Federated Graph Representation Learning for Online Student Performance Analysis

The rapid growth of online learning platforms has intensified the need for privacy-aware methods that can analyze learner behavior without centralizing sensitive activity logs. This study presents a Federated Learning-Based Graph Representation Learning (FL-GRL) framework for online student performance analysis in distributed learning environments. Each learner is represented through a local Student Learning Knowledge Graph (SLKG) that captures typed interactions with courses, lessons, webinars, challenges, and forum activities. Graph Neural Networks (GNNs) are used to derive relation-aware embeddings from these local graphs, while federated learning supports collaborative model optimization without sharing raw data. A federated clustering stage is then used to identify soft learner groups with partially overlapping behavioral patterns that may support exploratory personalization and confidence-aware educational follow-up. The current experiments focus on the feasibility of privacy-aware graph-based analysis rather than on a complete supervised prediction benchmark. Results across the evaluated graph-based variants indicate that the proposed framework is operationally viable, preserves relational structure better than flat-feature formulations, and provides an interpretable basis for learner-group discovery in privacy-sensitive online education settings.