PERM: Pre-training Question Embeddings via Relation Map for Improving Knowledge Tracing

Learning informative embedding for educational question (aka. representations) lies at the core of online learning systems. Recent solutions mainly focus on learning question embedding via the question-concept bipartite graph. However, the student-question-concept global relation is inadequately exploited. Moreover, finer-grained semantic information from student-question and student-concept interactions should also be further revealed. To this end, in this paper, we propose to Pre-train question Embeddings via Relation Map for knowledge tracing, namely PERM. Extensive experiments conducted on two real-world datasets show that PERM has higher expressive power which enables knowledge tracing methods to effectively predict students’ performance.