Towards more accurate and interpretable model: Fusing multiple knowledge relations into deep knowledge tracing

Abstract

With the rapid growth of online education, Knowledge tracing (KT) has become a well established problem, which evaluates the knowledge states of students and predicts their performance on new exercises. Recently, more and more works have noticed the importance of relations among knowledge points and proposed to introduce the knowledge relations into KT. However, how to precisely learn the representation of different types of knowledge relations and effectively fuse multiple relations into KT is still challenging. To address this issue, we propose a novel KT model, called Deep Knowledge Tracing with Multiple Relations (DKTMR), which can simultaneously fuse the directed relation and undirected relation into KT. More specifically, casting the knowledge relations as a graph, DKTMR designs to utilize two types of Generative Adversarial Networks (GANs) to learn the representation of knowledge point with different relations via graph representation learning. Then, the Gated Recurrent Unit (GRU) is used to update the students’ knowledge states. Furthermore, to consider the different contribution for each type of relation to the final prediction, an attention-based fusion method is proposed to learn the coefficients for different relations. Compared with several state-of-the-art baselines, the extensive experiments on four real-world datasets demonstrate the effectiveness and interpretability of DKTMR.