Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing

This paper proposes CPF, a Concept-driven Personalized Forgetting knowledge tracing model that integrates students' personalized cognitive abilities and hierarchical knowledge relationships to predict future learning performance by modeling individualized learning gains and forgetting rates.

Knowledge Tracing (KT) aims to trace changes in students' knowledge states throughout their entire learning process by analyzing their historical learning data and predicting their future learning performance. Existing forgetting curve theory based knowledge tracing models only consider the general forgetting caused by time intervals, ignoring the individualization of students and the causal relationship of the forgetting process. To address these problems, we propose a Concept-driven Personaliz

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