Active Learning outscored Passive and Gamified methods in a Medical Microbiology and Immunology Course: A Repeated-Measures Analysis

doi:10.21203/rs.3.rs-8147913/v1

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Active Learning outscored Passive and Gamified methods in a Medical Microbiology and Immunology Course: A Repeated-Measures Analysis

https://doi.org/10.21203/rs.3.rs-8147913/v1

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Introduction

Medical education has historically relied on lecture-centric, traditional models despite evidence that learner-centered approaches better support durable learning, an issue that often affects dense, foundational courses like medical microbiology and immunology. To address limited evidence in podiatric curricula, we compared Passive, Active, and Gamified instructional methods in a first-year podiatric course.

Methods

A total of 54 first-year podiatric medical students participated in a within-subjects repeated-measures study with a complete-case design, comparing Passive, Active, and Gamified instructional methods within a balanced instructional framework. Learning gains were assessed using pre- and post-quizzes across six unique content sessions, with data analyzed using repeated-measures ANOVA to determine which method most effectively improved knowledge performance.

Results

From the complete-case analysis (N = 43), Active learning produced the highest mean improvement in quiz scores (delta change = 1.64), followed by Passive (delta change = 0.98) and Gamified instruction (delta change = 0.65). A one-way repeated-measures ANOVA showed a significant effect of teaching method, F(2,84) = 10.16, p < 0.001, partial eta squared = 0.20. Pairwise comparisons revealed Active learning significantly outperformed both Gamified (p < 0.001) and Passive (p = 0.012), with no difference observed between the latter instructional methods (p = 0.447).

Conclusion

Despite growing interest in gamified and competitive formats, learning performance in preclinical podiatric education was strongest under active, collaborative, and engagement-focused instruction.

General Microbiology

Active

Passive

Gamified

Podiatric medicine

microbiology

Medical education has evolved substantially since the early twentieth century. The Flexner Report [1] and subsequent post- World War II expansion of National Institutes of Health funding formalized a research driven model of medical education emphasizing scientific rigor and extensive preclinical coursework [2]. As medical schools became increasingly integrated within research universities, lecture-based instruction dominated, and students were often likened to passive subjects in a pedagogical experiment fostering what Norman described as a behaviorist era in which “the student, like the rat, is a passive and motivation-free recipient of stimuli,” with “basic science facts taught in isolation and tested frequently, all without any clinical correlates.” Although this model efficiently delivered large volumes of information, it emphasized rote memorization and separate, compartmentalized learning objectives rather than integrated, applied understanding.

Over the past century, the number of U.S. medical students has risen by almost 450% [3, 4], prompting educators to question the effectiveness of passive information delivery and explore learner-centered strategies that position students as active participants in constructing knowledge [5]. Theoretical frameworks such as Edgar Dale’s “Cone of Learning” suggest that students retain far more of what they actively practice or experience than what they merely read or hear [6, 7]. Yet despite the growing demand, the traditional “sage on a stage” model remains the backbone of many foundational science courses [8, 9] and aspects of a master–apprenticeship model in clinical years, illustrating the persistent influence of hierarchical, top-down learning [10, 11]. As such, lecture-based instruction persists, highlighting a tension between teaching and learning- particularly in foundational courses like medical microbiology and immunology, where dense factual content and conceptual integration intersect, demanding active engagement and durable learning. This tension warrants empirical comparison of instructional methods in preclinical education.

Podiatric medical education presents a unique pedagogical landscape. With almost 100 credit hours of instruction during the first two years, podiatric programs are characterized by smaller cohorts, focused specialization from day one, and early clinical integration in areas such as infection management and antimicrobial decision making. Foundational science courses, including medical microbiology and immunology, are particularly demanding, requiring durable learning and integration across many systems. Yet, empirical data guiding instructional methods in podiatric curricula remain limited, creating a critical need for evidence-based teaching approaches.

Within this shifting educational landscape [12], three instructional approaches have come to define contemporary medical education: Passive learning, which emphasizes information transmission; Active learning, which prioritizes engagement, collaboration, and self-directed participation; and Gamified instruction, which incorporates motivational elements derived from game design. Although numerous studies across health professions have examined these methods individually [13–17], literature is scarce when comparing their relative effectiveness within the same student cohort, and almost none have done so in podiatric medical education [18, 19].

Passive instruction, characterized by traditional didactic lectures, has long dominated U.S. medical classrooms. It operates under the paradigm that knowledge is transmitted in discrete packets from instructor to student [20]. While this model efficiently delivers large tomes of information [21, 22], it often fosters poor long-term retention, superficial understanding, and limited opportunities to think critically [23]. These limitations led to the rise of Active learning, in which students engage directly in constructing knowledge through problem-based learning (PBL), case-based learning (CBL), flipped classroom, and collaborative formats such as jigsaw exercises [8, 24, 25]. Active learning appears to promote communication, motivation, and satisfaction by requiring students to build understanding through cognitive activity rather than passive reception [26]. Despite its advantages, challenges remain including resistance to unfamiliar formats [27], perceptions of greater student demand [28], and reports that students feel they learn less even when objective performance improves, emphasizing the instructor is just as crucial in the process of guiding understanding [29].

To bridge these challenges, Gamification- the integration of game-based elements into educational activities- has gained attention. While conceptually related to serious games and game based learning, Gamification applies points, competition, and reward systems to non-game contexts to enhance motivation and engagement [30–33]. Studies have reported positive effects on learner enjoyment and participation [34, 35], although results regarding knowledge performance remain mixed [36–38].

Despite growing interest in Active and Gamified learning, empirical research within podiatric medical education remains limited. Given the field’s smaller cohort sizes and intensive preclinical science curricula, podiatry offers a unique setting for evaluating how instructional strategies influence foundational knowledge acquisition. The purpose of this study was to compare Passive, Active, and Gamified instructional methods in a first-year Medical Microbiology and Immunology course to determine their relative effects on short-term learning performance. It was hypothesized that Active learning would produce the greatest knowledge gains compared with either Passive or Gamified instruction.

Participants and study design

A total of 54, first year podiatric medical students participated in a study that was a part of a six credit Medical Microbiology and Immunology course. Though alternative analytical approaches (ie mixed models) could have been used due to some participant data missing (< 3.5%), this study leveraged a within-subjects, repeated measures design, leveraging that each participant was their own control across three different teaching methods. Instruction was delivered by the same instructor over two consecutive weeks in a counterbalanced order to minimize sequencing effects and each of the six sessions covered unique material, reducing the likelihood that topic difficulty would bias learning outcomes. At the onset of each week, a combined 15 item pre-quiz (5 items aligned to each upcoming session) was given over material to be covered that week. Each class session ended with a 5-item quiz specific to that session along with a survey. The instructor delivering all sessions was not blinded to instructional method (by design) but was blinded to individual study enrollment and grade identifiers.

Teaching methods

For Passive lecturing, content was delivered entirely in a didactic form with little to no active questioning nor student interaction. Students were expected to rely entirely on their own processing of information for the material taught in class. For Active learning, a combination of techniques was employed. These involved fill-in-the-blank worksheets as material was discussed, production of 5-minute PPT presentation on main points from slides that are presented in front of the class, and “jigsaw” activities. For Gamified instruction, prizes were awarded to top winners of classroom competitions. Competitions involved one-on-one, publicly visible competitions over class content for that session and publicly visible team-based play in randomly assigned groups of 4–5 students, using two commercial quiz/competition platforms (e.g., a “battle royale” quiz and a team-based “jeopardy-style” format).

Missing data

Of 54 enrolled participants, 11 individuals had at least one missing quiz score across the 6 assessments from 3 teaching methods (< 3.5% of data). To ensure a balanced dataset, a complete-case analysis was conducted and students missing any teaching method quiz were excluded, yielding 43 participants for analysis.

Statistical analysis

Data were analyzed in SigmaPlot (v14.5). Knowledge gain (delta score; -5 to 5) was calculated as difference between post and pre quiz performance. A one-way repeated-measures ANOVA tested the effect of instructional method (Passive, Active, Gamified). Mauchly’s test indicated sphericity was met (p > .05); thus no Greenhouse–Geisser correction was applied per SigmaPlot v14.5. Three planned pairwise comparisons (Passive vs. Active, Passive vs. Gamified, Active vs. Gamified) were conducted following a significant main effect and Holm-Bonferroni corrected p-values are reported. Within-subjects effect sizes were calculated using the standard deviation of the difference scores. Effect magnitude was interpreted contextually following guidance that emphasizes disciplinary relevance rather than fixed thresholds[39], although conventional benchmarks of 0.20, 0.50, and 0.80 were referenced. Effect sizes are expressed as partial eta-squared [40–42] and all tests were two-tailed with α = 0.05. [43]. The study was approved by the Kent State University IRB (# 24-1660) and deemed exempt and participation was voluntary with informed consent.

Descriptive statistics

From the case-complete analysis (N = 43), the mean delta learning scores (post – pre quiz) were highest for Active learning instruction (M = 1.64, SEM = 0.15; 95%CI [1.33- 1.95]), followed by Passive (M = 0.98, SEM = 0.18; 95%CI [0.62- 1.34]), and Gamified being the lowest (M = 0.65, SEM = 0.14; 95%CI [0.37- 0.94]) (Figure 1; Table 1). Median values showed similar ordering and IQR (interquartile range) showed slightly greater range within Passive lecturing. The coefficient of variation (CV) was greatest for Gamified instruction (142.7%) followed by Passive (120.3%), and Active instruction was the least variable at 60.7%, indicating performance was higher and more consistent across participants for Active instruction (Table 2). CV values were reported to highlight relative variability across methods.

Repeated measures ANOVA

Assumptions of normality and equal variance were met (Shapiro–Wilk p = .054; Brown–Forsythe p = .513). A one-way repeated-measures ANOVA confirmed a significant main effect of teaching method on learning gains, F(2, 84) = 10.16, p<0.001 and relatively large effect size for medical education research (partial η² = 0.20)(Table 2). Of the three planned pairwise comparisons, Active produced greater gains than Passive (mean Δ difference = 0.66; t(42) = 2.97; Holm–Bonferroni p = .012) and Gamified (Δ = 0.99; t(42) = 4.42; p < .001); Passive vs Gamified was not significant (Δ = 0.33; t(42) = 1.46; p = .447) (Table 2). Additionally, pairwise comparisons yielded medium to large within-subjects effects (0.61-1.03), representing practically meaningful gains consistent with current trends in medical educational research [39].Table 1. Descriptive statistics for delta learning scores (Post- Pre quiz) across three teaching methods and 6 sessions (N=43).

Teaching Method	Mean Δ	SEM	95% CI Lower	95% CI Upper	SD	Coefficient of Variation (%)
Passive instruction	0.98	0.18	0.62	1.34	1.18	120.3
Active instruction	1.64	0.15	1.33	1.95	0.99	60.7
Gamified instruction	0.65	0.14	0.37	0.94	0.92	142.7

Table 2. Repeated-Measures ANOVA and Holm-Bonferroni Pairwise Comparisons of Delta Learning Scores (N=43) across three teaching methods and 6 sessions

Source of Variation	df	SS	MS	F	p	Partial eta²	Observed Power
between subjects	42	45.725	1.089
between treatments (method)	2	21.818	10.909	10.16	< .001	.20	.97
residual (error)	84	90.182	1.074

Post-hoc Holm-Bonferroni Pairwise Comparisons

Comparison	Mean difference (Δ)	Cohen’s d [95% CI]; effect interpretation	t	p
Active vs Passive	0.663	0.61 [0.20– 1.02]; moderate	2.966	0.012
Active vs Gamified	0.988	1.03 [0.61–1.45]; large	4.423	< 0.001
Passive vs Gamified	0.326	0.33 [–0.07- 0.73]; small	1.457	0.447

The current study was intentionally organized to focus on objective learning performance outcomes resulting from three common instructional strategies, Passive learning, Active learning, and Gamified instruction. Although research in medical education examine both knowledge and survey constructs of difficulty, engagement, motivation, and/or satisfaction, this study’s exclusive focus on performance provides a clearer answer to a thorny, core curricular question: which method most effectively improves knowledge acquisition? By leveraging a one-way repeated measures crossover design with unique information taught in each of the sessions, each participant served as their own control, thereby minimizing individual variability and thus strengthening within-subject validity.

Overall, our data demonstrates that Active learning instruction consistently outperformed both Passive and Gamified instruction across all classroom sessions. Although this outcome aligns with previous studies of Active learning in medical education [5, 44–46], the finding is particularly notable considering the relatively modest cohort size and repeated measures study design. Furthermore, the large educational effect size (partial η² = 0.20) underscores the practical importance of this outcome and the value of active engagement, collaboration, and application-based learning strategies in preclinical education [42].

Active learning is grounded in the principle that students construct knowledge through interaction and application with self-guided reflection. This approach promotes deeper cognitive processing by requiring learners to retrieve and apply information in varied contexts [44, 46]. Numerous studies have examined Active learning but interestingly they focus in on how new information is processed [15, 47]. The findings within our study align with Kolb’s Experiential Learning Theory which emphasizes that the student learns through iterative cycles of active engagement and reflection [48]. Students were allowed to interact with the material, apply it in context, and then to disseminate it to others, thus reflecting on their own work.

Although Active learning methods enhanced objective performance, several factors influence effectiveness. Research has shown that “spaced education,” in which shorter learning encounters are spaced and repeated over time, leads to more durable learning than the “bolus” sessions typical of many medical school curricula [21]. This approach parallels spiral curricula that scaffold knowledge through repeated opportunities for retrieval and application, much like what students encounter in clinical settings [49, 50]. Though this study only looked at short-term learning performance within a single class, future courses should explore how curricular alignment can institute educational reinforcement, especially before National Boards (APMLE) Part 1.

Anecdotally, another consideration that impacts Active learning is that perceptions of learning often diverge from actual learning [29]. Deslauriers and colleagues noted that students in Active classrooms felt they learned less, even when their performance improved. Within our study, Active learning was received well with students remarking that the strategy was “more memorable” whereas in Gamified, students expressed frustration when unable to outperform their peers. This suggests that the success of Active learning depends on instructional quality and content complexity but also relies on the expectations of the student especially if the student is resistant to new forms of learning [51]. Likewise, poorly scaffolded Active learning session can increase cognitive load and undermine learning performance and motivation. Within this study, it is theorized that though self-directed learning approach [52] was implemented in both Active and Gamified learning instruction, Active learning environment fostered more collaboration and engagement, therefore resulting in larger learning performance gains. Although the current study didn’t measure cognitive load, engagement, or motivation directly, prior research indicates these factors can moderate Active learning outcomes implying that Active learning is not a universal solution but a flexible framework to refine [53, 54].

While gamification has been shown to enhance motivation, enjoyment, and participation among medical students [30, 34, 35, 54], its direct impact on student learning isn’t entirely consistent. Festinger’s social comparison theory suggests that peer competition can heighten engagement [55] and positive experience with medical content [56–58]. However, systematic reviews report mixed effects on knowledge acquisition, citing irrelevance and lack of motivation as hindering variables [36–38]. In the present study, Gamified sessions yielded positive learning gains but slightly lower performance than Passive learning and significantly lower than Active learning. Several explanations may account for this. First gamification in theory relies on extrinsic motivators such as points, prizes, and leaderboards which can shift cognitive resources away from knowledge understanding and towards game performance [59]. These dynamics could enhance engagement for some learners while reducing it in others, resulting in uneven benefits and masking class performance [53, 60–62]. Likewise, potential misalignments between game activities and assessment objectives can limit learning when a game measures recall over higher order thinking, for example [14, 63]. Gamified learning, if poorly implemented, can introduce stress or frustration that detracts from engagement and may negatively affect learner well-being [64, 65].

Within Gamified learning, formalized instructor feedback, which is a hallmark of Active learning, is absent [66] and may hinder engagement, particularly once novelty of the game wears off [67]. Likewise, since Gamified activities often target speed over problem solving, this may have been a reason for why Gamified learning didn’t match or surpass Active learning gains in our study [67]. Our findings do support that learning performance increases within Ericsson’s theory of deliberate practice [68], which emphasizes that expertise develops through structured repetition and timely feedback, however the gains are significantly less than Active learning. Though initially surprising, perhaps a lack of structured feedback within the games didn’t allow for enough repetition and building of durable learning.

Implications for podiatric medical education

This study provides one of the first empirical comparative, within subjects approaches within U.S. podiatric medical education. The finding that Active learning produced the largest and most consistent learning gains has practical implications for preclinical courses such as microbiology and immunology, where early competency directly supports podiatric clinical competencies. Podiatric practitioners must rapidly integrate microbiological and immunological knowledge into wound management, infection control, and antimicrobial selection, areas where applied understanding and differential diagnosis is critical to patient outcomes.

As podiatric curricula continue to evolve toward competency-based and integrated learning environments, these results highlight that pedagogical design, not novelty, drive effective learning. Implementing structured active learning strategies such as flipped classrooms, team-based learning, problem-based learning, simulations, and jigsaw activities can foster not only short-term learning gains but also higher order reasoning. In contrast, the mixed performance of Gamified matching up with Passive Learning suggests that competition alone does not ensure durable knowledge gain, reinforcing the need for alignment between teaching methods, learning outcomes, and assessment performance.

Limitations

Several limitations should be noted. One key limitation is the sample size of the assessment. This study focused on one cohort of first year podiatric medical students from United States and may limit generalizability of the findings to other educational institutions or other systems. Additionally, another limitation is that knowledge gain was measured for short-term knowledge gains, long-term retention including encoding, storage, and retrieval was not assessed and should be followed up longitudinally. Finally, quiz reliability (KR-20) was not evaluated given the small number of items per session which was in part due to course time constraints; subsequent studies should include instrument quality metrics to ensure measurement precision.

The findings from this study indicate that Active learning was the most effective instructional method for improving learning outcomes within a podiatric, medical microbiology and immunology course. Although both Passive and Gamified methods produced positive mean gains, neither method led to statistically significant improvement compared to Active learning. The repeated-measures crossover design and large observed effect size provides practical evidence to the advantages of Active learning strategies in podiatric education. These findings also suggest that integrating structured, interactive approaches into foundational science courses may strengthen core preclinical knowledge for clinical reasoning in infection management, wound healing, and surgical decision-making in podiatric medicine. Collectively this study highlights the importance of optimizing teaching methods and providing empirical data for educators seeking to enhance learning effectiveness in podiatric education.

Ethics approval: The study was approved by the Kent State University IRB (# 24-1660) and deemed exempt and participation was voluntary with informed consent.

Data Availability: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Consent for publication: Not applicable

Competing Interests: The authors declare there are no competing interests.

Funding: This study was funded in part by a KSU- Teaching Scholars award.

Author contributions: MD was involved in all aspects of the study including inception, implementation, editing, and submission of the manuscript.

Acknowledgments: I would like to thank the Kent State University College of Podiatric Medicine and the KSU-CPM students for their participation in this study. I also thank LeighAnn Tomaswick from the Center for Teaching and Learning for reviewing the study materials and providing de-identified data to ensure blinding for the study design.

Flexner A. Medical Education in the United States and Canada. Science. 1910;32:41–50. https://doi.org/10.1126/science.32.810.41
Norman G. Medical education: past, present and future. Perspect Med Educ. 2012;1:6–14. https://doi.org/10.1007/s40037-012-0002-7
Colwell NP. Medical Education 1926-1928. Bulletin, 1929, No. 10. Bureau of Education, Department of the Interior [Internet]. ERIC; 1929 [cited 2025 Oct 22]; https://eric.ed.gov/?id=ED540106. Accessed 22 Oct 2025
Association of American Medical Colleges. Facts: Enrollment, graduates, and MD-PhD data [Internet]. AAMC; 2024. https://www.aamc.org/data-reports/students-residents/data/facts-enrollment-graduates-and-md-phd. Accessed 9 Oct 2025
Michael J. Where’s the evidence that active learning works? Advances in Physiology Education. 2006;30:159–67. https://doi.org/10.1152/advan.00053.2006
Joshi VR, Younger MJ, Joshi B. Active Learning and Competency Preconditioning Strengthen Osteopathic Medical Student Performance, Physician Attributes, and Competency Assessments. Res Dev Med Educ. Tabriz University of Medical Sciences; 2021;10:10–10. https://doi.org/10.34172/rdme.2021.010
Masters K. Edgar Dale’s Pyramid of Learning in medical education: A literature review. Medical Teacher. 2013;35:e1584–93. https://doi.org/10.3109/0142159X.2013.800636
McCoy L, Pettit RK, Kellar C, Morgan C. Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach. J Med Educ Curric Dev. 2018;5:2382120518765135. https://doi.org/10.1177/2382120518765135
Patrick L, Howell LA, Wischusen EW. Roles matter: Graduate student perceptions of active learning in the STEM courses they take and those they teach. Sci Prog. 2021;104:00368504211033500. https://doi.org/10.1177/00368504211033500
Nilsson MS, Pennbrant S, Pilhammar E, Wenestam C-G. Pedagogical strategies used in clinical medical education: an observational study. BMC Med Educ. 2010;10:9. https://doi.org/10.1186/1472-6920-10-9
Woolliscroft JO. Medical Student Clinical Education. In: Norman GR, Van Der Vleuten CPM, Newble DI, Dolmans DHJM, Mann KV, Rothman A, et al., editors. International Handbook of Research in Medical Education [Internet]. Dordrecht: Springer Netherlands; 2002 [cited 2025 Oct 22]. p. 365–80. https://doi.org/10.1007/978-94-010-0462-6_15
Emanuel EJ. The Inevitable Reimagining of Medical Education. JAMA. 2020;323:1127–8. https://doi.org/10.1001/jama.2020.1227
Boedeker P, Schlingmann T, Kailin J, Nair A, Foldes C, Rowley D, et al. Active Versus Passive Learning in Large-Group Sessions in Medical School: A Randomized Cross-Over Trial Investigating Effects on Learning and the Feeling of Learning. Med Sci Educ. 2024;35:459–67. https://doi.org/10.1007/s40670-024-02219-1
Gentry SV, Gauthier A, Ehrstrom BL, Wortley D, Lilienthal A, Car LT, et al. Serious Gaming and Gamification Education in Health Professions: Systematic Review. Journal of Medical Internet Research. 2019;21:e12994. https://doi.org/10.2196/12994
Kooloos JGM, Bergman EM, Scheffers MAGP, Schepens‐Franke AN, Vorstenbosch MATM. The Effect of Passive and Active Education Methods Applied in Repetition Activities on the Retention of Anatomical Knowledge. Anat Sci Educ. 2020;13:458–66. https://doi.org/10.1002/ase.1924
Sandoval-Hernández I, Molina-Torres G, León-Morillas F, Ropero-Padilla C, González-Sánchez M, Martínez-Cal J. Analysis of different gamification-based teaching resources for physiotherapy students: a comparative study. BMC Med Educ. 2023;23:675. https://doi.org/10.1186/s12909-023-04576-8
Xu M, Luo Y, Zhang Y, Xia R, Qian H, Zou X. Game-based learning in medical education. Front Public Health. 2023;11:1113682. https://doi.org/10.3389/fpubh.2023.1113682
Labovitz J, Hubbard C. The Use of Virtual Reality in Podiatric Medical Education. Clin Podiatr Med Surg. 2020;37:409–20. https://doi.org/10.1016/j.cpm.2019.12.008
Smith KM, Geletta S, Duelfer K. Flipped Classroom in Podiatric Medical Education. J Am Podiatr Med Assoc [Internet]. 2020 [cited 2025 Oct 23];110. https://doi.org/10.7547/19-060
Boyer EL. Scholarship Reconsidered: Priorities of the Professoriate. Princeton University Press, 3175 Princeton Pike, Lawrenceville, NJ 08648.: Princeton University Press, 3175 Princeton Pike, Lawrenceville, NJ 08648.; 1990.
Kerfoot BP, DeWolf WC, Masser BA, Church PA, Federman DD. Spaced education improves the retention of clinical knowledge by medical students: a randomised controlled trial. Med Educ. 2007;41:23–31. https://doi.org/10.1111/j.1365-2929.2006.02644.x
Wingfield SS, Black GS. Active Versus Passive Course Designs: The Impact on Student Outcomes. Journal of Education for Business. 2005;81:119–23. https://doi.org/10.3200/JOEB.81.2.119-128
Custers EJFM. Long-term retention of basic science knowledge: a review study. Adv in Health Sci Educ. 2010;15:109–28. https://doi.org/10.1007/s10459-008-9101-y
Chen F, Lui AM, Martinelli SM. A systematic review of the effectiveness of flipped classrooms in medical education. Med Educ. 2017;51:585–97. https://doi.org/10.1111/medu.13272
Hew KF, Lo CK. Flipped classroom improves student learning in health professions education: a meta-analysis. BMC Med Educ. 2018;18:38. https://doi.org/10.1186/s12909-018-1144-z
Driver R, Asoko H, Leach J, Scott P, Mortimer E. Constructing Scientific Knowledge in the Classroom. Educational Researcher. American Educational Research Association; 1994;23:5–12. https://doi.org/10.3102/0013189X023007005
Mohamed A-R. Effects of Active Learning Variants on Student Performance and Learning Perceptions. International Journal for the Scholarship of Teaching and Learning. 2008;2:11.
Shekhar P, Borrego M, DeMonbrun M, Finelli C, Crockett C, Nguyen K. Negative Student Response to Active Learning in STEM Classrooms: A Systematic Review of Underlying Reasons. Journal of College Science Teaching. 2020;49:45–54. https://doi.org/10.1080/0047231X.2020.12290664
Deslauriers L, McCarty LS, Miller K, Callaghan K, Kestin G. Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proc Natl Acad Sci USA. 2019;116:19251–7. https://doi.org/10.1073/pnas.1821936116
Çiftci S. Trends of Serious Games Research from 2007 to 2017: A Bibliometric Analysis. Journal of Education and Training Studies. ERIC; 2018;6:18–27.
López-Belmonte J, Segura-Robles A, Fuentes-Cabrera A, Parra-González ME. Evaluating activation and absence of negative effect: Gamification and escape rooms for learning. International journal of environmental research and public health. MDPI; 2020;17:2224.
Martí‐Parreño J, Méndez‐Ibáñez E, Alonso‐Arroyo A. The use of gamification in education: a bibliometric and text mining analysis. Computer Assisted Learning. 2016;32:663–76. https://doi.org/10.1111/jcal.12161
Trinidad M, Ruiz M, Calderon A. A bibliometric analysis of gamification research. IEEE Access. IEEE; 2021;9:46505–44.
Van Gaalen AEJ, Brouwer J, Schönrock-Adema J, Bouwkamp-Timmer T, Jaarsma ADC, Georgiadis JR. Gamification of health professions education: a systematic review. Adv in Health Sci Educ. 2021;26:683–711. https://doi.org/10.1007/s10459-020-10000-3
Welbers K, Konijn EA, Burgers C, De Vaate AB, Eden A, Brugman BC. Gamification as a tool for engaging student learning: A field experiment with a gamified app. E-Learning and Digital Media. 2019;16:92–109. https://doi.org/10.1177/2042753018818342
Almeida C, Kalinowski M, Uchôa A, Feijó B. Negative effects of gamification in education software: Systematic mapping and practitioner perceptions. Information and Software Technology. Elsevier; 2023;156:107142.
Gorbanev I, Agudelo-Londoño S, González RA, Cortes A, Pomares A, Delgadillo V, et al. A systematic review of serious games in medical education: quality of evidence and pedagogical strategy. Medical Education Online. Taylor & Francis; 2018;23:1438718. https://doi.org/10.1080/10872981.2018.1438718
Huang WD, Loid V, Sung JS. Reflecting on gamified learning in medical education: a systematic literature review grounded in the Structure of Observed Learning Outcomes (SOLO) taxonomy 2012—2022. BMC Med Educ. 2024;24:20. https://doi.org/10.1186/s12909-023-04955-1
Bakker A, Cai J, English L, Kaiser G, Mesa V, Van Dooren W. Beyond small, medium, or large: points of consideration when interpreting effect sizes. Educ Stud Math. 2019;102:1–8. https://doi.org/10.1007/s10649-019-09908-4
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers; 1988.
Kraft MA. Interpreting Effect Sizes of Education Interventions. Educational Researcher. American Educational Research Association; 2020;49:241–53. https://doi.org/10.3102/0013189X20912798
Richardson JTE. Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review. 2011;6:135–47. https://doi.org/10.1016/j.edurev.2010.12.001
Poitevineau J, Lecoutre B. Interpretation of significance levels by psychological researchers: The .05 cliff effect may be overstated. Psychonomic Bulletin & Review. 2001;8:847–50. https://doi.org/10.3758/BF03196227
Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, et al. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci USA. 2014;111:8410–5. https://doi.org/10.1073/pnas.1319030111
Lees-Murdock DJ, Khan D, Irwin R, Graham J, Hinch V, O’Hagan B, et al. Assessing the efficacy of active learning to support student performance across undergraduate programmes in biomedical science. British Journal of Biomedical Science. Frontiers Media SA; 2024;81:12148.
Ribeiro-Silva E, Amorim C, Aparicio-Herguedas JL, Batista P. Trends of active learning in higher education and students’ well-being: A literature review. Frontiers in Psychology. Frontiers Media SA; 2022;13:844236.
Markant DB, Ruggeri A, Gureckis TM, Xu F. Enhanced Memory as a Common Effect of Active Learning. Mind, Brain, and Education. 2016;10:142–52. https://doi.org/10.1111/mbe.12117
Kolb D. Experiental learning: Experience as the source of learning and development. Prentice Hall; 1984.
Fernández M, Wegerif R, Mercer N, Rojas-Drummond S. Re-conceptualizing" scaffolding" and the zone of proximal development in the context of symmetrical collaborative learning. The journal of classroom interaction. JSTOR; 2001;40–54.
Harden RM. What is a spiral curriculum? Medical Teacher. 1999;21:141–3. https://doi.org/10.1080/01421599979752
Silverthorn DU. When Active Learning Fails… and What to Do About It. In: Mintzes JJ, Walter EM, editors. Active Learning in College Science [Internet]. Cham: Springer International Publishing; 2020 [cited 2025 Oct 29]. p. 985–1001. https://doi.org/10.1007/978-3-030-33600-4_61
Fleiszer D, Fleiszer T, Russell R. Doughnut Rounds: A self-directed learning approach to teaching critical care in surgery. Medical Teacher. 1997;19:190–3. https://doi.org/10.3109/01421599709019380
Deci EL, Ryan RM. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media; 2013.
Hanus MD, Fox J. Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers & Education. 2015;80:152–61. https://doi.org/10.1016/j.compedu.2014.08.019
Festinger L. A Theory of Social Comparison Processes. Human Relations. 1954;7:117–40. https://doi.org/10.1177/001872675400700202
Beylefeld AA, Struwig MC. A gaming approach to learning medical microbiology: students’ experiences of flow. Medical Teacher. Taylor & Francis; 2007;29:933–40. https://doi.org/10.1080/01421590701601550
Do M, Sanford K, Roseff S, Hovaguimian A, Besche H, Fischer K. Gamified versus non-gamified online educational modules for teaching clinical laboratory medicine to first-year medical students at a large allopathic medical school in the United States. BMC Med Educ. 2023;23:959. https://doi.org/10.1186/s12909-023-04951-5
Pineros N, Tenaillon K, Marin J, Berry V, Jaureguy F, Ghelfenstein-Ferreira T, et al. Using gamification to improve engagement and learning outcomes in medical microbiology: the case study of ‘BacteriaGame.’ FEMS Microbiology Letters. Oxford University Press; 2023;370:fnad034.
Chou Y. Actionable gamification: Beyond points, badges, and leaderboards. Packt Publishing Ltd; 2019.
Aras GN, Çiftçi B. Comparison of the effect of reinforcement with question-answer and kahoot method on the success and motivation levels of nursing students: A quasi-experimental review. Nurse Education Today. Elsevier; 2021;102:104930.
Sailer M, Homner L. The Gamification of Learning: a Meta-analysis. Educ Psychol Rev. 2020;32:77–112. https://doi.org/10.1007/s10648-019-09498-w
Sweller J. Cognitive load during problem solving: Effects on learning. Cognitive science. Elsevier; 1988;12:257–85.
Biggs J. Enhancing teaching through constructive alignment. High Educ. 1996;32:347–64. https://doi.org/10.1007/BF00138871
Rosiek A, Rosiek-Kryszewska A, Leksowski Ł, Leksowski K. Chronic Stress and Suicidal Thinking Among Medical Students. Int J Environ Res Public Health. 2016;13:212. https://doi.org/10.3390/ijerph13020212
Rutledge C, Walsh CM, Swinger N, Auerbach M, Castro D, Dewan M, et al. Gamification in Action: Theoretical and Practical Considerations for Medical Educators. Acad Med. 2018;93:1014–20. https://doi.org/10.1097/ACM.0000000000002183
Chi MTH, Wylie R. The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist. 2014;49:219–43. https://doi.org/10.1080/00461520.2014.965823
Dichev C, Dicheva D. Gamifying education: what is known, what is believed and what remains uncertain: a critical review. Int J Educ Technol High Educ. 2017;14:9. https://doi.org/10.1186/s41239-017-0042-5
Ericsson KA, Harwell KW. Deliberate practice and proposed limits on the effects of practice on the acquisition of expert performance: Why the original definition matters and recommendations for future research. Frontiers in psychology. Frontiers Media SA; 2019;10:2396.

The authors declare no competing interests.

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Active Learning outscored Passive and Gamified methods in a Medical Microbiology and Immunology Course: A Repeated-Measures Analysis

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