用大模型和双曲空间建模知识层级，提升学习轨迹追踪精度
Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space
- 用大模型构建知识点层级关系，模拟学生行为生成合成数据
- 在双曲空间中对齐真实与合成数据分布，降低难度与遗忘差异
- 显式建模知识结构层次，精准捕捉不同层级知识点的学习曲线
知识追踪（KT）通过持续监控学习状态诊断学生对知识点的掌握程度。现有方法主要依赖基于ID或浅层文本特征的行为序列，难以捕捉认知状态的层次演化以及个体对题目难度的差异化感知，因语义建模能力有限。为此，本文提出大语言模型双曲对齐知识追踪（L-HAKT）。首先，教师代理深度解析题目语义，显式构建知识点间的层次依赖；学生代理模拟学习行为生成合成数据。随后，在双曲空间中对合成数据与真实数据进行对比学习，以缩小问题难度、遗忘模式等关键特征的分布差异。最后，通过优化双曲曲率，显式建模知识点树状层次结构，精确刻画不同层级知识点的学习曲线形态。在四个真实教育数据集上的大量实验验证了所提框架的有效性。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) diagnoses students' concept mastery through continuous learning state monitoring in education.Existing methods primarily focus on studying behavioral sequences based on ID or textual information.While existing methods rely on ID-based sequences or shallow textual features, they often fail to capture (1) the hierarchical evolution of cognitive states and (2) individualized problem difficulty perception due to limited semantic modeling. Therefore, this paper proposes a Large Language Model Hyperbolic Aligned Knowledge Tracing(L-HAKT). First, the teacher agent deeply parses question semantics and explicitly constructs hierarchical dependencies of knowledge points; the student agent simulates learning behaviors to generate synthetic data. Then, contrastive learning is performed between synthetic and real data in hyperbolic space to reduce distribution differences in key features such as question difficulty and forgetting patterns. Finally, by optimizing hyperbolic curvature, we explicitly model the tree-like hierarchical structure of knowledge points, precisely characterizing differences in learning curve morphology for knowledge points at different levels. Extensive experiments on four real-world educational datasets validate the effectiveness of our Large Language Model Hyperbolic Aligned Knowledge Tracing (L-HAKT) framework.
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