Student’s performance prediction based on an improved multi-view hypergraph neural network

Abstract

ABSTRACT Predicting students’ academic performance is a crucial area of research in Educational Data Mining. Efficient performance prediction can significantly improve instructional effectiveness, facilitate personalised learning, and identify at-risk students. While researchers have analysed the complex relationships between students’ daily behaviours and their academic performance, most studies focus solely on individual student models and disregard potential relationships among students. To address these issues, we propose an improved multi-view hypergraph neural network (IMHNN). IMHNN employs hypergraphs to establish high-order relationships between student behaviours and introduces an attentional convolutional network to adaptively obtain weights for different behaviours. Additionally, a residual network is introduced to mitigate transition smoothing and improve the generalisation performance of the model. Using a genuine education dataset, we compare the performance of various approaches, demonstrating that our method outperforms other existing methods.