Seleção Adaptativa de Questões para Avaliação Rápida de Código em um Ambiente Gamificado de Ensino de Programação
Resumo
O ensino introdutório de programação costuma enfatizar a escrita de código e a avaliação automática, oferecendo menos oportunidades para desenvolver a avaliação crítica de código existente. Este artigo apresenta e analisa um modo gamificado para avaliação rápida de código, no qual estudantes comparam dois trechos de código sob restrição de tempo e com feedback imediato. A proposta utiliza seleção adaptativa de questões, equilibrando diversidade de conteúdo e progressão de dificuldade. Com base em telemetria real de uso, foram analisadas 3.774 respostas válidas, associadas a 83 sessões e 29 estudantes. Os resultados indicam acurácia geral de 89,98% nas respostas às questões de avaliação rápida de código. Entretanto, esse desempenho não se distribui de forma uniforme: há variações por conceito de programação e por tipo de erro avaliado. As menores acurácias ocorrem em categorias que exigem julgamento mais conceitual, como boas práticas, escopo e comparação, sugerindo que a telemetria pode apoiar o diagnóstico de dificuldades e a recalibração das questões adaptativas.
Palavras-chave:
Avaliação de Código, Gamificação, Seleção Adaptativa de Questões
Referências
Anderson, L. W. and Krathwohl, D. R., editors (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. Longman, New York, NY.
Chiotaki, D., Poulopoulos, V., and Karpouzis, K. (2023). Adaptive game-based learning in education: A systematic review. Frontiers in Computer Science, 5:1062350.
Chou, Y.-S., Hou, H.-T., Chang, K.-E., and 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.
Chrysafiadi, K. and Virvou, M. (2024). Perfusit: Personalized fuzzy logic strategies for intelligent tutoring of programming. Electronics, 13(23):4827.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row, New York, NY.
Fowler, M., Beck, K., Brant, J., Opdyke, W., and Roberts, D. (1999). Refactoring: Improving the Design of Existing Code. Addison-Wesley Professional, Reading, MA.
Hong, Y., Saab, N., and Admiraal, W. (2024). Approaches and game elements used to tailor digital gamification for learning: A systematic literature review. Computers & Education, 212:105000.
Keuning, H., Jeuring, J., and Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Trans. Comput. Educ., 19(1).
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., Simon, B., and Thomas, L. (2004). A multi-national study of reading and tracing skills in novice programmers. ITiCSE-WGR '04, page 119–150, New York, NY, USA. Association for Computing Machinery.
Lozano, R., Prabhu, R. S., and 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.
Pădurean, V.-A., Denny, P., and Singla, A. (2025). BugSpotter: Automated generation of code debugging exercises. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, SIGCSE TS 2025, pages 896–902, New York, NY, USA. Association for Computing Machinery.
Scaico, P. D., Oliveira, W., Hamari, J., MilHosseini, S., and 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.
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.
Zhan, Z., He, L., Tong, Y., Liang, X., Guo, S., and Lan, X. (2022). The effectiveness of gamification in programming education: Evidence from a meta-analysis. Computers and Education: Artificial Intelligence, 3:100096.
Zhong, L. and 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.
Chiotaki, D., Poulopoulos, V., and Karpouzis, K. (2023). Adaptive game-based learning in education: A systematic review. Frontiers in Computer Science, 5:1062350.
Chou, Y.-S., Hou, H.-T., Chang, K.-E., and 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.
Chrysafiadi, K. and Virvou, M. (2024). Perfusit: Personalized fuzzy logic strategies for intelligent tutoring of programming. Electronics, 13(23):4827.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row, New York, NY.
Fowler, M., Beck, K., Brant, J., Opdyke, W., and Roberts, D. (1999). Refactoring: Improving the Design of Existing Code. Addison-Wesley Professional, Reading, MA.
Hong, Y., Saab, N., and Admiraal, W. (2024). Approaches and game elements used to tailor digital gamification for learning: A systematic literature review. Computers & Education, 212:105000.
Keuning, H., Jeuring, J., and Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Trans. Comput. Educ., 19(1).
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Moström, J. E., Sanders, K., Seppälä, O., Simon, B., and Thomas, L. (2004). A multi-national study of reading and tracing skills in novice programmers. ITiCSE-WGR '04, page 119–150, New York, NY, USA. Association for Computing Machinery.
Lozano, R., Prabhu, R. S., and 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.
Pădurean, V.-A., Denny, P., and Singla, A. (2025). BugSpotter: Automated generation of code debugging exercises. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, SIGCSE TS 2025, pages 896–902, New York, NY, USA. Association for Computing Machinery.
Scaico, P. D., Oliveira, W., Hamari, J., MilHosseini, S., and 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.
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.
Zhan, Z., He, L., Tong, Y., Liang, X., Guo, S., and Lan, X. (2022). The effectiveness of gamification in programming education: Evidence from a meta-analysis. Computers and Education: Artificial Intelligence, 3:100096.
Zhong, L. and 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.
Publicado
05/10/2026
Como Citar
MARTINS, William; BARBOSA, Kleydson Beckman; DA CRUZ, Allan K. B. S.; CÂMARA, Sávio; S. NETO, Carlos de Salles.
Seleção Adaptativa de Questões para Avaliação Rápida de Código em um Ambiente Gamificado de Ensino de Programação. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2884-2897.
DOI: https://doi.org/10.5753/sbie.2026.28313.
