Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing

• URL: http://arxiv.org/abs/2607.13103v1
• Date: Tue, 14 Jul 2026 09:11:59 GMT
• Title: Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
• Abstract summary: Knowledge tracing aims to predict students' future performance by modeling their evolving knowledge states from historical interactions.<n>We propose Phase-Aware Knowledge Tracing (PAKT), a KT framework that decomposes student interactions into ability and proficiency phases.<n>Our method consistently outperforms representative baselines, with a maximum AUC gain of 1.33% and an average gain of 0.82%.
• Score: 6.399286079325182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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