Data-driven School Management: An Analysis of Pathways to Enhancing Student Academic Performance and Mental Health

The rapid growth of data analytics in recent years has opened up new opportunities for organizations of all kinds to make the most of large amounts of data to drive decision making and optimize operations. This paper focuses on the application of data analysis in improving student management in educational institutions, and analyzes a variety of lifestyle and behavioral variables such as students' study time, extracurricular activities, sleep duration, and social behavior. By analyzing the impact of these factors on academic performance and stress levels, this study highlights the importance of data-driven approaches in shaping education policy. The study uses statistical tools and regression analysis to identify key patterns and correlations and provide actionable recommendations for schools to enhance student academic performance and effectively manage mental health. This study aims to show how data analytics can be applied in education to help schools create more personalized, efficient and supportive learning environments. The findings highlight the importance of balancing academic demands with student well-being and suggest interventions that schools can take to optimize academic outcomes and overall student health.