Multi-view hypergraph neural networks for student academic performance prediction

Academic performance prediction is a fundamental and hot issue in educational data mining (EDM). Recently, researchers have proposed a series of effective machine learning (ML) based classification strategies to predict students' academic performance. However, prior arts are typically concerned about individual models but neglect the association among students, which might considerably have an effect on the integrity of the academic performance-related representations. Meanwhile, students' multi-viewing behavior contains complex relations among students. Therefore, we propose a Multi-View Hypergraph Neural Network (MVHGNN) for predicting students' academic performance. MVHGNN uses hypergraphs to construct high-order relations among students. The semantic information implied by multiple behaviors is consolidated through meta-paths. Further, a Cascade Attention Transformer (CAT) module is introduced to mine the weight of different behaviors by the self-attention mechanism. Our method is evaluated on real campus student behavioral datasets. The experimental results demonstrate that our method outperforms the state-of-the-art ones.