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

arXiv: https://arxiv.org/abs/2609.21791 · HTML: https://mlsanity.com/paper/2609.21791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory](https://mlsanity.com/paper/1904.11738/) (2019)
- [Towards Interpretable Deep Learning Models for Knowledge Tracing](https://mlsanity.com/paper/2005.06139/) (2020)
- [A Theoretical Framework for AI Models Explainability with Application in Biomedicine](https://mlsanity.com/paper/2212.14447/) (2022)
- [A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models](https://mlsanity.com/paper/2403.07322/) (2024)
- [On the Interpretability of Deep Learning Based Models for Knowledge Tracing](https://mlsanity.com/paper/2101.11335/) (2021)
- [Towards a Unified Framework for Evaluating Explanations](https://mlsanity.com/paper/2405.14016/) (2024)

## Abstract (arXiv)

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.