DyGAS: Dynamic Graph-Augmented Sequence Modeling for Knowledge Tracing

Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progress. Knowledge tracing (KT) addresses this gap by predicting students’ future performance on exercises related to specific concepts, thereby enabling personalized learning and intelligent tutoring. Existing deep learning-based KT methods achieve promising results, but they often overemphasize either the sequential evolution of knowledge or the static structural relationships, which does not reflect the dynamic evolution of student learning. Moreover, they fail to model students’ knowledge state accurately under sparse interactions. To overcome these limitations, we propose DyGAS, a dynamic graph-augmented sequence modeling framework for knowledge tracing. The sequential module captures the dynamics pattern of knowledge acquisition and forgetting, while the structural module employs graph convolutional networks (GCN) to model inter-concept dependencies and knowledge transfer. Additionally, we propose that static knowledge modeling provides semantic priors to stabilize the representation of sparse concepts. Empirical results on three benchmark datasets demonstrate that DyGAS achieves superior performance compared to state-of-the-art methods, offering accurate and robust knowledge tracing across diverse learning scenarios.