Machine Learning Approaches for Analyzing Stress Adaptation and Resilience Among Teachers in Higher Education

Abstract

This research investigates the application of machine learning techniques to explore stress adaptation and resilience among teachers in higher education. Teachers frequently experience various stressors that can affect their mental well-being and professional performance. Utilizing advanced machine learning methodologies enables the identification of key predictors of stress and resilience, providing valuable insights into the coping mechanisms employed by educators. The study employs a systematic literature review to assess existing machine learning models and their effectiveness in predicting stress adaptation outcomes. Additionally, it introduces a predictive framework that captures the complex interplay between stressors and resilience factors, facilitating early detection of individuals at risk of burnout. ​Results show that machine learning not only enhances the understanding of stress dynamics among educators but also informs the development of targeted interventions to foster resilience.​ This comprehensive analysis contributes to a growing body of literature on educational stress, offering practical implications for policy-makers and educational institutions aiming to improve teacher well-being and sustainability in the academic environment.