Optimization of the degree of forgetting past data in Attentive Knowledge Tracing

Abstract

Knowledge Tracing (KT), using educational data to predict learners' knowledge states during the learning process, has attracted much attention. The most advanced KT method is Attentive Knowledge Tracing(AKT), which has been reported to show high prediction accuracy by incorporating a forgetting function of the past data to attention mechanisms. However, since AKT does not completely forget the past data, It causes non-negligible noises for estimating the past items weights. To slove the problem, we propose a new method to optimize the degree of forgetting the past data in AKT. In evaluation experiments, we compared the prediction accuracy of the proposed method with that of existing methods.