Application of Reinforcement Learning in Analysis and Optimization of Social Behavior of Students in Colleges and Universities

Abstract

This study investigates the application of reinforcement learning (RL) in analyzing and optimizing social behaviour among students in colleges and universities. Through a multidisciplinary approach that combines machine learning techniques, social network analysis, and personalized interventions, the study aims to enhance student engagement, connectivity, and academic success. Data collected from various sources, including academic records, social media interactions, and campus activities, are utilized to train RL algorithms and simulate different social scenarios. The results reveal a significant increase in student engagement rates, improvement in social connectivity, and enhancement of academic performance following RL-driven interventions. Qualitative feedback from students further corroborates the positive experiences associated with RL-guided initiatives, emphasizing themes such as increased sense of belonging, improved collaboration, and enhanced communication. While the study highlights the transformative potential of RL in educational settings, it also underscores the importance of addressing ethical concerns and ensuring equitable access to benefits across diverse student populations. Overall, the findings contribute to advancing our understanding of the role of AI-driven approaches in fostering supportive and inclusive learning environments.