Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction

TL;DR

This paper introduces a graph-based learning network that models exercise-specific and knowledge-aware interactions to improve student performance prediction accuracy in intelligent tutoring systems.

Contribution

It proposes a novel graph convolution approach that captures high-order interactions among students, exercises, and knowledge concepts, addressing limitations of prior methods.

Findings

Outperforms existing models on real-world datasets

Effectively models high-order interactions

Improves prediction accuracy significantly

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.