Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
Knowledge tracing (KT) predicts student performance by modeling evolving knowledge states. Existing methods treat interactions as a unified process, ignoring phase-specific learning. We propose Phase-Aware Knowledge Tracing (PAKT), which decomposes interactions into ability and proficiency phases. A multi-branch Transformer with type-aware readout captures phase-specific and holistic states. Causal analysis reveals confounding bias in phase-agnostic models. On six benchmarks, PAKT achieves up to 1.33% AUC improvement, averaging 0.82%.
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[Submitted on 14 Jul 2026]
Title:Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
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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%.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.13103 [cs.LG]
(or arXiv:2607.13103v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.13103
arXiv-issued DOI via DataCite
Submission history
From: Jinsong Chen [view email] [v1] Tue, 14 Jul 2026 09:11:59 UTC (6,401 KB)
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