DSCA-KT: Dual-State Context-Aware Model for Dynamic Knowledge Tracing

Knowledge Tracing (KT) aims to model students’ evolving knowledge states from historical learning interactions. Recently, dynamic KT methods have gained increasing attention for their ability to model continuously evolving interaction sequences. However, existing approaches still struggle to distinguish transient cognitive fluctuations from stable knowledge-acquisition patterns and often produce unreliable predictions in cold-start scenarios with limited interaction histories. To address these challenges, we propose DSCA-KT, a Dual-State Context-Aware framework for dynamic knowledge tracing. Specifically, we design context-aware recurrent aggregators that capture high-frequency cognitive variations, enabling the model to better characterize transient learning behaviors while preserving long-term knowledge trends. In addition, we introduce a context modulator that generates student- and question-specific contextual representations and incorporates prior structural knowledge to alleviate the cold-start issue. Extensive experiments on several widely used benchmark datasets demonstrate that DSCA-KT consistently outperforms state-of-the-art baselines under both transductive and inductive settings.