This study demonstrates improved dropout and engagement predictions in higher education, highlighting reinforcement learning and graph neural networks' impact.
Key Points
Engagement prediction improved by 22.1% using graph neural networks, indicating effective student support.
Dropout rate reduced by 39.6% using reinforcement learning techniques, impacting student outcomes positively.
Proximal Policy Optimization method was employed to define reinforced educational policies for better performance.
Findings suggest that adaptive education frameworks can redefine student support strategies in higher education.