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| Group Detection in Interest-Based Learning Communities Enhanced by Hypergraph from a Social Engagement Perspective |
| Li He, Liu Jiayu, Shen Wang, Shi Qianru, Xie Mengfan |
| School of Business and Management, Jilin University, Changchun130012 |
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Abstract Online learning group detection in the context of the new technological revolution empowering educational innovation, is a key approach for optimizing the stratified allocation of educational resources based on the personalized characteristics of learners. Existing detection methods for interest-based learning community online learning groups primarily rely on direct behavioral data and interaction metrics of learners, focusing less on the potential level of social engagement and community structure. To cultivate a culture of autonomous learning enhanced by learner profiles in a smart digital environment, we propose a group-detection method for interest-based learning communities enhanced by a hypergraph from a social engagement perspective. Initially, a feature set representing the learner’s level of social engagement is constructed based on factors influencing users’ social engagement. Subsequently, a hypergraph convolutional network (HyperGCN)-enhanced graph clustering algorithm is proposed to overcome the issue of ineffective capture of multivariate interactions and higher-order structures of learner groups previously encountered with bipartite graph detection. Data were collected from a real-life interest-based learning community to validate the effectiveness of the proposed method. Compared with the baseline, the proposed method achieved improvements of 16.16, 9.77, 16.01, and 22.14 percentage points in accuracy (Acc), F1, normalized mutual information (NMI), and adjusted Rand index (ARI), respectively. These results not only prove the effectiveness of HyperGCN in capturing the structure of learner groups for online learning group detection tasks but also provide methodological and theoretical support for formulating and adjusting personalized education configuration strategies from the perspective of social engagement.
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Received: 18 September 2024
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