A Dual-Agent Adaptive Arcade Mode for Rapid Code Evaluation in Programming Education
Resumo
Introduction: Programming education often emphasizes code construction, but offers fewer opportunities for students to practice rapid code evaluation, a skill related to debugging, code review, maintenance, and identification of bugs and bad practices. Objective: This paper investigates preliminary evidence on the use of an adaptive arcade-style mini-game for rapid code evaluation in introductory programming contexts. Methodology: The mini-game asks players to compare two code snippets and select the correct alternative under time pressure while a fuel resource decreases. Questions are selected by a dual-agent architecture inspired by minimax, balancing diversity and difficulty progression through gameplay telemetry. Results: The evaluation involved 13 active participants, 36 valid sessions, and 1,375 answers. Global accuracy was 87.3%, with an average response time of 3.35 seconds. From the first to the last valid session, mean accuracy increased from 73.9% to 80.1%, mean distance from 30,270 to 44,353, and mean maximum combo from 14.8 to 23.6. Results also showed differences among mechanics and dominance of the progression agent, suggesting the need for utility normalization, parameter tuning, and future comparison with baseline selection strategies.
Palavras-chave:
Game-based learning, Programming education, Adaptive learning, Code evaluation, Question selection
Referências
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Stidham, S. F. (2022). An integrative review of the conceptualization and assessment of the learner flow experience in the digital game-based learning environment between 2011 and 2021. Available in: [link].
Sweetser, P. e Wyeth, P. (2005). Gameflow: A model for evaluating player enjoyment in games. In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pages 3–10. DOI: 10.1145/1077246.1077253.
Takács, P., Balkányi, P., e Vekety, G. B. (2023). Achieving flow-state through gamification in education. ELTE. DOI: 10.1177/0974173920150321.
Zhong, L. e Zhou, J. (2024). Personalized games for computer science education in higher education: the effect of personalization feature on students’ engagement and flow state. Journal of Computing in Higher Education, pages 1–28. DOI: 10.1007/s12528-024-09427-z.
Chou, Y.-S., Hou, H.-T., Chang, K.-E., e Su, C.-L. (2023). Designing cognitive-based game mechanisms for mobile educational games to promote cognitive thinking: an analysis of flow state and game-based learning behavioral patterns. Interactive Learning Environments, 31(5):3285–3302. Available in: [link].
Fowler, M., Beck, K., Brant, J., Opdyke, W., e Roberts, D. (1999). Refactoring: Improving the Design of Existing Code. Addison-Wesley Professional, Reading, MA. Available in: [link].
Fu, F.-L., Su, R.-C., e Yu, S.-C. (2009). Egameflow: A scale to measure learners’ enjoyment of e-learning games. Computers Education, 52(1):101–112. DOI: 10.1016/j.compedu.2008.07.004.
Gkintoni, E., Antonopoulou, H., Sortwell, A., e Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain sciences, 15(2):203. DOI: 10.3390/brainsci15020203.
Idika, S. e Saihi, S. (2025). Enhancing student engagement through ai-driven adaptive learning and gamification. British Journal of Education, 13(12):57–103. DOI: 10.37745/bje.2013/vol13n1257103.
Keuning, H., Jeuring, J., e Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Trans. Comput. Educ., 19(1). Available in: [link].
Kiili, K. (2005). Digital game-based learning: Towards an experiential gaming model. The Internet and Higher Education, 8(1):13–24. DOI: 10.1016/j.iheduc.2004.12.001.
Koh, J., Cowling, M. A., Jha, M., e Sim, K. N. (2024). Design-based research as a value-added methodology for artificial intelligence in education. In Design and Implementation of Higher Education Learners’ Learning Outcomes (HELLO), pages 349–367. IGI Global Scientific Publishing. DOI: 10.4018/978-1-6684-9472-1.ch022.
Kyza, E. A. (2023). Technology-enhanced learning: A learning sciences perspective. In Learning, design, and technology: An international compendium of theory, research, practice, and policy, pages 221–244. Springer. DOI: 10.1007/978-3-319-17461-7_56.
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., Simon, B., e Thomas, L. (2004). A multinational study of reading and tracing skills in novice programmers. ITiCSE-WGR ’04, page 119–150, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/1044550.1041673.
Loor, M. A. M., Solorzano, D. M. A., Katherine, A., e Moreira, V. (2024). Integration of artificial intelligence in english teaching. Journal of Cleaner Producfion, 289:125834. Available in: [link].
Lozano, R., Prabhu, R. S., e Cheon, Y. (2024). Code smells for assessing and improving students’ coding skills and practices. In Innovations in Computing Research, volume 1058 of Lecture Notes in Networks and Systems, pages 617–631. Springer, Cham. DOI: 10.1007/978-3-031-65522-7_21.
Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 5(4):333–369. DOI: 10.1016/S0364-0213(81)80017-1.
Martel, J. S. S. e Garcias, A. P. (2024). A model of assessment co-creation in technology-enhanced learning environments in higher education. Digital Education Review, 45:204–213. Available in: [link].
Pan, Y., Shao, X., e Shakibaei, G. (2025). Influence of digital game-based learning on social collaboration, problem-solving skills, and motivation: An integrative approach of expectancy-value theory and flow theory. Learning and Motivation, 90:102123. DOI: 10.1016/j.lmot.2025.102123.
Ryan, R. M., Rigby, C. S., e Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30(4):344–360. DOI: 10.1007/s11031-006-9051-8.
Sánchez, F. A. P., Barrera, J. P. R., Maldonado, H. O. S., e Sanabria, C. A. C. (2026). Ai-based learning platforms: A systematic review of evaluation metrics for accessibility, interactivity and adaptability through the lens of universal design for learning. DOI: 10.21203/rs.3.rs-8801285/v1.
Scaico, P. D., Oliveira, W., Hamari, J., MilHosseini, S., e Brambilla, A. (2024). Understanding undergraduate students’ flow state in gamified and non-gamified educational systems: A qualitative case study. In 2024 IEEE Frontiers in Education Conference (FIE), pages 1–9. IEEE. DOI: 10.1109/FIE61694.2024.10892842.
Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1):153–189. DOI: 10.3102/0034654307313795.
Singletary, D. R. (2025). Beyond the Flow: A Critical Examination of Engagement and Learning Outcomes in Game-Based Learning Through Bibliometric and Manual Literature Analysis. Boise State University. Available in: [link].
Stidham, S. F. (2022). An integrative review of the conceptualization and assessment of the learner flow experience in the digital game-based learning environment between 2011 and 2021. Available in: [link].
Sweetser, P. e Wyeth, P. (2005). Gameflow: A model for evaluating player enjoyment in games. In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pages 3–10. DOI: 10.1145/1077246.1077253.
Takács, P., Balkányi, P., e Vekety, G. B. (2023). Achieving flow-state through gamification in education. ELTE. DOI: 10.1177/0974173920150321.
Zhong, L. e Zhou, J. (2024). Personalized games for computer science education in higher education: the effect of personalization feature on students’ engagement and flow state. Journal of Computing in Higher Education, pages 1–28. DOI: 10.1007/s12528-024-09427-z.
Publicado
29/09/2026
Como Citar
MARTINS, William; BARBOSA, Kleydson Beckman; CRUZ, Allan K. B. S. da; CÂMARA, Sávio; S. NETO, Carlos de Salles.
A Dual-Agent Adaptive Arcade Mode for Rapid Code Evaluation in Programming Education. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 978-990.
DOI: https://doi.org/10.5753/sbgames.2026.26217.
