Advanced Student Success Predictions in Higher Education with Graph Attention Networks for Personalized Learning

Conventional approaches such as Logistic Regression and Decision Trees have difficulties in accurately forecasting student progress in higher education due to their limited capacity to capture intricate data linkages. This study introduces a new method that utilizes a Graph Attention Network (GAT) to represent complex patterns and relationships in student data. The procedure involves data cleansing, standardization, natural language processing, feature extraction, and graph generation. The GAT model demonstrated exceptional performance, surpassing standard models, with an accuracy of 0.97, precision of 0.96, recall of 0.98, F1-Score of 0.979, and AUC-ROC of 0.99. The results illustrate the GAT model’s resilience and efficacy, providing a powerful instrument for educational analytics to increase personalized learning interventions and better student outcomes.