DS-HGCN: A Dual-Stream Hypergraph Convolutional Network for Predicting Student Engagement via Social Contagion

TL;DR

This paper introduces DS-HGCN, a dual-stream hypergraph convolutional network that models social contagion and multi-dimensional features to accurately predict student engagement, outperforming existing methods.

Contribution

The paper presents a novel dual-stream hypergraph convolutional network incorporating social contagion and attention mechanisms for student engagement prediction.

Findings

Achieves superior prediction accuracy on benchmark datasets.

Effectively models social contagion and emotional differences among students.

Outperforms state-of-the-art approaches significantly.

Abstract

Student engagement is a critical factor influencing academic success and learning outcomes. Accurately predicting student engagement is essential for optimizing teaching strategies and providing personalized interventions. However, most approaches focus on single-dimensional feature analysis and assessing engagement based on individual student factors. In this work, we propose a dual-stream multi-feature fusion model based on hypergraph convolutional networks (DS-HGCN), incorporating social contagion of student engagement. DS-HGCN enables accurate prediction of student engagement states by modeling multi-dimensional features and their propagation mechanisms between students. The framework constructs a hypergraph structure to encode engagement contagion among students and captures the emotional and behavioral differences and commonalities by multi-frequency signals. Furthermore, we introduce a hypergraph attention mechanism to dynamically weigh the influence of each student, accounting for individual differences in the propagation process. Extensive experiments on public benchmark datasets demonstrate that our proposed method achieves superior performance and significantly outperforms existing state-of-the-art approaches.