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

ArXiv|Friday, September 18, 2026|3 days ago|Samuel Girard

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As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy.

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Key Claims

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...

Original description

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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ID: http://arxiv.org/abs/2609.21791v1 · Indexed about 8 hours ago