Exploiting multiple question factors for knowledge tracing

Knowledge Tracing (KT) aims to predict future students' performance via their responses to a sequence of questions, which serves as a fundamental task for intelligent education. Most of the existing efforts directly predict students' performance depending on their dynamically changing knowledge states. However, the indi-vidualization of questions is neglected and difficulty level differ from question to question, which would give some valuable clues to KT. Towards this end, in this paper, we propose a novel Multiple Question Factors for Knowledge Tracing (MQFKT) method, which fully exploits various question factors to generate better prediction. On one hand, calibrated student-concept connection space is established to obtain fine-grained response rep-resentations on questions according to the information of responses on questions. On the other hand, individ-ualized difficulty levels with particular concept for different questions are introduced for improving the prediction performance. Extensive experiments on three datasets have shown that the MQFKT approach achieves more precise prediction of student performance and better interpretation of the model.