Enhancing learning process modeling for session-aware knowledge tracing

Abstract

Session-aware knowledge tracing tries to predict learners’ performance, by splitting learners’ sequences into sessions and modeling their learning within and between sessions. However, there still is a lack of comprehensive understanding of the learning processes and session-form learning patterns. Moreover, the knowledge state shifts between sessions at the knowledge concept level remain unexplored. To this end, we conduct in-depth data analysis to understand learners’ learning processes and session-form learning patterns. Then, we perform an empirical study validating knowledge state shifts at the knowledge concept level in real-world educational datasets. Subsequently, a method of Enhancing Learning Process Modeling for Session-aware Knowledge Tracing, ELPKT, is proposed to capture the knowledge state shifts at the knowledge concept level and track knowledge state across sessions. Specifically, the ELPKT models learners’ learning process as intra-sessions and inter-sessions from the knowledge concept level. In intra-sessions, fine-grained behaviors are used to capture learners’ short-term knowledge states accurately. In inter-sessions, learners’ knowledge retentions and decays are modeled to capture the knowledge state shift between sessions. Extensive experiments on four real-world datasets demonstrate that ELPKT outperforms the existing methods in learners’ performance prediction. Additionally, ELPKT shows its ability to capture the knowledge state shifts between sessions and provide interpretability for the predicted results.