Does Game-Based Learning Improve K–12 Mathematics Achievement Beyond Equivalent Practice? A Systematic Review and Multilevel Meta-Analysis of Comparator and Assessment Conditions

Authors

Qiang Zhang

Universiti Teknologi Malaysia (Johor Bahru Johor Malaysia)

Mohamad Ikram Zakaria

Universiti Teknologi Malaysia (Johor Bahru Johor Malaysia)

Norulhuda Ismail

Universiti Teknologi Malaysia (Johor Bahru Johor Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.100701137

Subject Category: Mathematics

Volume/Issue: 10/7 | Page No: 16613-16641

Publication Timeline

Submitted: 2026-08-09

Accepted: 2026-08-14

Published: 2026-08-22

Abstract

Game-based learning (GBL) is widely used in mathematics education, yet its added value depends on what comparison groups receive and how learning is assessed. This systematic review and multilevel meta-analysis examined whether complete digital or non-digital games improve objective K–12 mathematics achievement relative to concurrent non-game instruction or practice, with attention to comparator equivalence and assessment proximity. Four database searches yielded 2,633 records. Independent verification of Rayyan's deduplication history confirmed that 859 duplicates were removed, leaving 1,774 records for title–abstract screening; 150 candidate reports were then sought for retrieval. Sixty reports were not retrieved. Eighty eligible reports represented 79 independent study families. The primary three-level random-effects model included 42 dependent effects from 19 families and used cluster-robust CR2 inference. GBL produced a positive average effect, g = 0.368, 95% CI [0.176, 0.560], p < .001, with substantial residual heterogeneity (approximate I² = 65.5%). Neither comparator nor assessment conditions, nor the other prespecified moderators, reached statistical significance; the limited data did not support conclusions of equivalence. The pooled estimate remained positive across working-correlation assumptions, randomized-design restrictions, the expanded synthesis set, and leave-one-family-out analyses. However, no randomized family was judged at low risk of bias, most non-randomized families were at serious or critical risk, and exploratory diagnostics suggested possible small-study effects. On average, complete mathematics GBL was associated with better achievement than the available non-game comparators, but its added value over fully equivalent practice remains uncertain. Stronger randomized value-added trials using matched practice and both proximal and independent distal assessments are needed.

Keywords

game-based learning; mathematics education; K–12; multilevel meta-analysis; comparator equivalence; assessment proximity

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References

1. Anggoro, B. S., Dewantara, A. H., Suherman, S., Muhammad, R. R., & Saraswati, S. (2025). Effect of game-based learning on students’ mathematics high order thinking skills: A meta-analysis. Revista de Psicodidáctica, 30(1), 500158. https://doi.org/10.1016/j.psicoe.2024.500158 [Google Scholar] [Crossref]

2. Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174. [Google Scholar] [Crossref]

3. https://doi.org/10.20982/tqmp.12.3.p154 [Google Scholar] [Crossref]

4. Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). The effect of digital game-based learning interventions on cognitive, metacognitive, and affective-motivational learning outcomes in school: A meta-analysis. Review of Educational Research, 94(2), 193–227. [Google Scholar] [Crossref]

5. https://doi.org/10.3102/00346543231167795 [Google Scholar] [Crossref]

6. Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088–1101. https://doi.org/10.2307/2533446 [Google Scholar] [Crossref]

7. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Wiley. https://doi.org/10.1002/9780470743386 [Google Scholar] [Crossref]

8. Byun, J., & Joung, E. (2018). Digital game-based learning for K–12 mathematics education: A meta-analysis. School Science and Mathematics, 118(3–4), 113–126. https://doi.org/10.1111/ssm.12271 [Google Scholar] [Crossref]

9. Cheung, A. C. K., & Slavin, R. E. (2016). How methodological features affect effect sizes in education. Educational Researcher, 45(5), 283–292. https://doi.org/10.3102/0013189X16656615 [Google Scholar] [Crossref]

10. Cheung, M. W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19(2), 211–229. https://doi.org/10.1037/a0032968 [Google Scholar] [Crossref]

11. Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86(1), 79–122. [Google Scholar] [Crossref]

12. https://doi.org/10.3102/0034654315582065 [Google Scholar] [Crossref]

13. Conmy, T. (2023). We are still playing: A meta-analysis of game-based learning in mathematics education [Doctoral dissertation]. ProQuest Dissertations & Theses Global. (ERIC No. ED645006) [Google Scholar] [Crossref]

14. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining gamification. In Proceedings of the 15th International Academic MindTrek Conference (pp. 9–15). ACM. https://doi.org/10.1145/2181037.2181040 [Google Scholar] [Crossref]

15. Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x [Google Scholar] [Crossref]

16. Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315, 629–634. https://doi.org/10.1136/bmj.315.7109.629 [Google Scholar] [Crossref]

17. Ersen, Z. B., & Ergül, E. (2022). Trends of game-based learning in mathematics education: A systematic review. International Journal of Contemporary Educational Research, 9(3), 603–623. [Google Scholar] [Crossref]

18. https://doi.org/10.33200/ijcer.1109501 [Google Scholar] [Crossref]

19. Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A research and practice model. Simulation & Gaming, 33(4), 441–467. https://doi.org/10.1177/1046878102238607 [Google Scholar] [Crossref]

20. Habgood, M. P. J., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value of intrinsic integration in educational games. Journal of the Learning Sciences, 20(2), 169–206. https://doi.org/10.1080/10508406.2010.508029 [Google Scholar] [Crossref]

21. Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128. https://doi.org/10.3102/10769986006002107 [Google Scholar] [Crossref]

22. Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39–65. https://doi.org/10.1002/jrsm.5 [Google Scholar] [Crossref]

23. Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186 [Google Scholar] [Crossref]

24. Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions (2nd ed.). Wiley. [Google Scholar] [Crossref]

25. https://doi.org/10.1002/9781119536604 [Google Scholar] [Crossref]

26. Hoffmann, T. C., Glasziou, P. P., Boutron, I., Milne, R., Perera, R., Moher, D., Altman, D. G., Barbour, V., Macdonald, H., Johnston, M., Lamb, S. E., Dixon-Woods, M., McCulloch, P., Wyatt, J. C., Chan, A.-W., & Michie, S. (2014). Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide. BMJ, 348, g1687. https://doi.org/10.1136/bmj.g1687 [Google Scholar] [Crossref]

27. Hui, H. B., & Mahmud, M. S. (2023). Influence of game-based learning in mathematics education on the students’ cognitive and affective domain: A systematic review. Frontiers in Psychology, 14, 1105806. https://doi.org/10.3389/fpsyg.2023.1105806 [Google Scholar] [Crossref]

28. Kaçmaz, G., & Dubé, A. K. (2022). Examining pedagogical approaches and types of mathematics knowledge in educational games: A meta-analysis and critical review. Educational Research Review, 35, 100428. https://doi.org/10.1016/j.edurev.2021.100428 [Google Scholar] [Crossref]

29. Ke, F. (2016). Designing and integrating purposeful learning in game play: A systematic review. Educational Technology Research and Development, 64(2), 219–244. https://doi.org/10.1007/s11423-015-9418-1 [Google Scholar] [Crossref]

30. Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241–253. https://doi.org/10.3102/0013189X20912798 [Google Scholar] [Crossref]

31. Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review, 22(3), 215–243. https://doi.org/10.1007/s10648-010-9125-8 [Google Scholar] [Crossref]

32. National Council of Teachers of Mathematics. (2014). Principles to actions: Ensuring mathematical success for all. Author. [Google Scholar] [Crossref]

33. Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5, 210. https://doi.org/10.1186/s13643-016-0384-4 [Google Scholar] [Crossref]

34. Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

35. Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258–283. https://doi.org/10.1080/00461520.2015.1122533 [Google Scholar] [Crossref]

36. Pustejovsky, J. E., & Tipton, E. (2022). Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science, 23(3), 425–438. https://doi.org/10.1007/s11121-021-01246-3 [Google Scholar] [Crossref]

37. Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., et al. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10, 39. [Google Scholar] [Crossref]

38. https://doi.org/10.1186/s13643-020-01542-z [Google Scholar] [Crossref]

39. Rodgers, M. A., & Pustejovsky, J. E. (2021). Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. Psychological Methods, 26(2), 141–160. [Google Scholar] [Crossref]

40. https://doi.org/10.1037/met0000300 [Google Scholar] [Crossref]

41. Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32, 77–112. https://doi.org/10.1007/s10648-019-09498-w [Google Scholar] [Crossref]

42. Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60–78. https://doi.org/10.1002/jrsm.1095 [Google Scholar] [Crossref]

43. Sterne, J. A. C., Hernán, M. A., Reeves, B. C., et al. (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 [Google Scholar] [Crossref]

44. Sterne, J. A. C., Savović, J., Page, M. J., et al. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. https://doi.org/10.1136/bmj.l4898 [Google Scholar] [Crossref]

45. Tokac, U., Novak, E., & Thompson, C. G. (2019). Effects of game-based learning on students’ mathematics achievement: A meta-analysis. Journal of Computer Assisted Learning, 35(3), 407–420. https://doi.org/10.1111/jcal.12347 [Google Scholar] [Crossref]

46. Valentine, J. C., Pigott, T. D., & Rothstein, H. R. (2010). How many studies do you need? A primer on statistical power for meta-analysis. Journal of Educational and Behavioral Statistics, 35(2), 215–247. https://doi.org/10.3102/1076998609346961 [Google Scholar] [Crossref]

47. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03 [Google Scholar] [Crossref]

48. Viechtbauer, W., & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1(2), 112–125. https://doi.org/10.1002/jrsm.11 [Google Scholar] [Crossref]

49. Wouters, P., & van Oostendorp, H. (2013). A meta-analytic review of the role of instructional support in game-based learning. Computers & Education, 60(1), 412–425. [Google Scholar] [Crossref]

50. https://doi.org/10.1016/j.compedu.2012.07.018 [Google Scholar] [Crossref]

51. Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105(2), 249–265. https://doi.org/10.1037/a0031311 [Google Scholar] [Crossref]

52. Yao, D. (2026). The impact of gamified learning in mathematics education on students’ cognitive and affective outcomes: Insights from a second-order meta-analysis. British Educational Research Journal. Advance online publication. https://doi.org/10.1002/berj.70144 [Google Scholar] [Crossref]

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