Abstract: Predicting student performance is a fundamental task in Intelligent Tutoring
Systems (ITSs), by which we can learn about students' knowledge level and
provide personalized teaching strategies for them. Researchers have made plenty
of efforts on this task. They either leverage educational psychology methods to
predict students' scores according to the learned knowledge proficiency, or
make full use of Collaborative Filtering (CF) models to represent latent
factors of students and exercises. However, most of these methods either
neglect the exercise-specific characteristics (e.g., exercise materials), or
cannot fully explore the high-order interactions between students, exercises,
as well as knowledge concepts. To this end, we propose a Graph-based Exercise-
and Knowledge-Aware Learning Network for accurate student score prediction.
Specifically, we learn students' mastery of exercises and knowledge concepts
respectively to model the two-fold effects of exercises and knowledge concepts.
Then, to model the high-order interactions, we apply graph convolution
techniques in the prediction process. Extensive experiments on two real-world
datasets prove the effectiveness of our proposed Graph-EKLN.