DOI: 10.3390/educsci16071044 ISSN: 2227-7102

The Impact of Active Learning on Student Course Performance in STEM Varies by Type and Intensity: A Meta-Analysis

Shangmou Xu, Vicente Velasco, Mariah J. Hill, Elisa Tran, Sweta Agrawal, E. Nicole Arroyo, Shawn Behling, Nyasha Chambwe, Dianne Laboy Cintrón, Jacob D. Cooper, Gideon Dunster, Jared A. Grummer, Kelly M. Hennessey, Jennifer Hsiao, Nicole Iranon, Leonard Jones, Hannah Jordt, Marlowe Keller, Melissa E. Lacey, Caitlin E. Littlefield, Alexander T. Lowe, Shannon Newman, Vera Okolo, Savannah Olroyd, Brandon R. Peecook, Sarah B. Pickett, David L. Slager, Kathryn E. Stanchak, Vasudha Sundaravaradan, Camila Valdebenito, Claire R. Williams, Kaitlin A. Zinsli, Scott Freeman, Elli J. Theobald

Active learning in undergraduate STEM courses boosts student achievement and narrows gaps for underrepresented students compared to traditional lecturing. This meta-analysis aims to further understand the role of active learning practices by interrogating features of the classroom context (e.g., introductory vs. upper-level courses, class size, and discipline) and active learning practices (e.g., type and intensity of active learning) that are correlated with student learning in undergraduate STEM classrooms. After systematically reviewing the literature that was published between 2010 and 2016, and meta-analyzing 134 admitted studies, we found that active learning had a positive impact on student outcomes regardless of class size, course level, or STEM discipline, with an overall effect of roughly half a standard deviation on exam scores (g=0.519, p<0.001, df=231). More importantly, we highlighted two novel results regarding active learning practices. First, student performance was significantly better in courses that employed mid- (g=0.583) or high-intensity (g=0.593) active learning versus lower-intensity (g=0.246), as judged by the amount of class time studies reported students were actively engaged in course activities. Additionally, there was significant heterogeneity in efficacy across different types of active learning employed (QM=120.5, df=7, p<0.001; I2=59.8%). We conclude that most, if not all, types of active learning are effective, and that active learning intensity is associated with stronger effects, although this association may be confounded with other aspects of course redesign. These results suggest that when innovating in their classes, instructors should continually work to increase active learning intensity. Finally, the evidence presented here for active learning’s impact on student outcomes creates a strong foundation for faculty professional development and evaluation.

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