Personalized learning recommendations based on graph neural networks

This paper presents a novel graph neural network (GNN)-based model for personalized learning with advanced graph neural networks, incorporating both graph convolutional networks (GCN) and graph attention Networks (GAT). Our model leverages GCN, which consists of multiple layers embedding deep learning models, to aggregate data from neighboring nodes and capture the intricate relationships between students and courses. The GAT layers refine these embeddings by dynamically assigning importance weights to connections, prioritizing relationships critical for personalized course recommendations. This dual-layered approach enables the model to account for both global structural patterns and locally significant interactions within the student-course graph. We evaluated the performance of our model using the open university learning analytics dataset (OULAD), a rich dataset encompassing student demographic information, interaction data, and course performance metrics. Experimental results achieved 78.9% F1-score, 78.3% precision, and 76.2% recall in personalized recommendations, outperforming single-layer GCN implementations by approximately 15 percentage points. These results demonstrate the model's ability to handle complex, dynamic relationships in educational data, ensuring more relevant and effective recommendations. By addressing key challenges in recommendation systems, such as the need for dynamic weighting of relationships and the handling of sparsity in educational data, our study underscores the transformative potential of GNNs in advancing personalized education. This work sets the stage for further exploration of GNN applications in e-learning, paving the way for adaptive and intelligent course recommendation systems that align with individual learning needs and preferences.