Abstract: Knowledge tracing (KT) is a very important problem in the field of educational data mining. It aims to use the observable historical interaction data of students and the knowledge concepts (KCs) in the exercises to infer the students’ knowledge states (KS). Although existing efforts have yielded immense success on this task, most of them ignore the importance of using knowledge points to represent exercises, and the research on using contexts such as learning factors to represent knowledge points is not enough. Aiming at the above issue, a Knowledge Tracking method based on Contextualized Representations (KTCR) is proposed. Specifically, firstly, considering the complex contexts of students' learning process, a contextualized representation method based on response logs for KCs is devised to generate contextualized Q-matrix. Moreover, contextualized KCs and response logs are leveraged to re-represent vectors of exercises. Finally, Long Short-Term Memory network (LSTM) is adopted to estimate KS vectors of all students with the help of historical interaction data. Experiments on four real-world datasets demonstrate the rationality of the proposed method for the embedded representation of exercises, and can effectively estimate the knowledge state of students.

Key words: knowledge tracing, educational data mining, contextualized representation, Q-matrix, long short-term memory network