Abstract: Knowledge Tracing (KT) is a key task in educational data mining, aiming at modeling students changing knowledge states over time to infer students proficiency on concepts. However, most of existing knowledge tracing methods ignore the reliability and high-dimensional sparsity of the student-concept space based on the student-exercise-concept relationship, and do not combine the students response to the exercise to generate a reliable response representation. To address the above issues, a reliable response representation enhanced knowledge tracing method is proposed. Specifically, firstly, the student-exercise space is divided into fine-grained student-exercise spaces based on the student’s response records, and different student-concept spaces are obtained based on the exercise- concept space; secondly, the reliability of the student-concept space is obtained from both the relative reliability and absolute reliability of the student-concept space, and a reliable and low-dimensional student-concept space is obtained using dimensionality reduction methods; thirdly, the reliable response representation of the exercise is obtained by combining the students response to the exercise and the exercise representation method under two response conditions; finally, the students knowledge state at different timesteps is evaluated using a long short-term memory network and the obtained reliable response representation. Experimental results on four real datasets demonstrate the effectiveness and interpretability of the proposed method.

Key words: knowledge tracing, educational data mining, reliable response representation, long short-term memory network