Long- and Short-term Attention Network for Knowledge Tracing

Abstract

Knowledge Tracing (KT) is an important part of intelligent online education and is the key to personalized guidance for students' learning process. It aims at dynamically estimate students' knowledge status based on history answer records and predict whether they will answer the next question correctly. Predicting students' knowledge is a difficult task because student learning is a dynamic process and students' knowledge status is constantly changing. However, existing approaches often ignore the fact of students' stage development of learning ability and do not take into account the impact of short-term knowledge states on the next moment. Existing models do not emphasize the importance of students' long-term knowledge states and short-term knowledge states. In this paper, we propose a new Long- and Short-term Attention network Knowledge Tracing model (LSAKT). Specifically, we divided the sequences into subsequences based on time stamps, with the first attention layer learning the student's long-term knowledge state based on the interactional performance with the problem, while the other attention layer learns the student's short-term knowledge state based on the last sequence. Finally, we integrate the long-term knowledge state and the short-term knowledge state to form the student's final knowledge state. We evaluated the proposed model on four publicly available datasets, and the experimental results showed that the LSAKT approach achieved a fantastic result.