Learner Outcomes in Calculus-Based Physics Using Physics Education Technology Simulations: A Meta-Analysis
Main Article Content
Keywords
Blended learning, calculus-based, inquiry, physics, PHET, simulation, teaching
Abstract
PhET became a tool for learning Calculus-Based Physics (CBP) alongside inquiry-based teaching. A meta-analysis using a random-effects model was conducted following PRISMA guidelines to synthesize outcomes from eligible studies published from January 2006 to December 2025 in selected research databases. Of the 649 studies that were culled, 27 studies (N=10,062) met the criteria, with a moderate inter-rater agreement (κ=0.674,p<.001). A significant positive moderate random effect size (ES) (g=0.580,p<.001) indicated positive improvement in CBP learning. Moderator analysis further detected a positive effect on the learning outcomes among high school learners (g=1.144,p< .001), and when inquiry through exploration (g=0.898,p< .001) was employed. The implications of its effectiveness depend on the sustainability, digital nativeness, mathematical preparation, curriculum integration of inquiry-teaching frameworks, and the appropriateness of the digital tools used.
References
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Budnyk, O. (2019). Innovative competence of a teacher: Best European practices. Journal of Vasyl Stefanyk Precarpathian National University, 6(1), 76-89. https://doi.org/10.15330/jpnu.6.1.76-89
Burghardt, M., Ferdinand, P., Pfeiffer, A., & Romagnoli, G. (2020). Integration of new technologies and alternative methods in laboratory-based scenarios. International Conference on Remote Engineering and Virtual Instrumentation. https://doi.org/10.1007/978-3-03052575-0_40
Couch, A. S. (2014). The impact of virtual simulations on student comprehension of mechanics (Publication No. 151) [Master's thesis, Louisiana State University]. LSU Scholarly Repository. https://repository.lsu.edu/gradschool_ theses/151
Doherty, M. (2010). The effect of daily graded reflective journaling on gains in conceptual understanding and the scientific attitude toward conceptual understanding for high school students studying Newtonian mechanics (Publication No. 3424167) [Doctoral dissertation, University of Massachusetts Lowell]. ProQuest Dissertations and Theses Global.
Do, T. D., & Zsolnai, A. (2022). Teachers' social and emotional competence: A new approach of teacher education in Vietnam. Hungarian Educational Research Journal, 12(2), 131-144. https://doi. org/10.1556/063.2021.00050
Forero, D. A., Lopez-Leon, S., Gonzalez-Giraldo, Y., & Bagos, P. G. (2019). Ten simple rules for carrying out and writing meta-analyses. PLoS Comput Biol, 15(5), e1006922. https://doi.org/10.1371/journal.pcbi.1006922
Helm, F., & Beaven, A. (Eds.). (2020). Designing and implementing virtual exchange – A collection of case studies. Research-publishing.net. https://doi.org/10.14705/rpnet.2020.45.9782490057726
Hurtado-Bermúdez, S., & Romero-Abrio, A. (2020). The effects of combining virtual laboratory and advanced technology research laboratory on university learners' conceptual understanding of electron microscopy. Interactive Learning Environments, 1-16. https://doi.org/10.1080/104948 20.2020.1821716
Kaldaras, L., & Wieman, C. (2023). Introducing an instructional model for teaching blended math-science sensemaking in undergraduate STEM courses using computer simulations. arXiv. https://doi.org/10.48550/arXiv.2305.13451
Klassen, M., Jesiek, B. K., Zheng, L., & Case, J. M. (2022). Institutionalizing engineering education research: Comparing Australia, China, and the United States. In S. Christensen, C. Baillie, & G. Östlund (Eds.), Philosophy of engineering and technology: Engineering, social sciences, and the humanities: Have their conversations come of age? (pp. 33-63). Springer. https://doi.org/10.1007/978-3-03111601-8_3
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Kohn, P. K., Underwood, S. M., & Cooper, M. M. (2018). Energy connections and misconnections across chemistry and biology. CBE—Life Sciences Education, 17(1), ar3. https://doi.org/10.1187/cbe.17-08-0169
Madsen, A., McKagan, S. B., & Sayre, E. C. (2015). How physics instruction impacts learners' beliefs about learning physics: A meta-analysis of 24 studies. Physical Review Special Topics-Physics Education Research, 11(1), Article 010115. https://doi.org/10.1103/ PhysRevSTPER.11.010115
Massey-Allard, J. (2021). Learning physics with interactive simulations: Inductive inquiry learning activities for an introductory electromagnetism course [Doctoral dissertation, University of British Columbia]. UBC Theses and Dissertations. https://open.library.ubc. ca/cIRcle/collections/ubctheses/24/items/1.0402206
Molina, V. C., Guzman, D. S., Montalvo, T. R., & Garcia-Salcedo, R. (2025). Teaching competencies in physics for engineering education: A qualitative analysis from teaching practice. arXiv. https://doi.org/10.48550/arXiv.2510.27674
Moore, E., & Perkins, K. (2014a). Assessing the implicit scaffolding design framework: Effectiveness of the build a molecule simulation. In Proceedings of the National Association for Research in Science Teaching (NARST) Annual International Conference. ComPADRE. https://www.per-central.org/items/detail.cfm?ID=16318
Moore, E. B., Chamberlain, J. M., Parson, R., & Perkins, K. K. (2014b). PhET interactive simulations: Transformative tools for teaching chemistry. Journal of Chemical Education, 91(8), 1191-1197. https://doi.org/10.1021/ed4006497
Neidorf, T., Arora, A., Erberber, E., Tsokodayi, Y., & Mai, T. (2019). Review of research into misconceptions and misunderstandings in physics and mathematics. In T. Neidorf, A. Arora, E. Erberber, Y. Tsokodayi, & T. Mai (Eds.), Learner misconceptions and errors in physics and mathematics: Exploring data from TIMSS and TIMSS Advanced (pp. 11–20). Springer. https://doi.org/10.1007/9783-030-30188-0_2
Pedaste, M., Lazonder, A., Raes, A., Wajeman, C., Moore, E., & Girault, I. (2016). Grand Challenge Problem 3: Empowering science teachers using technology-enhanced scaffolding to improve inquiry learning. In T. de Jong, M. Specht, & F. Wild (Eds.), Grand challenge problems in technology-enhanced learning II: MOOCs and beyond (pp. 17-20). Springer. https://doi.org/10.1007/9783-319-12562-6_5
Peters, M., von Doetinchem, P., & von Doetinchem de Rande, S. (2023). Virtual physics laboratory courses: An evaluation of students' self-efficacy and intelligence mindset. arXiv. https://doi.org/10.48550/arXiv.2301.02699
Pospiech, G., Michelini, M., & Eylon, B.-S. (Eds.). (2019). Mathematics in physics education. Springer. https://doi. org/10.1007/978-3-030-04627-9
Redish, E. F. (2021). Using math in physics: Overview. The Physics Teacher, 59(5), 314-318. https://doi.org/10.1119/10.0004880
Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2006). Publication bias in meta-analysis: Prevention, assessment and adjustments. John Wiley & Sons. https://doi.org/10.1002/0470870161
Shi, L., & Lin, L. (2019). The trim-and-fill method for publication bias: Practical guidelines and recommendations based on a large database of meta-analyses. Medicine, 98(23), e15987-e15987. https://doi.org/10.1097/MD.0000000000015987
Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176-199. https://doi. org/10.1177/1745691615569582
Sukarelawan, R. M., Pranata, R., & Putri, A. (2024). The effectiveness of PhET simulations in physics education: A comprehensive meta-analysis of secondary vs. tertiary outcomes (2018-2023). Indonesian Science Education Journal, 17(3), 632-648.
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., & Boutron, I. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 355, i4919. https://doi.org/10.1136/bmj.i4919
Taylor, J. M., & Alanazi, S. (2023). Cohen's and Hedges'g. Journal of Nursing Education, 62(5), 316-317. Thomas, G. (2016). Metacognition in science education: considering cultural and contextual orientations. Alberta Science Education Journal, 44(2), 4-15.
Tokarz, K., Czekalski, P., Drabik, G., Paduch, J., Distefano, S., Di Pietro, R., Merlino, G., Scaffidi, C., Sell, R., Kuaban, G. S., Gansch, R., Grocholewicz, M., Harasymiv, S., Kocur, D., Orłowski, M., Pławiak-Mowna, A., Podaza, J., Robert, J., Schillinger, A., . . . Žagar, M. (2020). Internet of things network infrastructure for the educational purpose. 2020 IEEE Frontiers in Education Conference (FIE), 1–8. https://doi.org/10.1103/ FIE44824.2020.9274040
Tunggyshbay, M., Balta, N., & Admiraal, W. (2023). Flipped classroom strategies and innovative teaching approaches in physics education: A systematic review. Eurasia Journal of Mathematics, Science and Technology Education, 19(6), Article em2283. https://doi.org/10.29333/ejmste/13258
UNICEF. (2023). SDG 4 Quality Education: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. UNICEF. Retrieved June 12, 2023 from https://www.unicef.org/unicef-andsustainable-development-goals#sdg4
Yadak, P. (2020). A meta-analysis of gender gap on the FCI in high school and college introductory physics courses in the US and international countries (Publication No. 552) [Doctoral dissertation, University of San Francisco]. USF Scholarship: a digital repository @ Gleeson Library | Geschke Center. https://repository. usfca.edu/diss/552
Yunzal, A. N., Jr., & Casinillo, L. F. (2020). Effect of Physics Education Technology (PhET) simulations: Evidence from STEM students' performance. Journal of Education Research and Evaluation, 4(3), 221-226. https://doi.org/10.23887/jere.v4i3.27450
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Jul 23, 2026
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Sales, J. V., Rafon-Paghubasan, J., & Prudente, M. (2026). Learner Outcomes in Calculus-Based Physics Using Physics Education Technology Simulations: A Meta-Analysis. AsTEN Journal of Teacher Education, 10(1). https://doi.org/10.56278/asten.v10i1.4087
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Section 1: Research Articles and Research Briefs
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