Integrating Behavioral Analytics and AI for Enhanced Academic Performance Prediction Using Graph Convolution Networks and Deep Learning Techniques

The work explores the integration of behavioral analytics and artificial intelligence (AI) techniques, specifically Graph Convolutional Networks (GCNs) and deep learning, to predict student academic performance. By analyzing study habits, assignment submissions, gaming behavior, and exam scores, the framework bridges traditional evaluation methods and behavioral insights. Results demonstrate that a GCN model outperforms other machine learning approaches, achieving a Mean Squared Error (MSE) of 117.97. The findings highlight the potential of machine learning to enhance educational strategies, identify at-risk students, and provide actionable insights for data-driven educational improvements.