat informally with students about the class (p. 94).

Approaching students not only helps facilitate the activity, but it provides a chance for the instructor to work with students more closely and receive feedback. Instructors walking around the classroom ensure that both the students and instructor continue to engage and participate with the activity.

Encourage students

Encouraging students includes creating a supportive classroom environment, motivating students to do the activity, building respect and rapport with students, demonstrating care, and having a positive demeanor toward students’ success.

Ramsier et al. ( 2003 ) provided a detailed explanation of the importance of building a supportive classroom environment:

Most of this success lies in the process of negotiation and the building of mutual respect within the class, and requires motivation, energy and enthusiasm on behalf of the instructor… Negotiation is the key to making all of this work, and building a sense of community and shared ownership. Learning students’ names is a challenge but a necessary part of our approach. Listening to student needs and wants with regard to test and homework due dates…projects and activities, etc. goes a long way to build the type of relationships within the class that we need in order to maintain and encourage performance (pp. 16–18).

Here, the authors described a few specific strategies for supporting a positive demeanor, such as learning students’ names and listening to student needs and wants, which helped maintain student performance in an active learning classroom.

Other ways to build a supportive classroom environment were for instructors to appear more approachable. For example, Bullard and Felder ( 2007 ) worked to “give the students a sense of their instructors as somewhat normal and approachable human beings and to help them start to develop a sense of community” (p. 5). As instructors and students become more comfortable working with each other, instructors can work toward easing “frustration and strong emotion among students and step by step develop the students’ acceptance [of active learning]” (Harun, Yusof, Jamaludin, & Hassan, 2012 , p. 234). In all, encouraging students and creating a supportive environment appear to be useful strategies to aid implementation of active learning.

Planning strategies

The planning category encompasses strategies that occur outside of class time, distinguishing it from the explanation and facilitation categories. Four strategies fall into this category: design appropriate activities , create group policies , align the course , and review student feedback .

Design appropriate activities

Many studies took into consideration the design of appropriate or suitable activities for their courses. This meant making sure the activity was suitable in terms of time, difficulty, and constraints of the course. Activities were designed to strike a balance between being too difficult and too simple, to be engaging, and to provide opportunities for students to participate.

Li et al. ( 2009 ) explained the importance of outside-of-class planning and considering appropriate projects: “The selection of the projects takes place in pre-course planning. The subjects for projects should be significant and manageable” (p. 491). Haseeb ( 2011 ) further emphasized a balance in design by discussing problems (within problem-based learning) between two parameters, “the problem is deliberately designed to be open-ended and vague in terms of technical details” (p. 275). Armbruster et al. ( 2009 ) expanded on the idea of balanced activities by connecting it to group-work and positive outcomes, and they stated, “The group exercises that elicited the most animated student participation were those that were sufficiently challenging that very few students could solve the problem individually, but at least 50% or more of the groups could solve the problem by working as a team” (p. 209).

Instructors should consider the design of activities outside of class time. Activities should be appropriately challenging but achievable for students, so that students remain engaged and participate with the activity during class time.

Create group policies

Creating group policies means considering rules when using group activities. This strategy is unique in that it directly addresses a specific subset of activities, group work. These policies included setting team sizes and assigning specific roles to group members.

Studies outlined a few specific approaches for assigning groups. For example, Ramsier et al. ( 2003 ) recommended frequently changing and randomizing groups: “When students enter the room on these days they sit in randomized groups of 3 to 4 students. Randomization helps to build a learning community atmosphere and eliminates cliques” (p. 4). Another strategy in combination with frequent changing of groups was to not allow students to select their own groups. Lehtovuori et al. ( 2013 ) used this to avoid problems of freeriding and group dysfunction:

For example, group division is an issue to be aware of...An easy and safe solution is to draw lots to assign the groups and to change them often. This way nobody needs to suffer from a dysfunctional group for too long. Popular practice that students self-organize into groups is not the best solution from the point of view of learning and teaching. Sometimes friendly relationships can complicate fair division of responsibility and work load in the group (p. 9).

Here, Lehtovuori et al. ( 2013 ) considered different types of group policies and concluded that frequently changing groups worked best for students. Kovac ( 1999 ) also described changing groups but assigned specific roles to individuals:

Students were divided into groups of four and assigned specific roles: manager, spokesperson, recorder, and strategy analyst. The roles were rotated from week to week. To alleviate complaints from students that they were "stuck in a bad group for the entire semester," the groups were changed after each of the two in-class exams (p. 121).

The use of four specific group roles is a potential group policy, and Kovac ( 1999 ) continued the trend of changing group members often.

Overall, these studies describe the importance of thinking about ways to implement group-based activities before enacting them during class, and they suggest that groups should be reconstituted frequently. Instructors using group activities should consider whether to use specific group member policies before implementing the activity in the classroom.

Align the course

Aligning the course emphasizes the importance of purposely connecting multiple parts of the course together. This strategy involves planning to ensure students are graded on their participation with the activities as well as considering the timing of the activities with respect to other aspects of the course.

Li et al. ( 2009 ) described aligning classroom tasks by discussing the importance of timing, and they wrote, “The coordination between the class lectures and the project phases is very important. If the project is assigned near the directly related lectures, students can instantiate class concepts almost immediately in the project and can apply the project experience in class” (p. 491).

Krishnan and Nalim ( 2009 ) aligned class activities with grades to motivate students and encourage participation: “The project was a component of the course counting for typically 10-15% of the total points for the course grade. Since the students were told about the project and that it carried a significant portion of their grade, they took the project seriously” (p. 4). McClanahan and McClanahan ( 2002 ) expanded on the idea of using grades to emphasize the importance of active learning to students:

Develop a grading policy that supports active learning. Active learning experiences that are important enough to do are important enough to be included as part of a student's grade…The class syllabus should describe your grading policy for active learning experiences and how those grades factor into the student's final grade. Clarify with the students that these points are not extra credit. These activities, just like exams, will be counted when grades are determined (p. 93).

Here, they suggest a clear grading policy that includes how activities will be assessed as part of students’ final grades.

de Justo and Delgado ( 2014 ) connected grading and assessment to learning and further suggested that reliance on exams may negatively impact student engagement:

Particular attention should be given to alignment between the course learning outcomes and assessment tasks. The tendency among faculty members to rely primarily on written examinations for assessment purposes should be overcome, because it may negatively affect students’ engagement in the course activities (p. 8).

Instructors should consider their overall assessment strategies, as overreliance on written exams could mean that students engage less with the activities.

When planning to use active learning, instructors should consider how activities are aligned with course content and students’ grades. Instructors should decide before active learning implementation whether class participation and engagement will be reflected in student grades and in the course syllabus.

Review student feedback

Reviewing student feedback includes both soliciting feedback about the activity and using that feedback to improve the course. This strategy can be an iterative process that occurs over several course offerings.

Many studies utilized student feedback to continuously revise and improve the course. For example, Metzger ( 2015 ) commented that “gathering and reviewing feedback from students can inform revisions of course design, implementation, and assessment strategies” (p. 8). Rockland et al. ( 2013 ) further described changing and improving the course in response to student feedback, “As a result of these discussions, the author made three changes to the course. This is the process of continuous improvement within a course” (p. 6).

Herkert ( 1997 ) also demonstrated the use of student feedback for improving the course over time: “Indeed, the [collaborative] learning techniques described herein have only gradually evolved over the past decade through a process of trial and error, supported by discussion with colleagues in various academic fields and helpful feedback from my students” (p. 459).

In addition to incorporating student feedback, McClanahan and McClanahan ( 2002 ) commented on how student feedback builds a stronger partnership with students, “Using student feedback to make improvements in the learning experience reinforces the notion that your class is a partnership and that you value your students’ ideas as a means to strengthen that partnership and create more successful learning” (p. 94). Making students aware that the instructor is soliciting and using feedback can help encourage and build rapport with students.

Instructors should review student feedback for continual and iterative course improvement. Much of the student feedback review occurs outside of class time, and it appears useful for instructors to solicit student feedback to guide changes to the course and build student rapport.

Summary of strategies

We list the appearance of strategies within studies in Table 1 in short-hand form. No study included all eight strategies. Studies that included the most strategies were Bullard and Felder’s ( 2007 ) (7 strategies), Armbruster et al.’s ( 2009 ) (5 strategies), and Lenz’s ( 2015 ) (5 strategies). However, these three studies were exemplars, as most studies included only one or two strategies.

Table 2 presents a summary list of specific strategies, their categories, and descriptions. We also note the number of unique studies ( N ) and excerpts ( n ) that included the specific strategies. In total, there were eight specific strategies within three categories. Most strategies fell under the planning category ( N = 26), with align the course being the most reported strategy ( N = 14). Approaching students ( N = 13) and reviewing student feedback ( N = 11) were the second and third most common strategies, respectively. Overall, we present eight strategies to aid implementation of active learning.

Characteristics of the active learning studies

To address our first research question (What are the characteristics of studies that examine affective and behavioral outcomes of active learning and provide instructor strategies?), we discuss the different ways studies reported research on active learning.

Limitations and gaps within the final sample

First, we must discuss the gaps within our final sample of 29 studies. We excluded numerous active learning studies ( N = 383) that did not discuss or reflect upon the efficacy of their strategies to aid implementation of active learning. We also began this systematic literature review in 2015 and did not finish our coding and analysis of 2364 abstracts and 746 full-texts until 2018. We acknowledge that there have been multiple studies published on active learning since 2015. Acknowledging these limitations, we discuss our results and analysis in the context of the 29 studies in our dataset, which were published from 1990 to 2015.

Our final sample included only 2 studies that sampled mathematics and statistics courses. In addition, there was also a lack of studies outside of first-year courses. Much of the active learning research literature introduces interventions in first-year (cornerstone) or fourth-year (capstone) courses, but we found within our dataset a tendency to oversample first-year courses. However, all four course-years were represented, as well as all major STEM disciplines, with the most common STEM disciplines being engineering (14 studies) and biology (7 studies).

Thirteen studies implemented course-based active learning interventions, such as project-based learning (8 studies), inquiry-based learning (3 studies), or a flipped classroom (2 studies). Only one study, Lenz ( 2015 ), used a previously published active learning intervention, which was Process-Oriented Guided Inquiry Learning (POGIL). Other examples of published active learning programs include the Student-Centered Active Learning Environment for Upside-down Pedagogies (SCALE-UP, Gaffney et al., 2010 ) and Chemistry, Life, the Universe, and Everything (CLUE, Cooper & Klymkowsky, 2013 ), but these were not included in our sample of 29 studies.

In contrast, most of the active learning interventions involved adding in-class problem solving (either with individual students or groups of students) to a traditional lecture course (21 studies). For some instructors attempting to adopt active learning, using this smaller active learning intervention (in-class problem solving) may be a good starting point.

Despite the variety of quantitative, qualitative, and mixed method research designs, most studies used either self-made instructor surveys (20 studies) or their institution’s course evaluations (10 studies). The variation between so many different versions of instructor surveys and course evaluations made it difficult to compare data or attempt a quantitative meta-analysis. Further, only 2 studies used instruments with evidence of validity. However, that trend may change as there are more examples of instruments with evidence of validity, such as the Student Response to Instructional Practices (StRIP, DeMonbrun et al., 2017 ), the Biology Interest Questionnaire (BIQ, Knekta, Rowland, Corwin, & Eddy, 2020 ), and the Pedagogical Expectancy Violation Assessment (PEVA, Gaffney et al., 2010 ).

We were also concerned about the use of institutional course evaluations (10 studies) as evidence of students’ satisfaction and affective responses to active learning. Course evaluations capture more than just students’ responses to active learning, as the scores are biased toward the instructors’ gender (Mitchell & Martin, 2018 ) and race (Daniel, 2019 ), and they are strongly correlated with students’ expected grade in the class (Nguyen et al., 2017 ). Despite these limitations, we kept course evaluations in our keyword search and inclusion criteria, because they relate to instructors concerns about student resistance to active learning, and these scores continue to be used for important instructor reappointment, tenure, and promotion decisions (DeMonbrun et al., 2017 ).

In addition to students’ satisfaction, there were other measures related to students’ affective and behavioral responses to active learning. The most common measure was students’ self-reports of whether they thought they learned more or less (21 studies). Other important affective outcomes included enjoyment (13 studies) and self-efficacy (4 students). The most common behavioral measure was students’ participation (16 studies). However, missing from this sample were other affective outcomes, such as students’ identities, beliefs, emotions, values, and buy-in.

Positive outcomes for using active learning

Twenty-three of the 29 studies reported positive or mostly positive outcomes for their active learning intervention. At the start of this paper, we acknowledged that much of the existing research suggested the widespread positive benefits of using active learning in undergraduate STEM courses. However, much of these positive benefits related to active learning were centered on students’ cognitive learning outcomes (e.g., Theobald et al., 2020 ) and not students’ affective and behavioral responses to active learning. Here, we show positive affective and behavioral outcomes in terms of students’ self-reports of learning, enjoyment, self-efficacy, attendance, participation, and course satisfaction.

Due to the lack of mixed/neutral or negative affective outcomes, it is important to acknowledge potential publication bias within our dataset. Authors may be hesitant to report negative outcomes to active learning interventions. It could also be the case that negative or non-significant outcomes are not easily published in undergraduate STEM education venues. These factors could help explain the lack of mixed/neutral or negative study outcomes in our dataset.

Strategies to aid implementation of active learning

We aimed to answer the question: what instructor strategies to aid implementation of active learning do the authors of these studies provide? We addressed this question by providing instructors and readers a summary of actionable strategies they can take back to their own classrooms. Here, we discuss the range of strategies found within our systematic literature review.

Supporting instructors with actionable strategies

We identified eight specific strategies across three major categories: explanation, facilitation, and planning. Each strategy appeared in at least seven studies (Table 2 ), and each strategy was written to be actionable and practical.

Strategies in the explanation category emphasized the importance of establishing expectations and explaining the purpose of active learning to students. The facilitation category focused on approaching and encouraging students once activities were underway. Strategies in the planning category highlight the importance of working outside of class time to thoughtfully design appropriate activities , create policies for group work , align various components of the course , and review student feedback to iteratively improve the course.

However, as we note in the “Introduction” section, these strategies are not entirely new, and the strategies will not be surprising to experienced researchers and educators. Even still, there has yet to be a systematic review that compiles these instructor strategies in relation to students’ affective and behavioral responses to active learning. For example, the “explain the purpose” strategy is similar to the productive framing (e.g., Hutchison & Hammer, 2010 ) of the activity for students. “Design appropriate activities” and “align various components of the course” relate to Vygotsky’s ( 1978 ) theories of scaffolding for students (Shekhar et al., 2020 ). “Review student feedback” and “approaching students” relate to ideas on formative assessment (e.g., Pellegrino, DiBello, & Brophy, 2014 ) or revising the course materials in relation to students’ ongoing needs.

We also acknowledge that we do not have an exhaustive list of specific strategies to aid implementation of active learning. More work needs to be done measuring and observing these strategies in-action and testing the use of these strategies against certain outcomes. Some of this work of measuring instructor strategies has already begun (e.g., DeMonbrun et al., 2017 ; Finelli et al., 2018 ; Tharayil et al., 2018 ), but further testing and analysis would benefit the active learning community. We hope that our framework of explanation, facilitation, and planning strategies provide a guide for instructors adopting active learning. Since these strategies are compiled from the undergraduate STEM education literature and research on affective and behavioral responses to active learning, instructors have compelling reason to use these strategies to aid implementation of active learning.

One way to consider using these strategies is to consider the various aspects of instruction and their sequence. That is, planning strategies would be most applicable during the phase of work that occurs prior to classroom instruction, the explanation strategies would be more useful when introducing students to active learning activities, while facilitation strategies would be best enacted while students are already working and engaged in the assigned activities. Of course, these strategies may also be used in conjunction with each other and are not strictly limited to these phases. For example, one plausible approach could be using the planning strategies of design and alignment as areas of emphasis during explanation . Overall, we hope that this framework of strategies supports instructors’ adoption and sustained use of active learning.

Creation of the planning category

At the start of this paper, we presented a conceptual framework for strategies consisting of only explanation and facilitation categories (DeMonbrun et al., 2017 ). One of the major contributions of this paper is the addition of a third category, which we call the planning category, to the existing conceptual framework. The planning strategies were common throughout the systematic literature review, and many studies emphasized the need to consider how much time and effort is needed when adding active learning to the course. Although students may not see this preparation, and we did not see this type of strategy initially, explicitly adding the planning category acknowledges the work instructors do outside of the classroom.

The planning strategies also highlight the need for instructors to not only think about implementing active learning before they enter the class, but to revise their implementation after the class is over. Instructors should refine their use of active learning through feedback, reflection, and practice over multiple course offerings. We hope this persistence can lead to long-term adoption of active learning.

Despite our review ending in 2015, most of STEM instruction remains didactic (Laursen, 2019 ; Stains et al., 2018 ), and there has not been a long-term sustained adoption of active learning. In a push to increase the adoption of active learning within undergraduate STEM courses, we hope this study provided support and actionable strategies for instructors who are considering active learning but are concerned about student resistance to active learning.

We identified eight specific strategies to aid implementation of active learning based on three categories. The three categories of strategies were explanation, facilitation, and planning. In this review, we created the third category, planning, and we suggested that this category should be considered first when implementing active learning in the course. Instructors should then focus on explaining and facilitating their activity in the classroom. The eight specific strategies provided here can be incorporated into faculty professional development programs and readily adopted by instructors wanting to implement active learning in their STEM courses.

There remains important future work in active learning research, and we noted these gaps within our review. It would be useful to specifically review and measure instructor strategies in-action and compare its use against other affective outcomes, such as identity, interest, and emotions.

There has yet to be a study that compiles and synthesizes strategies reported from multiple active learning studies, and we hope that this paper filled this important gap. The strategies identified in this review can help instructors persist beyond awkward initial implementations, avoid some problems altogether, and most importantly address student resistance to active learning. Further, the planning strategies emphasize that the use of active learning can be improved over time, which may help instructors have more realistic expectations for the first or second time they implement a new activity. There are many benefits to introducing active learning in the classroom, and we hope that these benefits are shared among more STEM instructors and students.

Availability of data and materials

Journal articles and conference proceedings which make up this review can be found through reverse citation lookup. See the Appendix for the references of all primary studies within this systematic review. We used the following databases to find studies within the review: Web of Science, Academic Search Complete, Compendex, Inspec, Education Source, and Education Resource Information Center. More details and keyword search strings are provided in the “Methods” section.

Abbreviations

Science, technology, engineering, and mathematics

Student resistance to active learning

Instrument with evidence of validity

Instructor surveys

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Problem solving

Problem or project-based learning

End of course evaluations

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Acknowledgements

We thank our collaborators, Charles Henderson and Michael Prince, for their early contributions to this project, including screening hundreds of abstracts and full papers. Thank you to Adam Papendieck and Katherine Doerr for their feedback on early versions of this manuscript. Finally, thank you to the anonymous reviewers at the International Journal of STEM Education for your constructive feedback.

This work was supported by the National Science Foundation through grant #1744407. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Nguyen, K.A., Borrego, M., Finelli, C.J. et al. Instructor strategies to aid implementation of active learning: a systematic literature review. IJ STEM Ed 8 , 9 (2021). https://doi.org/10.1186/s40594-021-00270-7

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• Active learning
• Systematic review
• Instructor strategies; student response

REVIEW article

Trends of active learning in higher education and students’ well-being: a literature review.

• 1 Faculty of Sport Sciences and Physical Education, University of Coimbra, Coimbra, Portugal
• 2 Research Unit in Sport and Physical Activity (CIDAF), Coimbra, Portugal
• 3 Centre for 20th Century Interdisciplinary Studies (CEIS20), Coimbra, Portugal
• 4 Faculty of Education, Universidad Internacional de La Rioja, La Rioja, Spain
• 5 Faculty of Sport, University of Porto, Porto, Portugal
• 6 Research Centre in Education, Innovation, Intervention in Sport (CIFI2D), Porto, Portugal
• 7 Centre for Research and Intervention in Education (CIIE), Porto, Portugal

This literature Review had the purpose of inspecting how the use of active learning methodologies in higher education can impact students’ Well-being. Considering the Heads of State meeting at United Nations Headquarters on September 2015, in which the 2030 Agenda for Sustainable Development was adopted by all United Nations Member states, this literature review is limbered to the time period between September 2015 and September 2021. A Previous research focused on reviews was made to support the conceptual framework. The search was done in two databases - Web of Science main collection and Scopus - by two researchers autonomously, using the following search criteria: “higher education AND active learning AND student AND wellness OR well-being OR wellbeing.” The studies section attended the following inclusion criteria: (i) published in peer-reviewed journals; (ii) empirical studies; (iii) written in English, French, Portuguese or Spanish; (iv) open access full text; (v) Higher education context; and (vi) focused on the topic under study. The search provided 10 articles which were submitted to an inductive thematic analysis attending to the purpose of this review, resulting in two themes: (i) students’ well-being during confinement; (ii) methodological solutions for students’ well-being. Data show that the use of active methodologies, as digital technologies, and the incorporation of some practice as physical activity and volunteering seems to benefit students’ well-being, namely in their academic achievement, physical, emotional, and social life, and empower them to the professional future with multi-competencies. Higher education institutions need to understand the value of active learning methodologies in sustained education and promote them in their practices.

Introduction

The well-being of students has grown in importance in recent decades, and according to Ecclestone and Hayes (2009) , everyone considers that well-being should be emphasized as a component of education. There are different interpretations of well-being, and several educational policies that consider well-being in different ways. Tiberius (2013) highlights five main theories, with well-being considered as either a subjective theory (based on things that are intrinsically good for us) such as hedonism ( Bradley, 2015 ); desire fulfillment ( Griffin, 1986 ) or life satisfaction ( Sumner, 1996 ), or an objective theory (based on things that are instrumentally good for us) such as human nature fulfillment theory ( Nussbaum, 2011 ) or individually driven nature fulfillment theory ( Haybron, 2008 ). Thorburn (2020) taking as a reference this conceptual framework encompassed in two major perspectives of well-being, pointed out that a “middle-path version of well-being (one that coherently merges the intrinsic and the instrumental, the subjective with the objective) could better answer to the necessity of improvements in subject teaching and personal well-being.” Seeing this understanding of well-being, and the importance of citizens’ well-being for societal growth and sustainability, it is crucial that students’ well-being, the future citizens, be included in national and international policies.

Since the 2000s, there has been a strong interest in educating for personal well-being, Thorburn (2020) reported that countries like Australia, England, New Zealand, and Scotland, in different ways, try to incorporate issues related to students’ well-being in their national curricular reforms. As stated by Matthews et al. (2015) , well-being is represented in educational policy when schools display the ability to answer to societal concerns for students’ mental, emotional, social, and physical needs. Norway is an example since it believes that, even in difficult economic circumstances, schools can help to make young people’s lives more rewarding and meaningful ( Layard and Dun, 2009 ).

On a global scale, the United Nations resolution titled “Transforming our World: Sustainable Development Agenda 2030” went into effect on January 1, 2016, along with the 17 sustainable development goals. These intended to build, by 2030, a world with equitable and universal access to quality education at all levels, to health care and social protection, where physical, mental and social well-being are assured ( United Nations, 2015 , p. 17), emphasizing that no one, whether from developed or developing nations, would be left behind. Concerning education, especially goals three and four, pointed to “ensure healthy lives and promote well-being for all at all ages and ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, respectively” ( United Nations, 2015 , p. 17).

Without foreseeing the global public health crisis that was to come, the General Secretary of the UN reiterated the Agenda in September 2019, appealing to the need for the current decade to be one of action, so that the goals could be met, with education being one of the primary vectors of change ( United Nations, 2020 ). In fact, the pandemic’s accentuated inequality heightened a digital, educational, and social gap ( Díez-Gutiérrez and Gajardo-Espinoza, 2020 ), exacerbating socio-economic disparities and focusing attention on digital exclusion ( Tommaso and Soncin, 2021 ), jeopardizing even more the 2030 Agenda’s goals by forcing many students, already among the most disadvantaged, to drop out of school ( World Bank, 2020 ).

In this pandemic scenario, the relevance of student well-being has increased. Higher education institutions made significant technology expenditures to set up classrooms despite the fact that each university was free to select how to best organize the transition ( Bergdahl et al., 2020 ; Silamut and Petsangsri, 2020 ). As a result, the route toward the goals of sustainable development, although always significant, has become both fundamental and complex in this new context, with everyone having a role to play.

Universities, which Batista et al. (2021) claims that, in the context of teacher education, their commitment to society is nothing more than mere declarations of intent, see their responsibility increased here, given that they will have to train teachers for a future they do not foresee, but which they know is constantly changing and updating. Despite the limited academic references relating to how well-being may become a successful element of schooling, curricular adjustments have been taking place in several countries, including Australia, England, New Zealand, and Scotland, in order to integrate questions linked to well-being ( Thorburn, 2020 ).

In the ‘90s, higher education teachers had an intuitive grasp of “active learning,” believing that learning is intrinsically dynamic and that students are actively participating when listening to formal lectures in the classroom ( Bonwel and Eison, 1991 ). This kind of thinking shifted. According to the National Survey of Student Engagement and the Australasian Survey of Student Engagement, active learning includes students’ efforts to actively create their knowledge ( Brame, 2016 ). Students must read, write, discuss, solve problems, and engage in higher-order thinking activities such as analysis, synthesis, and assessment. Students should be involved in doing things and thinking about what they are doing, and students’ explorations of their attitudes and values should be emphasized in active learning practices ( Bonwel and Eison, 1991 ; Carr et al., 2015 ). Nevertheless, the notion of active learning results not only from teaching methodologies that require students to actively participate in the classes’ activities (to build their own knowledge) but involves other methodologies not related with the subject under study and that lead students to leave the walls of the school space. Carr et al. (2015) referring that this broader understanding of active learning, entails not only working with other students on projects during class, giving a presentation, asking questions or contributing to discussions, but also participating in a community-based project as part of a course, working with other students outside of class on assignments, discussing ideas from a course with others outside the class, and tutoring peers. So, active learning requires viewing the learning process as a constructive process that brings individuals from all over the world together. As stated by Misseyanni et al., 2018 (in preface 2018, p. XIX) “we do believe in the capacity of the global community of creative minds and caring individuals to use active learning for the development of a new culture that will lead to more sustainable societies.” The same authors argued that active learning entails adapting our circumstances, personal beliefs, and understandings to a global scale. According to this remark, higher education programs may empower students to have a more humanistic perspective, as well as for the well-being of their pupils. As a result, teaching approaches must be tailored to people and should aid in their integration into society, so that learning may be transferred to the future active lives of students who do not yet know what they want to do ( Sebastiani, 2017 ).

Collaboration among all is the way to answer to the challenges that the world is currently facing, such as environmental preservation, poverty, socially inclusive and just development, smart and sustainable cities, mutual respect, and the generation of new knowledge for providing sustainable solutions to social problems, is the vision for the active learning philosophy that must be implemented ( Misseyanni et al., 2018 ). Learning can always make a difference in this situation, reducing passivity in the face of challenges, mobilizing emotions, and inspiring action.

This issue prompted us to look at how the use of active learning methodologies in higher education might affect students’ well-being, which is the study’s main purpose. To accomplish this, we conducted a literature review focusing on the use of active learning methodologies in higher education, from the approval of the 2030 Sustainable Development Agenda (2015) and today (2021), in order to understand the sensitivity of higher education institutions to the agenda, based on the studies conducted during that time period.

What We Know From Previous Reviews?

With the goal of starting from a conceptual framework that would help to frame the focus of the current review, and in response to the component of PRISMA 2020 1 (previous studies), a search was conducted in the same databases considered for this review (Web of Science and Scopus), in journals that published exclusively reviews or are important in the field of higher education (see Figure 1 ), resulting on the selection of six studies were ( Akinla et al., 2018 ; van der Zanden et al., 2018 ; Kötter et al., 2019 ; Theelen et al., 2019 ; Thorburn, 2020 ; Tommaso and Soncin, 2021 ). These previous reviews approach the issue of student well-being from various angles and with different emphasis; however, the importance of educational procedures focusing on students is a recurring theme.

Figure 1. Preferred reporting items for this review.

Some of the arguments in higher education include the stress that the transfer to higher education causes students ( Akinla et al., 2018 ), as well as the desired results of higher education and how such outcomes should be assessed ( van der Zanden et al., 2018 ). By exploring multiple theoretical research strands, the conversation about what it means to be successful at university produced a conceptual framework made of three domains: students’ academic accomplishment, critical thinking skills, and social-emotional well-being. As a result, interventions that promote students’ well-being are critical to their performance. In a research with medical students, Akinla et al. (2018) suggested that near-peer mentorship may help with some of these issues during the transition phase to the university. Near-peer mentoring is a strategy that may enhance students’ professional and personal growth, as well as ease the transition and preserve well-being. In addition to being a significant resource in offering social and academic assistance to incoming students, near-peer mentoring aids in transition and stress reduction. Additionally, customized housing and activity programs (for example, participation in an outdoor orientation program) had a favorable influence on students’ well-being. Multiple research on peer mentorship programs found that involvement aided students’ social integration and adjustment rather than their overall adjustment feelings ( van der Zanden et al., 2018 ).

The pandemic scenario is another issue that makes well-being a crucial concern for higher education. According to Tommaso and Soncin (2021) , universities made significant technology expenditures to prepare classrooms for blended learning and strive to provide other activities in addition to teaching. The challenges of the shift from face-to-face to online education were numerous and complex, but they demonstrated that the fundamental goals of the faculties had to be the students, not the method itself. The significance of digital transformations was also emphasized. The difficulty presented has some positive aspects for innovative education. Furthermore, the same authors stated that one of the most essential lessons of this challenge is that the emphasis of education stays on relationships. Relations give meaning to students’ educational experiences as well as the process through which research and innovation are developed ( Tommaso and Soncin, 2021 ).

In a research with preservice teachers, Theelen et al. (2019) reported that computer-based classroom simulations provide a safe tool to practice and develop preservice teachers’ interpersonal competency, as well as contribute to preservice teachers’ well-being. This teacher-student centered strategy aids students in overcoming their difficulties with classroom management and interpersonal relationships between teachers and students. The authors also emphasize the need of investing in simulation active methodologies that improve preservice teachers’ learning experiences and have the potential to be a valuable asset for teacher education by bridging the gap between teacher education and classroom practice. It is necessary to conduct additional research into the interrelationships between preservice teachers’ well-being, interpersonal competency, learning experiences, and computer-based classroom simulations.

According to Thorburn (2020) , governmental policies in England, Australia, New Zealand, and Scotland are attempting to interact with well-being goals in education. There is an emphasis on teachers’ agency and students’ overall school-based accomplishments. The intention is to allow teachers to use their professional autonomy to create more comprehensive learning experiences for their pupils.

In a systematic review focused on the protective variables for health and well-being throughout medical education, Kötter et al. (2019) found a considerable variability of potential predictors, with few consistent. However, long-term coping techniques that involve all groups of students as active learners assist them in maintaining their well-being.

Summarizing, despite the various uses and coexistence of different active learning methodologies, the authors are unanimous in recognizing the benefits of student-centered approaches, providing them with experiences that go beyond the class. Learning, according to Sfards ’ ( 1998 ) conception, needs to be interpreted not only as acquisition, but also as an experience, where learners become active constructors of their learning. Acquiring academic specific knowledge is crucial, however, for that, students need to develop meta-competencies, such as critical thinking, problem-solving ability, and strategies for self-development. In addition, the pandemic situation, with the transition to online teaching, has brought even more emphasis to the need for higher education pedagogy centered on active learning methodologies, in which the teacher educator supports students in the construction of their knowledge and in the social and emotional well-being.

Materials and Methods

The procedures for this review were guided by Petticrew and Roberts ’ ( 2006 ) guidelines and the component of the previous studies (present at point 2.1) from of the suggestions of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis ( Page et al., 2021 ) was integrated. The stepwise approach used encompasses (i) formulating search items, (ii) selecting databases, (iii) conducting literature search, (iv) formulating inclusion criteria and applying these criteria to elected relevant literature, and (v) data extraction.

The formulation of search terms and eligibility criteria for searching relevant databases was meant to focus on identifying the most pertinent retrievals related to higher education, well-being, and active learning. The authors believed it was timely to review the interim period from 2015 to 2021, given the milestone that represents the approval in 2015 of the 2030 Agenda for Sustainable Development, in order to understand the sensitivity of higher education institutions to that agenda through the studies developed during that time period. The retrieved studies were considered relevant and included in this review when convincingly connected higher education teaching and student-centered approaches.

Databases and Search Terms

Two researchers independently conducted the search in two databases — Web of Science main collection and SCOPUS — using the following search criteria: “higher education AND active learning AND student AND wellness OR well-being OR wellbeing” in all fields. These databases were selected because they contain many of the leading publishers of scientific journals and worldwide databases most relevant to educational research.

Eligibility Criteria

The review’s eligibility required empirical studies in open access full text, written in languages understood by the authors (English, Portuguese, French, or Spanish), published in peer-reviewed academic journals, and limited to the period of 2015 to 2021 in open access. The inclusion criteria also required articles that report methodologies, and strategies used in higher education not only to promote university students’ learning but also well-being.

Selection of Articles and Descriptive Overview

Two of the authors independently conducted the identification of articles provided by the database searches in September 2021 to ensure consensus on relevant articles. The process of screening articles began with a close examination of each of the 42 references (24 from Scopus and 18 from Web of Science) retrieved by the database search (informed by the four specific search terms and the eligibility criteria). Abstracts from the 42 articles were reviewed by the same two authors. Each author independently determined which articles were relevant to the review before comparing data and analyzing any inconsistencies. Both authors agreed that 10 articles were eligible.

From Scopus were excluded 18 articles after analyzing each of them: 11 because they deviated from the focus of this review on the topic itself, five because the participants were not from higher education, and two because they did not present empirical studies in their methodology. This analysis resulted in six final articles included.

As for Web of Science, 14 articles were excluded from the 18 initial articles, of which one was not available, two were duplicated, six due to the participant profile (students not from higher education), three because the theme did not meet the focus for this review, and two for not presenting an empirical study, resulting in four final articles included.

This resulted in a total of 10 articles being considered relevant to the review. The process of selection of references for review and for the previous studies is summarized in Figure 1 , according to the three phases (identification, screening and included).

Data Extraction and Data Analysis

The 10 articles were examined through an inductive thematic analysis ( Braun and Clarke, 2012 ). Four steps were engaged in the theme analysis process: (i) reading each article and noting the main conclusions; (ii) compiling the numerous conclusions of each article in a Word document; (iii) labeling the conclusions of each article with preliminary codes before grouping them into more generic topics; and (iv) organizing the more generic topics into themes. Following the coding of each article’s conclusions, initial codes were iteratively grouped into general subjects and discussed among the authors, culminating in the identification of two themes: (1) students’ well-being during confinement; (2) methodological solutions for students’ well-being.

Regarding the year of publication, Scopus has three articles from 2021, two from 2020, and one from 2019, whereas Web of Science has one from 2021, two from 2020, and one from 2018. Taking both databases into account, four papers were published in 2021, four in 2020, one in 2019, and one in 2018. All the articles are written in English and any document from 2015 to 2017 fulfills our set of inclusion requirements.

The investigations were conducted in seven countries: three in Spain ( Díaz-Iso et al., 2020 ; Fernández et al., 2020 ; Luque-Suárez et al., 2021 ), two in the United Kingdom ( Mayew et al., 2020 ; Defeyter et al., 2021 ), and one in Germany ( Paulus et al., 2021 ), Taiwan ( Dutta et al., 2021 ), Israel ( Martin et al., 2020 ), the United States of America ( Vatovec and Ferrer, 2019 ), and Sweden ( Bälter et al., 2018 ). Most studies included students in higher education (as it was an inclusion criterion), however, Bälter et al.’s (2018) investigation included also nine teachers. Those students’ (and teachers’) scientific fields are distinct such as medicine ( Martin et al., 2020 ), engineering ( Bälter et al., 2018 ; Paulus et al., 2021 ), education ( Luque-Suárez et al., 2021 ), human health and the environment ( Vatovec and Ferrer, 2019 ). The remainder studies did not specify the scientific areas of the participants but stated that they belong to various areas, colleges, or universities.

Six of the authors’ approaches were fundamentally quantitative, with questionnaire searching as data collecting instrument ( Fernández et al., 2020 ; Mayew et al., 2020 ; Defeyter et al., 2021 ; Dutta et al., 2021 ; Luque-Suárez et al., 2021 ; Paulus et al., 2021 ), and four employed a mixed methods approach, where other instruments were used alongside the questionnaires, such as interviews ( Bälter et al., 2018 ; Díaz-Iso et al., 2020 ), life experiences ( Martin et al., 2020 ), and intervention reports ( Vatovec and Ferrer, 2019 ). Table 1 displays the results and provides an overview of the research found.

Table 1. Articles included in the literary review.

In view of the results obtained, we can observe that four of the five articles from 2021 (the year with the most publications) focus on the well-being of students, particularly mental and emotional, during their confinement due to COVID-19, as does one of the four articles from 2020. The discussions will be structured according to the two main themes identified: (i) students’ well-being during confinement; and (ii) methodological solutions for students’ well-being.

The study main purpose was to look at how active learning in higher education might affect students’ well-being. This literature review focused on the use of active learning methodologies in higher education in order to understand the sensitivity of higher education institutions to the 2030 Sustainable Development Agenda. The studies included in this review pointed that students’ well-being was an issue under study, considered during confinement period and in relation of diferents active methodologies used by university teachers.

Students’ Well-Being During Confinement

Fernández et al. (2020) discovered that developing a virtual communicative relationship was a way to reduce emotions of loneliness or social isolation. This similar assumption, about the favorable effects of online classes on student well-being, is evident in a Theelen et al. ’s ( 2019 ) study with preservice teachers. According to the authors, computer-based classroom simulations offer a safe approach for preservice teachers to practice and develop their interpersonal competency, which contributes to their well-being. This teacher-student centered strategy assists them in overcoming issues with classroom management and teacher-student interpersonal interaction.

Dutta et al. (2021) also looked at how Taiwanese students used digital technologies during confinement, focusing on what they called a “sustainable cloud-base e-learning system,” which defined a learning configuration that included data and communications and allowed for the creation and execution of innovation within an e-learning system. The findings highlight that self-efficacy has a significant impact on students’ predisposition to utilize or not use ‘digital cloud technologies’ functioning as a facilitator of student behavior.

However, as Thorburn (2020) points out, public policies in England, Australia, New Zealand, and Scotland are attempting to interact with well-being goals in education. There is an emphasis on instructors, agency, and students, as well as broader school-based accomplishments. The goal is to provide instructors greater professional autonomy so they may create more comprehensive learning experiences for their pupils. In the same context, Defeyter et al. (2021) attempted to understand why some English students showed low levels of mental well-being during confinement, with the findings indicating that the lack of confidence in the performance of their respective universities and governments in the face of ecological disasters has an impact on their mental well-being, as it transmits a sense of insecurity and uneasiness.

With an identical intention, Luque-Suárez et al. (2021) focused on ways to overcome the mental and emotional distress caused by the first confinement of Spanish students, defending the voluntary work performed by those students as a very positive way to find them again. Promoting emotional well-being improves self-esteem and people’s lives by restoring emotional balance and coping with feelings of depression or isolation. Simultaneously, the shortage of employment, which existed previous to the pandemic but has been exacerbated by it, leads Spanish students to regard volunteering as a method to get into the labor market.

In summary, these four studies looked at the effects of confinement on university students’ mental and emotional well-being, attempting to understand the causes and methods for maintaining well-being ( Fernández et al., 2020 ; Defeyter et al., 2021 ), confirming the role of digital technologies in their daily lives ( Dutta et al., 2021 ), and compensating for the wear and tear of that period through the development of voluntary work ( Luque-Suárez et al., 2021 ).

Despite their undeniable interest, these studies seem to be discrete pedagogical experiences conducted by groups of researchers and focusing on extremely specific features, rather than strategic and anchored goals of higher education institutions.

Given that the pandemic has increased the risk of public mental health problems ( Zhang et al., 2020 ) and that approximately 30 million university students worldwide have had to transition from traditional learning to virtual learning ( Wang and Zhao, 2020 ; Hermassi et al., 2021 ), it would be expected to investigate and expand teaching through active learning methodologies in higher education, in which students’ autonomy and decision-making capacity, but also cooperation and experience sharing, are the focus. Learning using these approaches would lessen the sense of malaise caused by isolation (or even loneliness) and ambiguity of the situation, since they transform into a contextualized and self-responsible learning process that takes into account each individual’s skills and restrictions.

Methodological Approaches for Students’ Well-Being

This is where we find the least recent articles, with just one from 2021, three from 2020, one from 2019, and one from 2018. In general, they are simple research documenting active approaches used in higher education. The main goal is to empower students given their academic accomplishment, critical thinking skills, and social-emotional and contribute to their well-being ( Akinla et al., 2018 ; van der Zanden et al., 2018 ). In studies with German and Swedish students, Bälter et al. (2018) and Paulus et al. (2021) concluded that a sedentary lifestyle is detrimental to higher education students’ commitments and results, proposing breaks in academic activities with moments of physical activity ( Paulus et al., 2021 ), and holding seminars outside ( Bälter et al., 2018 ), an idea that we had already found in the review study by van der Zanden et al. (2018) . These results put in evidence the necessity to include well-being in educational policies in schools. Matthews et al. (2015) defends that society needs to take attention to students’ mental, emotional, social, and physical state.

Martin et al. (2020) found that discussing vulnerable situations that occurred throughout the professional life of experienced doctors with Israeli medical students had a highly good effect on the latter’s well-being, making them realize that there is room for failure. This concept of near-peer mentoring as a means of promoting personal and professional development and social integration of students had already emerged in the review study by van der Zanden et al. (2018) , namely in the transition to higher education ( Akinla et al., 2018 ). Near-peer mentoring, in addition to being a valuable resource in providing social and academic support to new students, also helps to facilitate transition and a reduction in stress levels.

The studies by Díaz-Iso et al. (2020) , with Spanish students, and Mayew et al. (2020) , with English students, focus on well-being through a sense of social integration resulting from the interrelationship promoted by extracurricular volunteering ( Díaz-Iso et al., 2020 ), and from the use of a digital platform. Communication between students becomes (more) inclusive as a result ( Mayew et al., 2020 ) due to the use of that digital platform that has the potential to give a voice to less interventionist pupils for whatever reason, whether gender, culture, disability, or other.

Finally, in a broader ecological context, Vatovec and Ferrer (2019) determined that students’ well-being is directly related to their perceptions of the ecological sustainability of each activity they engage in.

Attempting to link the research based on the methods used or advised to attain well-being, three articles link the feeling of well-being of students with the usage of (new) digital technologies ( Fernández et al., 2020 ; Mayew et al., 2020 ; Dutta et al., 2021 ); in two of them, well-being is associated with the practice of physical activity as a way to counteract a sedentary lifestyle, which is considered detrimental to individuals’ well-being ( Bälter et al., 2018 ; Paulus et al., 2021 ); and in two others, it is associated with the performance of volunteer work ( Díaz-Iso et al., 2020 ; Luque-Suárez et al., 2021 ). According to Thorburn (2020) , this kind of methodologies emphasize not only teacher’s agency but also students’ agency. In this way teachers can use their professional autonomy to create more comprehensive learning experiences for their pupils.

Seeking to recognize active methodological forms in higher education, it appears that those that incorporate some practice of physical activity to balance sedentary life, and those that carry out some volunteer practice greatly favor the well-being of students, whether in their academic, professional, physical, emotional, and social perspectives. Regardless of the approaches identified as possible predictors of student well-being, the consistency is low. Long-term coping techniques that involve all groups of students as active learners assist them in maintaining their well-being.

Even in a time of pandemic and subsequent confinement, it was expected that teaching through active methodologies, which were translated into work proposals that implied communication and cooperation between teachers and students, would have increased in an attempt to alleviate the isolation in which everyone was and controlling the harmful emotional effects. However, while there was some concern for the students’ well-being, it was restricted to pedagogical experiences and/or focused on instrument use.

So, despite these results, and although some countries (and some higher education institutions) are attempting to integrate well-being goals into education, as seen in England, Australia, New Zealand and Scotland ( Thorburn, 2020 ), the global picture is not encouraging, given the 193 United Nations members who have signed the Organization’s 2030 Agenda for Sustainable Development. This was clear when we identified that, since the signing of the 2030 Agenda for Sustainable Development, no study from 2015, 2016, or 2017 was found in the two databases used, and just one in each of the subsequent years (2018 and 2019). Despite the inclusion criteria including French, Spanish and Portuguese, only articles in English were found. This brings us back to Batista et al. (2021) , who state that when we try to critically evaluate the work performed by universities in regard to their commitment to society, we find nothing promising beyond simple declarations of intent.

Higher education institution policy is a key impediment to social innovation and preserving ethical consistency between what is claimed and done in vocational training and what is actually done in reality of classrooms in higher education. In this regard, structural changes at both macro (institutional) and micro (educational practice) levels are required to lead to an understanding of the value of active learning methodologies in sustained education, because they are situated, meaningful, and contextualized, making the most of resources with view to multi-competence learning, which forms citizens with rights ( Vallaeys, 2021 ). According to Thorburn (2020) , the lack of consistency between national and international policies on teaching for well-being hampers the role of schools and teachers, who are tasked with juggling a plethora of tasks. Simultaneously, they must prepare how to include and respond to the new imperatives of personal well-being policies. In light of this, Thorburn (2020) argues that it is critical to devise a feasible plan for improving students’ progress and allowing teachers to make greater use of their professional autonomy.

Notwithstanding the modest amount of studies found, the results show that the use of active learning methodologies (in and out of class) in higher education positively impacts students’ well-being, particularly, in their academic accomplishment, physical, emotional, and social lives, and to equip them with multi-competencies for their professional future. Nevertheless, there is some alienation or even lack of interest on this subject from the scientific community and, eventually, from higher education institutions themselves.

Concerning the understanding of higher education institutions to the 2030 Agenda, where universal literacy is aspired through equitable and universal access to quality education at all levels, to health care and social protection, and where physical, mental, and social well-being are assured. However, that sensitivity is still tenuous and interpreted in a very limited and geographically circumscribed way. Despite the Agenda’s recruitment of all social sectors, including the scientific and academic community, and its appeal to transparent, effective, and accountable institutions, it appears to us that universities are slow to recognize the significance of their role in this entire process, as well as the ways to play it.

Furthermore, with the massive global public health problem that we have been experiencing since the beginning of 2020, it was expected that the panorama would change significantly. Given the gravity of the situation, we cannot consider the ten articles discovered as a good number, even when the results show that the majority of the objectives of the studies pursue things that are instrumentally good for us, converging to an essentially objective interpretation of well-being. This number is one of the study’s limitations, as is the fact that it only represents seven countries, of which, five from the European Continent, one from North America, and one from Asia. This reveals a lack of sensitivity to the study of student well-being while using active learning methodologies, whatever its interpretation, in socially disadvantaged regions or countries. We believe that investing in research on this topic in teams that mobilize different higher education institutions, or even international scientific networks that may include institutions and researchers from different continents, will be one way to overcome this limitation, allowing not only the geographical expansion of research, but also broader interpretations of well-being.

Finally, given the importance of this topic, we believe that in future studies, the number of research databases should be expanded beyond Scopus and Web of Science, allowing for an increase in the number of articles as well as a greater diversity and representativeness of other countries or regions.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

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

The reviewer CS-G declared a shared affiliation, with no collaboration, with one of the authors, JA-H to the handling editor at the time of the review.

Publisher’s Note

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

Acknowledgments

We thank the Faculty of Sport Sciences and Physical Education of the University of Coimbra for having financially supported this publication.

• ^ The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesize studies. The structure and presentation of the Prisma have been modified to facilitate implementation (see Page et al., 2021 ). Besides the identification of studies in databases, the new PRISMA offers the possibility to include previous review studies and identification of other studies using other tools as websites, organizations, and manual search.

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Kötter, T., Fuchs, S., Heise, M., Riemenschneider, H., Sanftenberg, L., Vajda, C., et al. (2019). What keeps medical students healthy and well? A systematic review of observational studies on protective factors for health and well-being during medical education. BMC Med. Educ. 19:94. doi: 10.1186/s12909-019-1532-z

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Luque-Suárez, M., Olmos-Gómez, M. C., Castán-García, M., and Portillo-Sánchez, R. (2021). Promoting emotional and social well-being and a sense of belonging in adolescents through participation in volunteering. Healthcare 9:359. doi: 10.3390/healthcare9030359

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Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:71. doi: 10.1136/bmj.n71

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Keywords : sustainability, active methodologies, university, well-being, review

Citation: Ribeiro-Silva E, Amorim C, Aparicio-Herguedas JL and Batista P (2022) Trends of Active Learning in Higher Education and Students’ Well-Being: A Literature Review. Front. Psychol. 13:844236. doi: 10.3389/fpsyg.2022.844236

Received: 27 December 2021; Accepted: 14 February 2022; Published: 18 April 2022.

Reviewed by:

Copyright © 2022 Ribeiro-Silva, Amorim, Aparicio-Herguedas and Batista. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Elsa Ribeiro-Silva, [email protected]

† These authors have contributed equally to this work

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

Strategies of active learning methodologies in nursing education: an integrative literature review

Affiliation.

• 1 Faculdade de Medicina de Marília. Marília, São Paulo, Brazil.
• PMID: 33787786
• DOI: 10.1590/0034-7167-2020-0130

Objectives: to analyze the scientific evidence on the strategies of active learning methodologies used in the training of nurses, as well as their contributions and obstacles in training.

Methods: integrative literature review conducted with 33 selected articles in the Medical Literature Analysis and Retrieval System Online, Latin American and Caribbean Literature in Health Sciences, Nursing Database, Scopus, Web of Science and Education Resources Information Center databases.

Results: among the strategies, simulation, problem-based learning and flipped classroom were highlighted. The active search, the integration of theory and practice and group work were examples of contributions to the training of nurses. However, the lack of preparation of the actors and the lack of structural support contribute to the dissatisfaction of the students.

Final considerations: the active learning methodology places the student at the heart of the learning process, favors critical thinking and the ability to make decisions.

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Bishop, C., Caston, M., & King, C. (2014). Learner-centered environments: Creating effective strategies based on student attitudes and faculty reflection. Journal of the Scholarship of Teaching and Learning, 14(3), 46-63. doi:  http://dx.doi.org/10.14434/josotl.v14i3.5065

“Abstract: Learner-centered environments effectively implement multiple teaching techniques to enhance students’ higher education experience and provide them with greater control over their academic learning. This qualitative study involves an exploration of the eight reasons for learner-centered teaching found in Terry Doyle’s 2008 book, Helping Students Learn in a Learner Centered Environment. Doyle’s principles were investigated through the use of surveys, student focus group interviews, and faculty discussions to discover a deeper understanding of the effects a “learner-centered” teaching environment has on long term learning in comparison to a “teacher-centered” learning environment. These data revealed five primary themes pertaining to student resistance to learner-centered environments. The results assisted in the development of strategies educators can adopt for creating a successful learner-centered classroom.”

Doyle, T., & Zakrajsek, T. (2013). The new science of learning: how to learn in harmony with your brain. First edition. Sterling, Virginia: Stylus.

Todd Zakrajsek is a highly published and sought after speaker in higher education and the Director of the Lilly Conferences for Evidence Based Teaching and Learning in higher education. He is also past Executive Director of the Center for Faculty Excellence at University of North Carolina at Chapel Hill. The New Science summarizes the neuroscience that supports active learning.

Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223-231. Retrieved from  http://proxy.lib.umich.edu/login?url=http://search.proquest.com/docview/217960253?accountid=14667

This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active, collaborative, cooperative, and problem-based learning.

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Anderson, L. W. (2001). A Taxonomy for learning, teaching, and assessing: a revision of Bloom's Taxonomy of educational objectives. Complete ed. New York: Longman.

A Taxonomy describes and classifies learning based on Bloom’s Taxonomy (1956) and provides insights for teaching and assessing.The text is written to guide teachers as they plan and prepare lessons and assessments to meet learning objectives.

Bransford, J. (2000). How people learn. Expanded ed. Washington,  D.C.: National Academy Press.

How People Learn offers an overview of the research in cognitive science that explains how people learn. Chapter 1 provides the foundational knowledge that supports the practice of active learning.

Design Process

Barkley, E. F. (2010). Student engagement techniques: a handbook for college faculty. San Francisco: Jossey-Bass.

Student Engagement Techniques is a heavily relied upon resources in many schools, and it is referenced often in literature and at conferences. This book provides practical activities intended to involve students in active learning. The book also offers a conceptual framework for understanding student engagement.

Weimer, M. (2013). Learner-Centered Teaching : Five key changes to practice. 2nd ed. San Francisco: Jossey-Bass.

MaryEllen Weimer provides articles and workshops for Magna Publications and Faculty Focus. MaryEllen is a leader in the field of effective teaching and learning in higher education. Her books provide concrete strategies and practices supported by learning theory. She focuses on the function of content (lower order skills outside of class; active learning in class) and the responsibility for learning (on learner). Her work also has an emphasis on assessment and classroom management.

Wiggins, G. P.., McTighe, J. (2005). Understanding by design. Expanded 2nd ed. Alexandria, VA: Association for Supervision and Curriculum Development.

Understanding by Design is a design framework developed by Grant Wiggins and Jay McTighe. The process is reflected in the Academic Technology Services planning guide. The “backward” design process is widely accepted and taught in schools of education.

Active Learning in Higher Education, Sage Publishing

Active Learning in Higher Education is an international, refereed publication for all those who teach and support learning in Higher Education and those who undertake or use research into effective learning, teaching and assessment in universities and colleges. The journal has an objective of improving the status of teaching and learning support as professional activity and embraces academic practice across all curriculum areas in higher education.

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Active Blended Learning: Definition, Literature Review, and a Framework for Implementation

Introduction.

With the growing use of technologies in educational interventions, approaches to learning and teaching have evolved to take place in different environments with a variety of strategies and techniques. Blended learning programmes have thus become pervasive within academic institutions (Adams Becker et al., 2017; Sharples et al., 2014). As these courses cater to a wide range of needs and lifestyles, they represent an attractive option for both traditional and non-traditional learners (Waha & Davis, 2014). Although researchers have largely reported non-significant differences, particularly in terms of student outcomes and satisfaction, blended learning courses have been found to be as effective or better overall than similar ones in other modes of study (Liu et al., 2016; Stockwell et al., 2015). Comparative studies often attempt to replicate teaching practices in face-to-face, blended and online settings. However, the combination of curriculum materials, pedagogy and learning time seems to create the real advantages (Means et al., 2010). The most effective blended courses enable students to learn in ways not feasible in other formats (Adams Becker et al., 2017).

Active learning is particularly useful for achieving a successful and rewarding educational experience. It can result in fewer failing students, higher performance in examinations (Freeman et al., 2014), enhanced problem-solving skills (Hake, 1998), critical thinking (Shin et al., 2014), increased attendance and learner satisfaction (Lumpkin et al., 2015; Stockwell et al., 2015). It can also reduce the attainment gap between disadvantaged and non-disadvantaged students (Haak et al., 2011). The move towards active learning makes classrooms resemble real-world work and social settings that foster cross-disciplinary interactions (Adams Becker et al., 2017). Students perceive that active classrooms promote creativity and innovation (Chiu & Cheng, 2016). When learners participate in active learning environments, they tend to outperform their peers in more traditional classroom settings (Cotner et al., 2013).

This chapter focuses on the joint implementation of blended and active learning to maximise the benefits of both approaches in higher education settings. We addressed three main areas:

Institutional definitions . We analysed the information available on public-facing university websites to establish a starting point for the study of these approaches.

Academic literature . We systematically reviewed the literature on active blended learning (ABL) published in indexed, peer-reviewed journals up to June 2020 to identify trends and patterns.

Framework for active blended learning . We present and describe an evidence-based framework to guide and scale up the implementation of ABL in higher education.

Institutional Definitions

Despite its widespread usage, it is surprisingly difficult to find a universal definition of blended learning. In their review of 97 articles relating to blended learning in higher education, Smith and Hill (2019) reported a lack of consistency and clarity in the literature. Perhaps the only consensus relates to the combination of online and face-to-face elements (e.g., Garrison & Vaughan, 2008). The nature of these components remains ambiguous, and could relate to content availability, teaching strategies, learning opportunities or social interactions. Thus, descriptions of blended learning can refer to the ratio between web-based and traditional provision (Allen et al., 2007; Sener, 2015), the delivery methods (Clayton Christensen Institute, 2017; Kim, 2017) or the pedagogy (Freeman Herreid & Schiller, 2013; Mapstone et al., 2014). This variance complicates the development of research and practice, emphasising the need for shared understandings (Smith & Hill, 2019).

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Title: a survey of deep active learning.

Abstract: Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

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Protocol for a scoping review study on learning plan use in undergraduate medical education

• Anna Romanova   ORCID: orcid.org/0000-0003-1118-1604 1 ,
• Claire Touchie 1 ,
• Sydney Ruller 2 ,
• Victoria Cole 3 &
• Susan Humphrey-Murto 4  

Systematic Reviews volume  13 , Article number:  131 ( 2024 ) Cite this article

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The current paradigm of competency-based medical education and learner-centredness requires learners to take an active role in their training. However, deliberate and planned continual assessment and performance improvement is hindered by the fragmented nature of many medical training programs. Attempts to bridge this continuity gap between supervision and feedback through learner handover have been controversial. Learning plans are an alternate educational tool that helps trainees identify their learning needs and facilitate longitudinal assessment by providing supervisors with a roadmap of their goals. Informed by self-regulated learning theory, learning plans may be the answer to track trainees’ progress along their learning trajectory. The purpose of this study is to summarise the literature regarding learning plan use specifically in undergraduate medical education and explore the student’s role in all stages of learning plan development and implementation.

Following Arksey and O’Malley’s framework, a scoping review will be conducted to explore the use of learning plans in undergraduate medical education. Literature searches will be conducted using multiple databases by a librarian with expertise in scoping reviews. Through an iterative process, inclusion and exclusion criteria will be developed and a data extraction form refined. Data will be analysed using quantitative and qualitative content analyses.

By summarising the literature on learning plan use in undergraduate medical education, this study aims to better understand how to support self-regulated learning in undergraduate medical education. The results from this project will inform future scholarly work in competency-based medical education at the undergraduate level and have implications for improving feedback and supporting learners at all levels of competence.

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Open Science Framework osf.io/wvzbx.

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Competency-based medical education (CBME) has transformed the approach to medical education to focus on demonstration of acquired competencies rather than time-based completion of rotations [ 1 ]. As a result, undergraduate and graduate medical training programs worldwide have adopted outcomes-based assessments in the form of entrustable professional activities (EPAs) comprised of competencies to be met [ 2 ]. These assessments are completed longitudinally by multiple different evaluators to generate an overall impression of a learner’s competency.

In CBME, trainees will progress along their learning trajectory at individual speeds and some may excel while others struggle to achieve the required knowledge, skills or attitudes. Therefore, deliberate and planned continual assessment and performance improvement is required. However, due to the fragmented nature of many medical training programs where learners rotate through different rotations and work with many supervisors, longitudinal observation is similarly fragmented. This makes it difficult to determine where trainees are on their learning trajectories and can affect the quality of feedback provided to them, which is a known major influencer of academic achievement [ 3 ]. As a result, struggling learners may not be identified until late in their training and the growth of high-performing learners may be stifled [ 4 , 5 , 6 ].

Bridging this continuity gap between supervision and feedback through some form of learner handover or forward feeding has been debated since the 1970s and continues to this day [ 5 , 7 , 8 , 9 , 10 , 11 ]. The goal of learner handover is to improve trainee assessment and feedback by sharing their performance and learning needs between supervisors or across rotations. However, several concerns have been raised about this approach including that it could inappropriately bias subsequent assessments of the learner’s abilities [ 9 , 11 , 12 ]. A different approach to keeping track of trainees’ learning goals and progress along their learning trajectories is required. Learning plans (LPs) informed by self-regulated learning (SRL) theory may be the answer.

SRL has been defined as a cyclical process where learners actively control their thoughts, actions and motivation to achieve their goals [ 13 ]. Several models of SRL exist but all entail that the trainee is responsible for setting, planning, executing, monitoring and reflecting on their learning goals [ 13 ]. According to Zimmerman’s SRL model, this process occurs in three stages: forethought phase before an activity, performance phase during an activity and self-reflection phase after an activity [ 13 ]. Since each trainee leads their own learning process and has an individual trajectory towards competence, this theory relates well to the CBME paradigm which is grounded in learner-centredness [ 1 ]. However, we know that medical students and residents have difficulty identifying their own learning goals and therefore need guidance to effectively partake in SRL [ 14 , 15 , 16 , 17 ]. Motivation has also emerged as a key component of SRL, and numerous studies have explored factors that influence student engagement in learning [ 18 , 19 ]. In addition to meeting their basic psychological needs of autonomy, relatedness and competence, perceived learning relevance through meaningful learning activities has been shown to increase trainee engagement in their learning [ 19 ].

LPs are a well-known tool across many educational fields including CBME that can provide trainees with meaningful learning activities since they help them direct their own learning goals in a guided fashion [ 20 ]. Also known as personal learning plans, learning contracts, personal action plans, personal development plans, and learning goals, LPs are documents that outline the learner’s roadmap to achieve their learning goals. They require the learner to self-identify what they need to learn and why, how they are going to do it, how they will know when they are finished, define the timeframe for goal achievement and assess the impact of their learning [ 20 ]. In so doing, LPs give more autonomy to the learner and facilitate objective and targeted feedback from supervisors. This approach has been described as “most congruent with the assumptions we make about adults as learners” [ 21 ].

LP use has been explored across various clinical settings and at all levels of medical education; however, most of the experience lies in postgraduate medical education [ 22 ]. Medical students are a unique learner population with learning needs that appear to be very well suited for using LPs for two main reasons. First, their education is often divided between classroom and clinical settings. During clinical training, students need to be more independent in setting learning goals to meet desired competencies as their education is no longer outlined for them in a detailed fashion by the medical school curriculum [ 23 ]. SRL in the workplace is also different than in the classroom due to additional complexities of clinical care that can impact students’ ability to self-regulate their learning [ 24 ]. Second, although most medical trainees have difficulty with goal setting, medical students in particular need more guidance compared to residents due to their relative lack of experience upon which they can build within the SRL framework [ 25 ]. LPs can therefore provide much-needed structure to their learning but should be guided by an experienced tutor to be effective [ 15 , 24 ].

LPs fit well within the learner-centred educational framework of CBME by helping trainees identify their learning needs and facilitating longitudinal assessment by providing supervisors with a roadmap of their goals. In so doing, they can address current issues with learner handover and identification as well as remediation of struggling learners. Moreover, they have the potential to help trainees develop lifelong skills with respect to continuing professional development after graduation which is required by many medical licensing bodies.

An initial search of the JBI Database, Cochrane Database, MEDLINE (PubMed) and Google Scholar conducted in July–August 2022 revealed a paucity of research on LP use in undergraduate medical education (UGME). A related systematic review by van Houten–Schat et al. [ 24 ] on SRL in the clinical setting identified three interventions used by medical students and residents in SRL—coaching, LPs and supportive tools. However, only a couple of the included studies looked specifically at medical students’ use of LPs, so this remains an area in need of more exploration. A scoping review would provide an excellent starting point to map the body of literature on this topic.

The objective of this scoping review will therefore be to explore LP use in UGME. In doing so, it will address a gap in knowledge and help determine additional areas for research.

This study will follow Arksey and O’Malley’s [ 26 ] five-step framework for scoping review methodology. It will not include the optional sixth step which entails stakeholder consultation as relevant stakeholders will be intentionally included in the research team (a member of UGME leadership, a medical student and a first-year resident).

Step 1—Identifying the research question

The overarching purpose of this study is to “explore the use of LPs in UGME”. More specifically we seek to achieve the following:

Summarise the literature regarding the use of LPs in UGME (including context, students targeted, frameworks used)

Explore the role of the student in all stages of the LP development and implementation

Determine existing research gaps

Step 2—Identifying relevant studies

An experienced health sciences librarian (VC) will conduct all searches and develop the initial search strategy. The preliminary search strategy is shown in Appendix A (see Additional file 2). Articles will be included if they meet the following criteria [ 27 ]:

Participants

Medical students enrolled at a medical school at the undergraduate level.

Any use of LPs by medical students. LPs are defined as a document, usually presented in a table format, that outlines the learner’s roadmap to achieve their learning goals [ 20 ].

Any stage of UGME in any geographic setting.

Types of evidence sources

We will search existing published and unpublished (grey) literature. This may include research studies, reviews, or expert opinion pieces.

Search strategy

With the assistance of an experienced librarian (VC), a pilot search will be conducted to inform the final search strategy. A search will be conducted in the following electronic databases: MEDLINE, Embase, Education Source, APA PsycInfo and Web of Science. The search terms will be developed in consultation with the research team and librarian. The search strategy will proceed according to the JBI Manual for Evidence Synthesis three-step search strategy for reviews [ 27 ]. First, we will conduct a limited search in two appropriate online databases and analyse text words from the title, abstracts and index terms of relevant papers. Next, we will conduct a second search using all identified key words in all databases. Third, we will review reference lists of all included studies to identify further relevant studies to include in the review. We will also contact the authors of relevant papers for further information if required. This will be an iterative process as the research team becomes more familiar with the literature and will be guided by the librarian. Any modifications to the search strategy as it evolves will be described in the scoping review report. As a measure of rigour, the search strategy will be peer-reviewed by another librarian using the PRESS checklist [ 28 ]. No language or date limits will be applied.

Step 3—Study selection

The screening process will consist of a two-step approach: screening titles/abstracts and, if they meet inclusion criteria, this will be followed by a full-text review. All screening will be done by two members of the research team and any disagreements will be resolved by an independent third member of the team. Based on preliminary inclusion criteria, the whole research team will first pilot the screening process by reviewing a random sample of 25 titles/abstracts. The search strategy, eligibility criteria and study objectives will be refined in an iterative process. We anticipate several meetings as the topic is not well described in the literature. A flowchart of the review process will be generated. Any modifications to the study selection process will be described in the scoping review report. The papers will be excluded if a full text is not available. The search results will be managed using Covidence software.

Step 4—Charting the data

A preliminary data extraction tool is shown in Appendix B (see Additional file 3 ). Data will be extracted into Excel and will include demographic information and specific details about the population, concept, context, study methods and outcomes as they relate to the scoping review objectives. The whole research team will pilot the data extraction tool on ten articles selected for full-text review. Through an iterative process, the final data extraction form will be refined. Subsequently, two members of the team will independently extract data from all articles included for full-text review using this tool. Charting disagreements will be resolved by the principal and senior investigators. Google Translate will be used for any included articles that are not in the English language.

Step 5—Collating, summarising and reporting the results

Quantitative and qualitative analyses will be used to summarise the results. Quantitative analysis will capture descriptive statistics with details about the population, concept, context, study methods and outcomes being examined in this scoping review. Qualitative content analysis will enable interpretation of text data through the systematic classification process of coding and identifying themes and patterns [ 29 ]. Several team meetings will be held to review potential themes to ensure an accurate representation of the data. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) will be used to guide the reporting of review findings [ 30 ]. Data will be presented in tables and/or diagrams as applicable. A descriptive summary will explain the presented results and how they relate to the scoping review objectives.

By summarising the literature on LP use in UGME, this study will contribute to a better understanding of how to support SRL amongst medical students. The results from this project will also inform future scholarly work in CBME at the undergraduate level and have implications for improving feedback as well as supporting learners at all levels of competence. In doing so, this study may have practical applications by informing learning plan incorporation into CBME-based curricula.

We do not anticipate any practical or operational issues at this time. We assembled a team with the necessary expertise and tools to complete this project.

Availability of data and materials

All data generated or analysed during this study will be included in the published scoping review article.

Abbreviations

• Competency-based medical education

Entrustable professional activity

• Learning plan
• Self-regulated learning
• Undergraduate medical education

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Acknowledgements

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This study will be supported through grants from the Department of Medicine at the Ottawa Hospital and the University of Ottawa. The funding bodies had no role in the study design and will not have any role in the collection, analysis and interpretation of data or writing of the manuscript.

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Contributions

AR designed and drafted the protocol. CT and SH contributed to the refinement of the research question, study methods and editing of the manuscript. VC designed the initial search strategy. All authors reviewed the manuscript for final approval. The review guarantors are CT and SH. The corresponding author is AR.

Authors’ information

AR is a clinician teacher and Assistant Professor with the Division of General Internal Medicine at the University of Ottawa. She is also the Associate Director for the internal medicine clerkship rotation at the General campus of the Ottawa Hospital.

CT is a Professor of Medicine with the Divisions of General Internal Medicine and Infectious Diseases at the University of Ottawa. She is also a member of the UGME Competence Committee at the University of Ottawa and an advisor for the development of a new school of medicine at Toronto Metropolitan University.

SH is an Associate Professor with the Department of Medicine at the University of Ottawa and holds a Tier 2 Research Chair in Medical Education. She is also the Interim Director for the Research Support Unit within the Department of Innovation in Medical Education at the University of Ottawa.

CT and SH have extensive experience with medical education research and have numerous publications in this field.

SR is a Research Assistant with the Division of General Internal Medicine at the Ottawa Hospital Research Institute.

VC is a Health Sciences Research Librarian at the University of Ottawa.

SR and VC have extensive experience in systematic and scoping reviews.

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Supplementary Information

Additional file 1. prisma-p 2015 checklist., 13643_2024_2553_moesm2_esm.docx.

Additional file 2: Appendix A. Preliminary search strategy [ 31 ].

Additional file 3: Appendix B. Preliminary data extraction tool.

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Romanova, A., Touchie, C., Ruller, S. et al. Protocol for a scoping review study on learning plan use in undergraduate medical education. Syst Rev 13 , 131 (2024). https://doi.org/10.1186/s13643-024-02553-w

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• Published: 15 May 2024

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research

• Bárbara B. Mendes   ORCID: orcid.org/0000-0001-8630-1119 1   na1 ,
• Zilu Zhang   ORCID: orcid.org/0009-0000-2180-5957 2   na1 ,
• João Conniot 1 ,
• Diana P. Sousa   ORCID: orcid.org/0000-0003-3474-5417 1 ,
• João M. J. M. Ravasco 1 ,
• Lauren A. Onweller   ORCID: orcid.org/0009-0004-0865-4495 2 ,
• Andżelika Lorenc   ORCID: orcid.org/0000-0002-1474-7864 3 , 4 ,
• Tiago Rodrigues   ORCID: orcid.org/0000-0002-1581-5654 3 ,
• Daniel Reker   ORCID: orcid.org/0000-0003-4789-7380 2 , 5 &
• João Conde   ORCID: orcid.org/0000-0001-8422-6792 1  

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• Nanoparticles
• Nanotechnology in cancer

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

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Acknowledgements

This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-StG-2019-848325), the Duke Science and Technology Initiative and the National Institutes of Health NIGMS grant R35GM151255. We acknowledge Fundação para a Ciência e a Tecnologia (FCT) for financial support in the framework of the PhD grant 2020.06638.BD (D.P.S.), the Duke Department of Biomedical Engineering for support through a BME Fellowship (Z.Z.), the National Science Foundation (NSF) for support through the Graduate Research Fellowship DGE2129754 (L.A.O.) and the ERASMUS+ programme (A.L.).

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These authors contributed equally: Bárbara B. Mendes, Zilu Zhang.

Authors and Affiliations

ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas (NMS|FCM), Universidade NOVA de Lisboa, Lisbon, Portugal

Bárbara B. Mendes, João Conniot, Diana P. Sousa, João M. J. M. Ravasco & João Conde

Department of Biomedical Engineering, Duke University, Durham, NC, USA

Zilu Zhang, Lauren A. Onweller & Daniel Reker

Instituto de Investigação do Medicamento (iMed), Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal

Andżelika Lorenc & Tiago Rodrigues

Department of Biopharmacy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland

Andżelika Lorenc

Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA

Daniel Reker

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Contributions

J. Conde conceived the idea and concept of the study. D.R. conceived the ML platform. T.R. conceived the data curation. B.B.M., J. Conniot, D.P.S., J.M.J.M.R. and J. Conde collected all of the data from the published manuscripts, organized the dataset and calculated the correlations. Z.Z., L.A.O. and A.L. conducted the data analysis, text mining and designed, implemented and evaluated the ML models. J. Conde, D.R. and T.R provided guidance and supervised the work. All authors contributed to the writing and editing of the paper, and all authors approved the final version of the paper.

Corresponding authors

Correspondence to Tiago Rodrigues , Daniel Reker or João Conde .

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Competing interests.

J. Conde and T.R. are co-founders and shareholders of TargTex SA Targeted Therapeutics for Glioblastoma Multiforme. J. Conde is a member of the Global Burden of Disease (GBD) consortium from the Institute for Health Metrics and Evaluation (IHME), University of Washington, USA, and member of the Scientific Advisory Board of Vector Bioscience, Cambridge. T.R. acts as a consultant to the pharmaceutical, biotechnology and technology industry and is a full member of the Acceleration Consortium, University of Toronto. D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a scientific mentor for Start2 and serves on the scientific advisory board of Areteia Therapeutics. The other authors declare no competing interests.

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Nature Nanotechnology thanks Natalie Boehnke, Karolina Jagiełło and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Results and Discussion, Figs. 1– 9, Tables 1–8 and Refs. 1–11.

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Alluvial plot source data for Fig. 2c.

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Raw data for Fig. 4d, alluvial plot source data for Fig. 4e and statistical source data for Fig. 4f.

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Mendes, B.B., Zhang, Z., Conniot, J. et al. A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01673-7

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Received : 03 May 2023

Accepted : 10 April 2024

Published : 15 May 2024

DOI : https://doi.org/10.1038/s41565-024-01673-7

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