# RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer > Source: > Published: 2026-09-21 04:00:00+00:00 # RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer By Samuel Girard, Juan D. Pinto, Jill-J\^enn Vie, Amel BouzeghoubSource: [arXiv cs.LG](https://arxiv.org/list/cs.LG/recent) arXiv:2609.21791v1 Announce Type: new Abstract: As [deep learning](https://www.machinebrief.com/glossary/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](https://www.machinebrief.com/glossary/overfitting), particularly when dealing with the small datasets that are common in educational applications. In this paper, we propose a novel[regularization](https://www.machinebrief.com/glossary/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. Get AI news in your inbox Daily digest of what matters in AI. ## Key Terms Explained Deep Learning A subset of machine learning that uses neural networks with many layers (hence 'deep') to learn complex patterns from large amounts of data. Overfitting When a model memorizes the training data so well that it performs poorly on new, unseen data. Regularization Techniques that prevent a model from overfitting by adding constraints during training.