Knowledge Tracing (KT) aims to dynamically model a student's mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery. To address this issue, we propose a Knowledge Mastery-State Disambiguation for Knowledge Tracing model (KeenKT), which represents a student's knowledge state at each interaction using a Normal-Inverse-Gaussian (NIG) distribution, thereby capturing the fluctuations in student learning behaviors. Furthermore, we design an NIG-distance-based attention mechanism to model the dynamic evolution of the knowledge state. In addition, we introduce a diffusion-based denoising reconstruction loss and a distributional contrastive learning loss to enhance the model's robustness. Extensive experiments on six public datasets demonstrate that KeenKT outperforms SOTA KT models in terms of prediction accuracy and sensitivity to behavioral fluctuations. The proposed method yields the maximum AUC improvement of 5.85% and the maximum ACC improvement of 6.89%.

翻译：知识追踪（Knowledge Tracing，KT）旨在根据学生的历史学习交互动态建模其对知识概念的掌握程度。现有方法大多依赖单点估计，无法区分真实能力与偶然发挥或粗心失误，导致掌握状态判断存在歧义。为解决该问题，本文提出一种面向知识追踪的知识掌握状态消歧模型（KeenKT），该模型采用正态逆高斯（Normal-Inverse-Gaussian，NIG）分布表征学生在每次交互时的知识状态，从而捕捉学习行为中的波动性。进一步，我们设计了基于NIG距离的注意力机制来建模知识状态的动态演化过程。此外，通过引入基于扩散的去噪重构损失与分布对比学习损失，增强了模型的鲁棒性。在六个公开数据集上的大量实验表明，KeenKT在预测精度和对行为波动的敏感性方面均优于当前最优知识追踪模型。所提方法实现了最高5.85%的AUC提升与最高6.89%的ACC提升。