Abstract: Knowledge tracing(KT) aims to predict a learner’s future performance based on their historical responses and assess changes in their knowledge state.Exploring these changes facilitates personalized services,such as course and exercise recommendations.However,most existing knowledge tracing models do not consider comprehensive features,leading to incomplete assessments of a learner’s knowledge state changes.To address this issue,a new knowledge tracing model－knowledge tracing model based on exercise-knowledge point heterogeneous graph and multi-feature fusion(EKMFKT)is propose.Specifically,we study two behavioral features(attempt and hint) and two temporal features(response time and interval time) and their impact on the knowledge state.To simulate knowledge acquisition and forgetting,we design learning and forgetting gates,which comprehensively update the knowledge state.For model inputs,a graph embedding method based on the question-knowledge point heteroge-neous graph is designed to pre-train question representations,preserving the associations between questions and knowledge points.Experimental results on two public datasets demonstrate that EKMFKT outperforms existing models in predictive performance.By incorporating multiple features and ensuring the connection between question and knowledge point,EKMFKT provides a more reasonable representation of knowledge state changes,enhancing the interpretability of the model.

Key words: Knowledge tracing, Heterogeneous graph, Learning behavior, Time feature, Forgetting behavior