A Generalizable Approach for Learner Performance Prediction Using GraphSAGE Neural Networks

Abstract

Understanding and predicting learner performance is essential for designing effective educational strategies and supporting students who need additional help. Although graph neural networks (GNNs) show strong potential for modeling learner–content interactions, many knowledge-based GNN approaches struggle to generalize across different educational contexts due to their reliance on predefined knowledge structures. This study introduces an independent GNN framework based on GraphSAGE, evaluated on five real-world datasets from diverse learning environments. The model captures learning interactions without depending on knowledge-tracing assumptions and achieves strong performance (AUC = 0.87). Using inductive learning, GraphSAGE generates embeddings for new learners and items without retraining, improving adaptability and scalability. Consistent results across datasets show minimal need for domain-specific tuning, highlighting the model's potential for deployment in various learning systems to support adaptive pathways, early interventions, and equitable resource allocation.