Qi Mo

Knowledge Tracing (KT), a pivotal component of intelligent tutoring systems, models the evolution of student knowledge states to predict future performance. While KT fundamentally relies on the premise that performance on similar questions is highly correlated, existing approaches often depend on generalized question representations, neglecting the rich, multi-faceted nature of question attributes. To address this limitation, we propose the Hierarchical Question Attribute-Fused KT (HQAF-KT) model, a novel architecture that deconstructs question similarity through three hierarchical dimensions: inherent, dynamic, and statistical. HQAF-KT first enriches foundational representations by integrating inherent question attributes. It then deploys a Dynamic Computing module that leverages student-specific dynamic attributes to personalize similarity assessments based on individual cognitive contexts. Furthermore, a Statistic Correction module refines generalized statistical attributes to account for unique student abilities. This hierarchical fusion enables a nuanced, individualized modeling of question relationships. Extensive experiments on three large-scale, real-world datasets demonstrate that HQAF-KT significantly outperforms state-of-the-art baselines by effectively capturing multi-level question similarity.