MGKT: A Multi-Relation Enhanced Graph-Based Model for Knowledge Tracing

Knowledge tracing defines the task of predicting future performance of students based on their historical interactions. Recently, some graph-based methods try to capture correspondence between questions and concepts by constructing the question-concept bipartite graph to tackle the knowledge tracing problem. However, they fail to explicitly integrate such intrinsic relations into the final answer predictor due to the sparse data. In this paper, we propose a novel Multi-relation Enhanced Graph-based Model for Knowledge Tracing (MGKT) to tackle the above problem. More specifically, MGKT constructs graph structure to explore multiple relations such as the high-order association among questions and the similarity of question’s attributes. In addition, two self-supervised training strategies, namely hypergraph contrast learning and hypergraph reconstruction, are proposed to incorporate these special correlations into question representations. Extensive experiments demonstrate that MGKT outperforms state-of-the-art knowledge tracing methods on three benchmark datasets.