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.

翻译：知识追踪（KT）旨在通过历史交互预测学习者未来的表现。尽管近期KT方法通过学习与知识组件对齐的项目表示取得了改进，但这些方法忽略了问题解决的过程动态性。我们提出行为感知项目建模（BAIM）框架，该框架通过整合动态解题过程信息来丰富项目表示。BAIM利用推理语言模型将每个项目的解题过程分解为四个问题解决阶段（即理解、计划、执行、回顾），这一分解在理论上根植于波利亚框架。具体而言，它从各阶段嵌入轨迹中提取阶段级表示，捕捉了超越表面特征的潜在信号。为反映学习者异质性，BAIM自适应路由这些阶段级表示，在KT主干网络中引入情境条件机制，使不同学习者的解题阶段能够获得差异化关注。在XES3G5M和NIPS34数据集上的实验表明，BAIM持续优于基于强预训练的基线方法，尤其在重复学习者交互场景下取得显著增益。