Recomendação Sequencial com Filtragem Pedagógica baseada em Knowledge Tracing para Plataformas de Ensino de Algoritmos
Resumo
Este trabalho propõe e avalia um pipeline de quatro estágios para recomendação de exercícios de programação que integra recomendação sequencial e rastreamento do conhecimento. O método combina o GRU4Rec, selecionado entre cinco arquiteturas de recomendação sequencial, com o Deep Knowledge Tracing (DKT), escolhido entre cinco modelos de Knowledge Tracing pelo melhor equilíbrio entre acurácia e custo computacional. O DKT atua como filtro pedagógico sobre o ranking do GRU4Rec, suprimindo exercícios cuja probabilidade de acerto estimada esteja abaixo de um limiar adaptativo por exercício. Experimentos conduzidos em sete datasets de plataformas de programação (Cosmo, BePKT, CodeNet, CodeNet-len, CodeNet-time, POJ e CodeForces), com perfis variados de escala e esparsidade, mostram que a filtragem pedagógica produz melhorias em todas as métricas de ranqueamento avaliadas, com impacto mais expressivo no posicionamento do exercício relevante no topo do ranking.
Palavras-chave:
Recomendação Sequencial, Knowledge Tracing, Ensino de Programação
Referências
Barbosa, P. L. S., Carmo, R. A. F. d., Gomes, J. P., and Viana, W. (2024). Adaptive learning in computer science education: A scoping review. Education and Information Technologies, 29(8):9139–9188.
Ghosh, A., Heffernan, N., and Lan, A. S. (2020). Context-aware attentive knowledge tracing. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2330–2339.
Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2016). Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR).
Hou, Y., Mu, S., Zhao, W. X., Li, Y., Ding, B., and Wen, J.-R. (2022). CORE: Simple and effective session-based recommendation within consistent representation space. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1796–1801.
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 197–206.
Liu, Y., Zhu, R., and Gao, M. (2023). Personalized programming guidance based on deep programming learning style capturing. In International Conference on Computer Science and Education, pages 214–231. Springer.
Mou, L., Li, G., Zhang, L., Wang, T., and Jin, Z. (2016). Convolutional neural networks over tree structures for programming language processing. In Proceedings of the AAAI conference on artificial intelligence.
Muepu, D. M. and Watanobe, Y. (2024). From code to ratings: Converting programming data to enhance collaborative filtering in educational online judge systems. IEEE Access, 12:20239–20251.
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., and Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in neural information processing systems, 28.
Puri, R., Kung, D. S., Janssen, G., Zhang, W., Domeniconi, G., Zolotov, V., Dolby, J., Chen, J., Choudhury, M., Decker, L., et al. (2021). Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks. arXiv preprint arXiv:2105.12655.
Quadrana, M., Cremonesi, P., and Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys, 51(4):1–36.
Shen, S., Liu, Q., Huang, Z., Zheng, Y., Yin, M., Wang, M., and Chen, E. (2021). A survey of knowledge tracing: Models, variants, and applications. IEEE Transactions on Learning Technologies.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformers. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), pages 1441–1450.
Tan, Y., Liu, H., Gao, J., Lin, K., and Shi, Y. (2021). SINE: Sparse interest network for sequential recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM), pages 202–210.
Volostnov, D. (2024). Codeforces Users Submissions Dataset (End of 2024). [link]. Acesso em: 2024.
Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q. Z., and Orgun, M. (2020). Sequential recommender systems: Challenges, progress and prospects. arXiv preprint arXiv:2001.04830.
Watanobe, Y., Rahman, M. M., Matsumoto, T., Rage, U. K., and Ravikumar, P. (2022). Online judge system: Requirements, architecture, and experiences. International Journal of Software Engineering and Knowledge Engineering, 32(06):917–946.
Xu, Y., Ni, Q., Liu, S., Mi, Y., Yu, Y., and Hao, Y. (2022). Learning style integrated deep reinforcement learning framework for programming problem recommendation in online judge system. International Journal of Computational Intelligence Systems, 15(114).
Zhang, J., Shi, X., King, I., and Yeung, D.-Y. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (WWW), pages 765–774.
Zhao, W. X., Mu, S., Hou, Y., Lin, W., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021). RecBole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM), pages 4653–4664.
Zheng, W., Du, Q., Fan, Y., Tan, L., Xia, C., and Yang, F. (2022). A personalized programming exercise recommendation algorithm based on knowledge structure tree. Journal of Intelligent & Fuzzy Systems, 42:2169–2180.
Zhu, R., Zhang, D., Han, C., Gaol, M., Lu, X., Qian, W., and Zhou, A. (2022). Programming knowledge tracing: A comprehensive dataset and a new model. In 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pages 298–307. IEEE.
Ghosh, A., Heffernan, N., and Lan, A. S. (2020). Context-aware attentive knowledge tracing. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2330–2339.
Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2016). Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR).
Hou, Y., Mu, S., Zhao, W. X., Li, Y., Ding, B., and Wen, J.-R. (2022). CORE: Simple and effective session-based recommendation within consistent representation space. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1796–1801.
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 197–206.
Liu, Y., Zhu, R., and Gao, M. (2023). Personalized programming guidance based on deep programming learning style capturing. In International Conference on Computer Science and Education, pages 214–231. Springer.
Mou, L., Li, G., Zhang, L., Wang, T., and Jin, Z. (2016). Convolutional neural networks over tree structures for programming language processing. In Proceedings of the AAAI conference on artificial intelligence.
Muepu, D. M. and Watanobe, Y. (2024). From code to ratings: Converting programming data to enhance collaborative filtering in educational online judge systems. IEEE Access, 12:20239–20251.
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., and Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in neural information processing systems, 28.
Puri, R., Kung, D. S., Janssen, G., Zhang, W., Domeniconi, G., Zolotov, V., Dolby, J., Chen, J., Choudhury, M., Decker, L., et al. (2021). Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks. arXiv preprint arXiv:2105.12655.
Quadrana, M., Cremonesi, P., and Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys, 51(4):1–36.
Shen, S., Liu, Q., Huang, Z., Zheng, Y., Yin, M., Wang, M., and Chen, E. (2021). A survey of knowledge tracing: Models, variants, and applications. IEEE Transactions on Learning Technologies.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformers. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), pages 1441–1450.
Tan, Y., Liu, H., Gao, J., Lin, K., and Shi, Y. (2021). SINE: Sparse interest network for sequential recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM), pages 202–210.
Volostnov, D. (2024). Codeforces Users Submissions Dataset (End of 2024). [link]. Acesso em: 2024.
Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q. Z., and Orgun, M. (2020). Sequential recommender systems: Challenges, progress and prospects. arXiv preprint arXiv:2001.04830.
Watanobe, Y., Rahman, M. M., Matsumoto, T., Rage, U. K., and Ravikumar, P. (2022). Online judge system: Requirements, architecture, and experiences. International Journal of Software Engineering and Knowledge Engineering, 32(06):917–946.
Xu, Y., Ni, Q., Liu, S., Mi, Y., Yu, Y., and Hao, Y. (2022). Learning style integrated deep reinforcement learning framework for programming problem recommendation in online judge system. International Journal of Computational Intelligence Systems, 15(114).
Zhang, J., Shi, X., King, I., and Yeung, D.-Y. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th International Conference on World Wide Web (WWW), pages 765–774.
Zhao, W. X., Mu, S., Hou, Y., Lin, W., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021). RecBole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM), pages 4653–4664.
Zheng, W., Du, Q., Fan, Y., Tan, L., Xia, C., and Yang, F. (2022). A personalized programming exercise recommendation algorithm based on knowledge structure tree. Journal of Intelligent & Fuzzy Systems, 42:2169–2180.
Zhu, R., Zhang, D., Han, C., Gaol, M., Lu, X., Qian, W., and Zhou, A. (2022). Programming knowledge tracing: A comprehensive dataset and a new model. In 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pages 298–307. IEEE.
Publicado
05/10/2026
Como Citar
MARANHÃO, Djefferson; SOARES NETO, Carlos de Salles.
Recomendação Sequencial com Filtragem Pedagógica baseada em Knowledge Tracing para Plataformas de Ensino de Algoritmos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1474-1488.
DOI: https://doi.org/10.5753/sbie.2026.27858.
