Meta-path structured graph pre-training for improving knowledge tracing in intelligent tutoring

Knowledge tracing (KT) aims to predict students' future performance by tracking their learning behaviors in intelligent tutoring systems (ITS). In KT, three main types of entities are involved: students, exercises, and knowledge concepts. Graph structures provide an effective framework for establishing relationships between different entities. However, existing KT methods have neglected complex connections and implicit relational features, thus facing challenges in capturing high -order information in educational data. To this end, this paper proposes MPSG, a M eta- P ath S tructured G raph method designed to harness high -order information between entities to pre-train informative exercise embeddings for improving KT. Technically, structured by different meta-paths, four relation graphs are derived to establish implicit cross-entity relationships. On each graph, the surrounding neighbors of each node are then obtained through an intimacy-based sampling strategy. Subsequently, during the representation learning stage, node features under different meta-path views are generated and then aggregated to obtain the final exercise embeddings. The learned embeddings are optimized through three auxiliary tasks, anchored within a self-supervised learning paradigm. Extensive experiments across four public datasets demonstrate that our method can significantly enhance the predictive performance of downstream KT models.