Cross-Course Knowledge Tracing for Student Performance Prediction in MOOCs

Abstract

Knowledge tracing (KT), which uses machine learning models to predict students’ future performance, has received a lot of attention in intelligence education. However, in Massive Open Online Courses (MOOCs), most of the existing KT methods can only track students’ performance in one course. In addition, when there is scant learning record data on new courses, training a new KT model becomes challenging. Furthermore, existing KT methods tend to excel in specific courses, but their generalization ability is inferior when faced with similar or distinct courses. To address these challenges, this paper proposes a MOOC-oriented cross-course knowledge tracing model (MCKT). In MCKT, we first construct two attribute relationship graphs to obtain the student and KC representations from the source course and the target course, respectively. Then, the element-wise attention mechanism is used to fuse the student representations from both courses. Next, MLPs are used to reconstruct the interaction between students and KCs in each course to enhance students’ cross-course representation. Finally, recurrent neural networks (RNNs) are used to predict the students’ performance in each course. Experiments show that our proposed approach outperforms existing KT methods in predicting students’ performance across diverse MOOCs, proving its effectiveness and superiority.