RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer

Abstract:As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced interpretability, which is crucial for practitioners in educational settings to adopt new methodologies. Additionally, deep learning models are prone to overfitting, particularly when dealing with the small datasets that are common in educational applications. In this paper, we propose a novel regularization technique designed to enhance the robustness of deep-learning-based knowledge tracing models, while simultaneously improving their interpretability. Our method addresses both the interpretability and overfitting challenges, making it more feasible for real-world educational applications.

Machine Learning (cs.LG)

arXiv:2609.21791 [cs.LG]

(or arXiv:2609.21791v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.21791 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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