SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing

Abstract

Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods.