Interpretable Knowledge Tracing with Difficulty-Aware Attention and Selective State Space Model

Knowledge Tracing (KT) aims to model students' knowledge states based on their historical learning sequence, playing a critical role in online education platforms.As the performance of sequence-based KT methods continues to improve, their increasing model complexity and lack of transparency have become significant limitations.In contrast, educational theory-driven KT methods incorporate educationally meaningful features (such as question difficulty or time spent on questions) to enhance interpretability and performance.However, these models typically adopt simpler structures to reduce complexity and avoid overfitting, which limits their ability to effectively capture the sequential characteristics of learning compared to sequence-based methods.To address these limitations, this paper aims to integrate the strengths of both types of methods by proposing an Interpretable KT approach with Difficulty-Aware Attention and Selective State Space Model (ASIKT).Specifically, leveraging educational context, we design a difficulty-enhanced attention mechanism to model students' knowledge retrieval process