A deep knowledge tracing model based on multi-head self-attention mechanism
Ya Zhou, Fengzhen Wu, Guimin Huang, Jianxing Lin, Nanxiao Deng, Qingkai Guo · 2023
How to provide individualized instruction and personalized teaching for students and effectively track their knowledge status in a targeted manner is a key concern in the field of education. The target of Knowledge Tracing (KT) is to use the learner's behavior in historical learning activities to model the students' educational process and forecast the future performance of students. Recently, self-attention has been used several times in the KT field. Traditional models have the problem of losing information under long sequence data. Inspired by Transformer, in this paper, we present a deep knowledge tracing model (MA-DKT) based on the multi-head self-attention mechanism, which employs the LSTM model combined with the multi-head self-attention mechanism to implement a deep knowledge tracing model that is able to catch the data characteristics of the students at different temporal scales. Our model works well on publicly available datasets and achieves some improvement in AUC.