通过动态解题过程建模，提升知识追踪的准确性
Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing
- 基于波利亚解题框架，将题目拆解为四阶段动态过程
- 在XES3G5M和NIPS34上显著优于基线模型，重复交互下增益更大
- 适配不同学习者风格，可解释性强，适合教育智能系统
知识追踪（KT）旨在根据学习者的历史交互预测其未来表现。尽管近期方法通过学习与知识组件对齐的题目表示有所提升，但忽略了问题求解的动态过程。本文提出行为感知题目建模（BAIM），通过整合动态解题过程信息丰富题目表示。BAIM利用推理语言模型将每个题目的求解过程分解为四个阶段（理解、规划、执行、回顾），基于波利亚框架。具体地，从各阶段的嵌入轨迹中提取阶段级表示，捕捉表面特征之外的潜在信号。为反映学习者差异，BAIM在KT主干中引入上下文感知路由机制，动态强调不同学习者的不同解题阶段。在XES3G5M和NIPS34数据集上的实验表明，BAIM持续优于强基线模型，尤其在重复学习交互场景下表现更优。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) aims to predict learners' future performance from past interactions. While recent KT approaches have improved via learning item representations aligned with Knowledge Components, they overlook the procedural dynamics of problem solving. We propose Behavior-Aware Item Modeling (BAIM), a framework that enriches item representations by integrating dynamic procedural solution information. BAIM leverages a reasoning language model to decompose each item's solution into four problem-solving stages (i.e., understand, plan, carry out, and look back), pedagogically grounded in Polya's framework. Specifically, it derives stage-level representations from per-stage embedding trajectories, capturing latent signals beyond surface features. To reflect learner heterogeneity, BAIM adaptively routes these stage-wise representations, introducing a context-conditioned mechanism within a KT backbone, allowing different procedural stages to be emphasized for different learners. Experiments on XES3G5M and NIPS34 show that BAIM consistently outperforms strong pretraining-based baselines, achieving particularly large gains under repeated learner interactions.
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。