Learning consistent representations with temporal and causal enhancement for knowledge tracing

Abstract

Knowledge tracing is a crucial component of intelligent educational systems and deep learning technologies have significantly propelled its advancement. However, most existing models suffer from the incomplete data modeling problem of knowledge tracing, leading to inconsistent representations of students’ actual knowledge states. This paper proposes a new approach called Temporal- and Causal-enhanced Knowledge Tracing to improve the consistency of students’ knowledge state representations in intelligent educational systems. In particular, our method introduces a causal self-attention mechanism based on front-door adjustment theory, which improves interaction representation and reduces prediction errors caused by dataset bias. To effectively integrate the features of interval and response times into our model, we further use forget and input gates to simulate knowledge forgetting and acquisition, respectively. This maintains consistent learning behavior representations and improves model predictions. The results of our extensive experiments on the three datasets demonstrate that our method outperforms previous knowledge-tracing methods in predicting student scores.