Multi-factor Memory Attentive Model for Knowledge Tracing

Liu Congjie (Liaoning University); Xiaoguang Li (Liaoning University)*


The traditional knowledge tracing with neural network usually embeds the required information and predicates the knowledge proficiency by embedded information. Only few information, however, is considered in traditional methods, such as the information of exercises in terms of concept. In this paper, we propose a multi-factor memory attentive model for knowledge tracing (MMAKT). Based on Neural Cognitive Diagnosis (NeuralCD) framework, MMAKT introduces the factors of the knowledge concept relevancy, the difficulty of each concept, the discrimination among exercises and the student’s proficiency to construct interaction vectors. Moreover, in order to achieve more accurate prediction precision, MMAKT introduces attention mechanism to enhance the expression of historical relationship between interactions. We conduct experiments on several real-world datasets and the experimental results show that our model outperforms the state-of-the-art approaches.