Weighted Heterogeneous Graph-based Three-view Contrastive Learning for Knowledge Tracing in Personalized e-Learning Systems

Personalized e-learning systems are applications of consumer electronics in the field of education that provide individualized and adaptive services for users. Knowledge tracing (KT), as a key technology, aims to model learners’ knowledge states through the interactions between learners and learning resources, and predicts their future performance. However, the problem of interaction sparsity in educational resources leads to the fact that simple representations of questions usually fail to accurately capture students’ knowledge states. In this paper, inspired by the data-driven paradigm, we propose a novel knowledge tracing method named weighted heterogeneous graph-based Three-view Contrastive Learning framework for Knowledge Tracing (TCL4KT). Technically, three different view encoders in TCL4KT complement each other to obtain question embeddings with rich information. Specifically, TCL4KT considers the semantic information of higher-order, heterogeneous and the downstream task on a weighted heterogeneous graph of KT to learn high-quality representations. Besides, a meta-path-based positive sample selection strategy and joint contrastive loss are employed to gain better prediction performance. Experimental results on four datasets demonstrate the superiority of TCL4KT over baseline models, and further analysis verifies the effectiveness of our three-view contrastive learning framework.