Enhancing Online Learning Community Discovery Through Graph Neural Networks and Data Optimization

Abstract

As digital platforms continue to reshape the way knowledge is acquired and shared, there is a critical need for effective community discovery algorithms that can operate in sparse data environments. Traditional methods often fail to capture the complex dynamics of online educational platforms, where explicit relationships among learners are typically underrepresented. Therefore, to address the challenges of sparse inter-learner connections within online learning communities, this paper proposes a novel framework that introduces the “Cyber Learning Community” model which reconceptualizes learner relationships by emphasizing collaborative and supportive interactions. Specifically, it combines a modified graph neural network (GNN) architecture and a bespoke community discovery algorithm to unearth and reinforce the latent networks within large datasets. The proposed methodology involves two primary tasks: predicting learners’ interest labels from their online behavior, and identifying robust community structures through the innovative use of the Discrete Beetle Antennae Search algorithm (DBAS). The effectiveness of our approach is demonstrated through extensive experiments on real-world datasets, showing marked improvements in community coherence and learning engagement. The results indicate that our methods not only enhance the detection and formation of educational communities but also suggest scalable applications for broader social network analysis.