Attentive Simple Recurrent Unit Knowledge Tracing Based on Learning Ability

Abstract

Knowledge tracing can analyze the current knowledge level of students through the data of students’ previous learning activities. However, the existing models usually consider the features of exercises, ignoring the individual differences of students. It is difficult to accurately predict students’ mastery. In this paper, we propose an attentive simple recurrent unit knowledge tracing (SRU-MAKT) based on learning ability. The experimental results show that our model is superior to the existing models, and the AUC increases by 1.6%. We also conduct visualization experiments, which show that SRU-MAKT has interpretability.