发表机构
Inria-Saclay; University of Illinois Urbana-Champaign; Telecom-Sud Paris（法国国家信息与自动化研究所萨克雷中心; 伊利诺伊大学厄巴纳-香槟分校; 南巴黎电信学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对深度学习知识追踪模型可解释性差和易过拟合的问题，本文提出一种基于IRT正则化的新方法，同时提升模型稳健性与可解释性，以适用于真实教育应用。
AI 中文摘要
随着深度学习模型的不断进步，知识追踪模型已取得更高的准确性。然而，这些提升以降低可解释性为代价，而可解释性对于教育环境中的实践者采用新方法至关重要。此外，深度学习模型容易过拟合，尤其是在处理教育应用中常见的小型数据集时。在本文中，我们提出了一种新颖的正则化技术，旨在增强基于深度学习的知识追踪模型的稳健性，同时提高其可解释性。我们的方法同时解决了可解释性和过拟合的挑战，使其更适用于真实世界的教育应用。
英文摘要
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.
