Global and local co-attention networks enhanced by learning state for knowledge tracing

Abstract

In intelligent tutoring systems, knowledge tracing (KT) stands as a pivotal technology for facilitating personalized learning among students. Effectively capturing the continually evolving knowledge mastery states of students poses a formidable challenge in KT prediction. Traditional KT methods typically model students’ global knowledge mastery states solely based on the chronological sequence of their historical interactions, neglecting the significance of their current learning state and the inherent interplay between global and local knowledge mastery states. To bridge these gaps, this paper introduces a novel Learning State Enhanced Co-attention Model (LSEKT) for knowledge tracing. In terms of methodology, we contend that a student’s recent answering behavior is intricately tied to implicit learning states. Consequently, we devise a learning state extraction network to capture the student’s current learning state. Furthermore, to construct a more robust and interdependent representation of both global and local knowledge mastery states, we integrate a co-attention network. This network enhances the attention paid to pertinent knowledge points across both global and local scales, thereby adeptly capturing the underlying connections between global and local interaction sequences. Concurrently, we incorporate contrastive learning as an auxiliary task within our model to bolster its predictive prowess. Ultimately, we evaluated our approach through extensive experiments on four widely used datasets. The experimental outcomes underscore the remarkable performance of our model across diverse evaluation metrics, emphasizing the effectiveness of our proposed LSEKT model.