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

2026-09-18

AI Summary

本文提出了一种新的正则化技术RegKT，旨在提高基于深度学习的知识追踪模型的鲁棒性和可解释性，以解决教育应用中的过拟合和难以解释的问题。

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.

Problem

• knowledge tracing
• interpretability
• overfitting

Innovation

• IRT-Regularizer
• Interpretable
• Robust
• Deep Knowledge Tracing