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

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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 interpret

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