Dynamic domain learning ability enhanced knowledge tracing with stability

Knowledge Tracing (KT) aims to dynamically model a student's knowledge state to predict future learning performance. However, most existing approaches have two main limitations. On the one hand, they fail to capture the gradual evolution of knowledge over time, overlooking the stable nature of the learning process. As a result, their predictions often show significant temporal fluctuations. On the other hand, they ignore individual differences in students' abilities across different tasks within the same domain, typically modeling ability as a single, static level, which limits the accuracy of personalized predictions. To address these issues, this research proposes Dynamic Domain Learning Ability Enhanced Knowledge Tracing with Stability (DLAKT). Firstly, unlike previous knowledge tracing methods that primarily rely on knowledge mastery, DLAKT is the first to explicitly incorporate domain learning ability into the KT framework. This design addresses the limitation that knowledge states alone cannot fully capture individual differences. By establishing a clear mapping between skills and multiple ability dimensions, DLAKT constructs an interpretable representation of domain learning ability. The model dynamically adjusts the ability improvement rate according to the student's knowledge state, response time, and item difficulty, enabling personalized modeling of evolving abilities. Secondly, DLAKT explicitly models knowledge forgetting and accumulation based on memory networks by incorporating multiple learning behavior features, thereby more accurately simulating the learning dynamics. It further introduces a Transformer-based smoothing module to reduce fluctuations in the knowledge state and enhance model stability. Finally, through the joint modeling of knowledge evolution and the dynamic update of domain learning ability, DLAKT achieves more accurate and stable predictions of student performance. Experiments on three real-world educational datasets show that DLAKT consistently outperforms existing mainstream models in prediction accuracy.