Contrastive Learning with Bidirectional Transformers for Knowledge Tracing

Knowledge tracing aims to predict students' probability of correctly answering the next question based on their interaction history. Previous methods employ left-to-right unidirectional transformers to encode the historical behaviors into hidden representations, especially with contrastive learning methods. Using uni-directional models to model student behaviors can only learn the hidden representation from its previous items, which restricts the power of the representation capability. Inspired by the success of BERT in text understanding, we propose a novel Bidirectional Transformer encoder guided Contrastive Learning framework for deep Knowledge Tracing system, named as Bi-CL4KT to generate the accurate response predictions for the next question. We incorporate the Cloze task and carefully design data augmentation methods to generate high-quality positive and negative instances for contrastive learning. Extensive experiments conducted on three real-world education datasets show that the proposed method significantly outperforms the state-of-the-art methods.