区分学习阶段，更精准预测学生知识掌握情况
Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
- 将学习过程拆分为能力构建与熟练度提升两个阶段
- 在六个数据集上平均提升0.82%准确率，最高达1.33%
- 适合教育智能系统开发者和学习行为研究者
知识追踪（KT）旨在通过建模学生历史交互行为来预测其未来表现。现有方法通常将原始交互序列视为统一行为过程，忽略了学习行为的阶段性特征。我们初步观察发现，经过充分练习后，学生更可能正确回答之前出错的知识点，表明学习存在从能力构建到熟练度提升的过渡。受此启发，我们提出相位感知知识追踪（PAKT），基于定制分解机制将学生交互划分为能力与熟练度两个阶段。为有效利用分解后的序列，设计了类型感知读出模块的多分支Transformer，联合捕捉阶段特异性和整体知识状态。进一步通过因果分析揭示了传统无相位感知模型中复杂学习行为纠缠带来的混淆偏差。在六个公开基准上的实验表明，本方法持续优于代表性基线，最大AUC提升1.33%，平均提升0.82%。
原文摘要 · Abstract (English)
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%.
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