Comprehensive analysis of stress factors affecting students: a machine learning approach

The increasing levels of stress among students worldwide pose a significant challenge to educational institutions. This study aims to systematically identify and analyse the factors contributing to student stress using advanced machine learning techniques.

To explore the primary stressors affecting students and to evaluate the interrelations among psychological, physiological, environmental, academic, and social factors in influencing student stress levels.

The study utilized a comprehensive dataset, StressLevelDataset.csv, collected from a diverse group of 1100 students across various educational institutions. We employed machine learning tools, including correlation analysis and feature importance analysis using Random Forest models, to identify and rank the most significant stressors.

Key findings suggest that psychological factors like self-esteem and physiological factors like sleep quality are crucial predictors of stress levels in students. A significant negative correlation was found between students’ anxiety levels and their academic performance, highlighting the adverse impacts of psychological stress on educational outcomes.

The results underscore the importance of targeted interventions focusing on mental health and well-being within educational settings. By addressing the identified stressors, particularly in the psychological and physiological domains, educational institutions can enhance student well-being and improve academic performance.