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Q-learning Adaptive Mathematics Game for Early Numeracy Learning Evaluation
Corresponding Author(s) : Rio Andriyat Krisdiawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 4, November 2026 (Article in Progress)
Abstract
Early numeracy learning in elementary school requires learning media that can respond to differences in students’ abilities, especially when students show different levels of accuracy, response time, help use, and task completion. This study evaluated a Q-learning-based adaptive mathematics game designed to support early numeracy learning among first-grade elementary students. The evaluation focused on pretest–posttest learning outcome changes and adaptive model performance. The study involved 38 first-grade students from SDN 1 Langseb and used a one-group pretest-posttest design. Students completed a pretest, played a six-level adaptive mathematics game, completed a posttest, and were observed by the teacher and researcher during implementation. Learning outcome changes were analyzed using gain, N-Gain, and the Wilcoxon Signed-Rank Test. Adaptive model performance was evaluated through student states, adaptive actions, reward behavior, Q-value update, difficulty transition, decision consistency, and classroom feasibility. The results showed that the mean score increased from 43.95 in the pretest to 80.79 in the posttest, with a mean N-Gain of 0.666. The Wilcoxon test indicated a significant difference between pretest and posttest scores, with p < 0.001. The model produced 228 adaptive decisions across six levels, with Increase, Maintain, and Decrease/Reinforcement actions. The reward pattern was consistent with the adaptive logic, and the mean Q-value increased from 1.115 to 1.199. The findings show improved early-numeracy scores after the intervention and interpretable adaptive difficulty behavior in the observed classroom context. Because the study used a one-group pretest–posttest design without a comparison group, the observed improvement cannot be attributed solely to the adaptive game.
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References
Pusat Asesmen Pendidikan, “Kerangka Asesmen Tes Kemampuan Akademik Jenjang SD/MI/Sederajat: Matematika SD Sederajat,” Kementerian Pendidikan Dasar dan Menengah, 2026. Accessed: May 31, 2026. [Online]. Available: https://pusmendik.kemendikdasmen.go.id/tka/tka/view/mata-pelajaran-wajib/sd
N. Behnamnia, A. Kamsin, and S. Hayati, “Impact of Digital Game-Based Learning on STEM education in primary schools: a meta-analysis of learning approaches,” Innoeduca. International Journal of Technology and Educational Innovation, vol. 10, no. 2, pp. 113–140, Dec. 2024, doi: 10.24310/ijtei.102.2024.19694.
E. Debrenti, “Game-Based Learning experiences in primary mathematics education,” Front. Educ. (Lausanne)., vol. 9, Mar. 2024, doi: 10.3389/feduc.2024.1331312.
D. Chiotaki, V. Poulopoulos, and K. Karpouzis, “Adaptive game-based learning in education: a systematic review,” 2023, Frontiers Media SA. doi: 10.3389/fcomp.2023.1062350.
R. Krisdiawan, T. Sugiharto, N. AmaliaAsikin, and L. Rohmawati, “UI/UX Design for AR Card Game: Enhancing English Vocabulary Learning with Augmented Reality,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Jun. 2025, doi: 10.22219/kinetik.v10i3.2240.
A. Riedmann, P. Schaper, and B. Lugrin, “Reinforcement Learning in Education: A Systematic Literature Review,” Int. J. Artif. Intell. Educ., vol. 35, no. 5, pp. 2669–2723, Dec. 2025, doi: 10.1007/s40593-025-00494-6.
I. Amzil, S. Aammou, Z. Tagdimi, and H. Erradi, “Personalizing Learning Experiences with Q-Learning in Adaptive Educational Systems,” 2023, pp. 70–77. doi: 10.2991/978-94-6463-360-3_9.
C. J. C. H. Watkins and P. Dayan, “Q-learning,” Mach. Learn., vol. 8, no. 3–4, pp. 279–292, May 1992, doi: 10.1007/BF00992698.
M. F. Contrino, M. Reyes-Millán, P. Vázquez-Villegas, and J. Membrillo-Hernández, “Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach,” Smart Learning Environments, vol. 11, no. 1, p. 6, Feb. 2024, doi: 10.1186/s40561-024-00292-y.
E. du Plooy, D. Casteleijn, and D. Franzsen, “Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement,” Heliyon, vol. 10, no. 21, p. e39630, Nov. 2024, doi: 10.1016/j.heliyon.2024.e39630.
F. Ouyang and P. Jiao, “Artificial intelligence in education: The three paradigms,” Computers and Education: Artificial Intelligence, vol. 2, p. 100020, 2021, doi: 10.1016/j.caeai.2021.100020.
T. Kabudi, I. Pappas, and D. H. Olsen, “AI-enabled adaptive learning systems: A systematic mapping of the literature,” Computers and Education: Artificial Intelligence, vol. 2, p. 100017, 2021, doi: 10.1016/j.caeai.2021.100017.
L. Climent, A. Longhi, A. Arbelaez, and M. Mancini, “A framework for designing Reinforcement Learning agents with Dynamic Difficulty Adjustment in single-player action video games,” Entertain. Comput., vol. 50, p. 100686, May 2024, doi: 10.1016/j.entcom.2024.100686.
T. N. De Oliveira and L. Chaimowicz, “Investigating Reinforcement Learning for Dynamic Difficulty Adjustment,” in Proceedings of the 22nd Brazilian Symposium on Games and Digital Entertainment, New York, NY, USA: ACM, Nov. 2023, pp. 66–75. doi: 10.1145/3631085.3631229.
X. Wang et al., “The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis,” Journal of Educational Computing Research, vol. 62, no. 6, pp. 1348–1383, Oct. 2024, doi: 10.1177/07356331241240459.
B. Capili and J. K. Anastasi, “An Introduction to Types of Quasi-Experimental Designs,” AJN, American Journal of Nursing, vol. 124, no. 11, pp. 50–52, Nov. 2024, doi: 10.1097/01.NAJ.0001081740.74815.20.
I. Sarlis, D. Kotsifakos, and C. Douligeris, “Evaluating Educational Game Design Through Human–Machine Pair Inspection: Case Studies in Adaptive Learning Environments,” Multimodal Technologies and Interaction, vol. 9, no. 9, p. 92, Sep. 2025, doi: 10.3390/mti9090092.
D. Lakens, “Sample Size Justification,” Collabra Psychol., vol. 8, no. 1, Mar. 2022, doi: 10.1525/collabra.33267.
R. R. Hake, “Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses,” Am. J. Phys., vol. 66, no. 1, pp. 64–74, Jan. 1998, doi: 10.1119/1.18809.
D. C. Funder and D. J. Ozer, “Evaluating Effect Size in Psychological Research: Sense and Nonsense,” Adv. Methods Pract. Psychol. Sci., vol. 2, no. 2, pp. 156–168, Jun. 2019, doi: 10.1177/2515245919847202.
W. Lu, J. Griffin, T. D. Sadler, J. Laffey, and S. P. Goggins, “Serious Game Analytics by Design: Feature Generation and Selection Using Game Telemetry and Game Metrics,” Journal of Learning Analytics, vol. 10, no. 1, pp. 168–188, Mar. 2023, doi: 10.18608/jla.2023.7681.
W. Lu, J. Griffin, T. D. Sadler, J. Laffey, and S. P. Goggins, “Game-Based Learning Prediction Model Construction,” Journal of Learning Analytics, vol. 12, no. 1, pp. 293–321, Mar. 2025, doi: 10.18608/jla.2025.8105.
C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” WIREs Data Mining and Knowledge Discovery, vol. 10, no. 3, May 2020, doi: 10.1002/widm.1355.
D. Rey and M. Neuhäuser, “Wilcoxon-Signed-Rank Test,” in International Encyclopedia of Statistical Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1658–1659. doi: 10.1007/978-3-642-04898-2_616.
N. N. Dan, L. T. B. T. Trung, N. T. Nga, and T. M. Dung, “Digital game-based learning in mathematics education at primary school level: A systematic literature review,” Eurasia Journal of Mathematics, Science and Technology Education, vol. 20, no. 4, p. em2423, Apr. 2024, doi: 10.29333/ejmste/14377.
M. J. Gomez, J. A. Ruipérez-Valiente, and F. J. G. Clemente, “A Systematic Literature Review of Game-Based Assessment Studies: Trends and Challenges,” IEEE Transactions on Learning Technologies, vol. 16, no. 4, pp. 500–515, Aug. 2023, doi: 10.1109/TLT.2022.3226661.
S. K. Banihashem, H. Dehghanzadeh, D. Clark, O. Noroozi, and H. J. A. Biemans, “Learning analytics for online game-Based learning: a systematic literature review,” Behaviour & Information Technology, vol. 43, no. 12, pp. 2689–2716, Sep. 2024, doi: 10.1080/0144929X.2023.2255301.