用概率分布建模学生知识状态，区分真实能力与偶然表现。
KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing
- 用NIG分布表示知识状态，捕捉学习波动
- 在6个数据集上最高提升6.89%准确率
- 适合关注学习行为细微变化的研究者
知识追踪（KT）旨在根据学生的历史学习交互动态建模其对知识点的掌握程度。现有方法多依赖单一数值估计，无法区分真实能力与偶然发挥或粗心失误，导致掌握状态判断模糊。为此，我们提出知识掌握状态消歧模型KeenKT，将每次交互中的学生知识状态建模为正态逆高斯（NIG）分布，从而捕捉学习行为的波动性。进一步设计基于NIG距离的注意力机制，建模知识状态的动态演化。同时引入基于扩散的去噪重建损失和分布对比学习损失，增强模型鲁棒性。在六个公开数据集上的大量实验表明，KeenKT在预测准确率和对行为波动的敏感性方面均优于当前最优方法，最大AUC提升5.85%，最大ACC提升6.89%。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。