Graph Attention Deep Knowledge Tracing Model Integrated with IRT

Abstract

Knowledge tracing aims to trace students’ knowledge state(the degree of knowledge) based on their historical answer performance in real time and predict their future answer performance.The current research only explores the direct influence of the question or concept itself on the performance of students’ answering questions,while often ignores the indirect influence of the deep-level information in the questions and the concepts contained on the performance of students’ answering questions.In order to make better use of these deep-level information,a graph attention deep knowledge tracing model integrated with IRT(GAKT-IRT) is proposed,which integrates item response theory(IRT).The graph attention network is applied to the field of knowledge tracing and uses IRT to increase the interpretability of the model.First,obtain the deep-level feature representation of the problem through the graph attention network layer.Next,model students’ knowledge state based on their historical answer sequence that combines the in-depth information.Then,use IRT to predict students’ future answer performance.Results of comparative experiments on 6 open real online education datasets prove that the GAKT-IRT model can better complete the knowledge tracing task and has obvious advantages in predicting the future performance of students in answering questions.

Keywords

• knowledge tracing|graph attention network|item response theory|deep learning|interpretability