Improving knowledge tracing via a heterogeneous information network enhanced by student interactions

Abstract

Knowledge tracing (KT) aims to evaluate the knowledge state of students based on their coursework interactions in an intelligent tutoring system (ITS). It is the most fundamental and challenging task in the system. Existing KT models have achieved promising results. However, considerable interactions between the student entity and the other two types of entities (i.e., questions and skills) in ITS have not been well utilized, which restricts the performance of these models. To this end, we propose HIN-KT, a novel heterogeneous information network (HIN)-based pre-processing model, to further enhance the performance of existing KT models. An HIN is first built to model student–question–skill interactions. Subsequently, HIN-KT employs the interaction information modeled by HIN to pre-train embeddings for questions and then adopts pre-trained question embeddings to enhance the performance of recent deep KT models. Experimental results over three public KT datasets demonstrate that large gains in knowledge tracing can be achieved when the proposed HIN-KT model is used to pre-train question embeddings for state-of-the-art deep KT models. In particular, the two state-of-the-art deep KT models, deep knowledge tracing (DKT) and convolutional knowledge tracing (CKT) successfully improved their performance by an average of 7.88% with the help of the HIN-KT model, which is significant progress made in the knowledge tracing domain.