DEEP KNOWLEDGE TRACING WITH ATTENTION MECHANISM FOR PERSONALIZED LEARNING PATH RECOMMENDATION IN INTELLIGENT TUTORING SYSTEMS

Komronbek H. Obloev

Authors

Komronbek H. Obloev Asia International University

Keywords:

Deep Knowledge Tracing, Attention Mechanism, Intelligent Tutoring Systems, Personalized Learning, LSTM, Educational Data Mining, Adaptive Learning Paths.

Abstract

The advancement of intelligent tutoring systems (ITS) requires accurate modeling of student knowledge states over time. Traditional Bayesian Knowledge Tracing (BKT) and rule-based methods, while interpretable, suffer from limited expressive power and an inability to capture long-range dependencies between learning interactions. This study proposes an Attention-based Deep Knowledge Tracing (A-DKT) model that integrates a self-attention mechanism with a Long Short-Term Memory (LSTM) network to predict student mastery and recommend personalized learning paths in higher education environments.

References

Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep Knowledge Tracing. Advances in Neural Information Processing Systems, 28.

Pandey, S., & Karypis, G. (2019). A Self-Attentive Model for Knowledge Tracing. Proceedings of the 12th International Conference on Educational Data Mining (EDM).

Ghosh, A., Heffernan, N., & Lan, A. S. (2020). Context-Aware Attentive Knowledge Tracing. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

Corbett, A. T., & Anderson, J. R. (1995). Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.

Zhang, J., Shi, X., King, I., & Yeung, D.-Y. (2017). Dynamic Key-Value Memory Networks for Knowledge Tracing. Proceedings of the 26th International Conference on World Wide Web.

Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.

Obloev, K. H. (2026). Adaptive Reinforcement Learning Framework for Personalized Educational Trajectories in AI-Driven Higher Education Systems.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Center for Curriculum Redesign.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y. (2021). SAINT+: Integrating Temporal Features for EdNet Correctness Prediction. Proceedings of the 11th International Conference on Learning Analytics and Knowledge.