发表机构
School of the Information Management, Wuhan University; School of Computer Science and Artificial Intelligence, Hubei University of Technology; Faculty of Artificial Intelligence in Education, Central China Normal University（武汉大学信息管理学院; 湖北工业大学计算机科学与人工智能学院; 华中师范大学人工智能教育学部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在通过能力和熟练程度建模进行知识追踪，提出PAKT框架，基于定制机制分解学生交互，设计多分支Transformer捕获知识状态，通过因果分析揭示偏差，实验证明该方法优于基线。
AI 中文摘要
知识追踪旨在通过对学生历史交互中的知识状态演变进行建模来预测其未来表现。现有方法通常将原始交互序列视为统一行为过程，忽视学习行为的阶段特性。初步观察表明学生经充分练习后更易正确回答曾答错的知识概念，这意味着从能力构建到熟练程度导向学习的转变。基于此，我们提出阶段感知知识追踪（PAKT）框架。它基于定制分解机制将学生交互分解为能力和熟练程度阶段。为有效利用分解序列，设计了带类型感知读出模块的多分支Transformer以联合捕获特定阶段和整体知识状态。还进行因果分析揭示了无阶段感知知识追踪模型中复杂学习行为纠缠导致的混杂偏差。在六个公共基准上的大量实验表明，我们的方法始终优于代表性基线，最大AUC增益为1.33%，平均增益为0.82%。
英文摘要
Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions. Existing KT methods usually treat the raw interaction sequence as a unified behavioral process, overlooking the phase-specific nature of learning behaviors. Our preliminary observations show that students are more likely to correctly answer previously failed knowledge concepts after sufficient practice, suggesting a transition from ability-building to proficiency-oriented learning. Motivated by this, we propose Phase-Aware Knowledge Tracing (PAKT), a KT framework that decomposes student interactions into ability and proficiency phases based on the tailored decomposition mechanism. To effectively exploit the decomposed sequences, we design a multi-branch Transformer with a type-aware readout module to jointly capture phase-specific and holistic knowledge states. We further provide a causal analysis to reveal the confounding bias caused by entangling complex learning behaviors in phase-agnostic KT models. Extensive experiments on six public benchmarks demonstrate that our method consistently outperforms representative baselines, with a maximum AUC gain of 1.33% and an average gain of 0.82%.
