Dynamic heterogeneous graph contrastive networks for knowledge tracing

Knowledge tracing (KT) is a crucial task in online education that traces students’ evolving cognition changes over time. However, it is a challenging task due to the heterogeneity of knowledge and incomplete cognition evolution sequences. This paper proposes KT-Deeper, a long-term Knowledge Tracing framework based on Dynamic reinforced heterogeneous graph contrastive networks, to predict students’ cognitive states on specific skills. Particularly, KT-Deeper initially employs temporal heterogeneous graphs to model the interconnections between different types of knowledge entities (e.g., students, exercises, and skills). Subsequently, KT-Deeper formalizes knowledge tracing as a dynamic link prediction task on the temporal heterogeneous graph sequence and proposes a reinforced graph generation approach to refine the incomplete graph sequence for supporting long-term knowledge tracing. KT-Deeper further presents a self-supervised heterogeneous graph embedding method to extract the structural features of knowledge evolution. Finally, KT-Deeper leverages recurrent neural networks to learn the temporal features of students’ cognitive evolution and predict whether a student will master a specific skill. Experimental results confirm that KT-Deeper exhibits superior performance compared to existing cutting-edge techniques, showcasing its promising accuracy and robustness in incomplete long-term knowledge tracing tasks.