Knowledge Tracing

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Knowledge tracing estimates what a learner knows and predicts future responses from sequences of questions, answers, and other learning behaviors. Methods range from Bayesian Knowledge Tracing and item-response theory to recurrent networks, Transformers, knowledge graphs, and language-model approaches that use text or problem-solving steps. These estimates can personalize practice, identify skill gaps, and guide decisions about when a learner is ready to advance. A central challenge is balancing predictive accuracy with reliable, interpretable, and fair mastery estimates, especially when data are sparse or benchmark results hide differences across learners and items.

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Tracing Mathematical Proficiency Through Problem-Solving Processes
Nov 29, 2025Jungyang Park, Suho Kang, Jaewoo Park +4Intelligent Tutoring Systems Knowledge Tracing
1Yonsei University · 2Mathpresso · 3Seoul National University

Nov 29, 2025J/K move · Enter open · S save

Tracing Mathematical Proficiency Through Problem-Solving Processes

Jungyang Park, Suho Kang, Jaewoo Park, Jaehong Kim+3

1Yonsei University · 2Mathpresso · 3Seoul National University

Intelligent Tutoring Systems Knowledge Tracing

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Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring Systems. However, traditional KT methods face fundamental limitations in explainability, as they rely solely on the response correctness, neglecting the rich information embedded in students' problem-solving processes. To address this gap, we propose Knowledge Tracing Leveraging Problem-Solving Process (KT-PSP), which incorporates students' problem-solving processes to capture the multidimensional aspects of mathematical proficiency. We also introduce KT-PSP-25, a new dataset specifically designed for KT-PSP. Building on this, we present StatusKT, a KT framework that employs a teacher-student-teacher three-stage LLM pipeline to extract students' Mathematical Proficiency (MP) as intermediate representation. In this pipeline, the teacher LLM first extracts problem-specific proficiency indicators, then a student LLM generates responses based on the student's solution process, and a teacher LLM evaluates these responses to determine mastery of each indicator. The experimental results on KT-PSP-25 demonstrate that StatusKT improves the prediction performance of existing KT methods. Moreover, StatusKT provides interpretable explanations for its predictions by explicitly modeling students' mathematical proficiency. Code is available here.