Prediction of Iranian EFL Teachers' Burnout Level Using Machine Learning Algorithms and Maslach Burnout Inventory

Abstract

Burnout is that the result of extreme work-related stress and it's categorized by emotional, psychological, and physical exhaustion. we will count it as a modern epidemic within the teaching profession likewise as different professions. The measures that may have an effect on a lecturer and can cause burnout are categorized as Depersonalization, Emotional Exhaustion, Personal Accomplishment. though articles on educator burnout vary in their approach, they incline to specialize in the causes of burnout, the way to assess the signs of burnout, methods to stop burnout, and/or next steps for directors and teachers. The main issue that has been incomprehensible is the prediction of obtaining burned out. Through prediction, we will cut back the prices of the burnout impact on teachers, students, schools, and society and stop its consequences like depression, coronary failure, or perhaps suicide. Our objective is to research and predict the burnout level in English as a far-off Language (EFL) teacher. we are going to use Maslach Burnout Inventory (MBI) to gather a dataset and live the 3 subscales of teacher burnout: emotional exhaustion, depersonalization, and reduced personal accomplishment. when analyzing the collected dataset that consisted of 1433 teachers’ data, 9 machine learning classification algorithms by implementing exploitation python programing language is applied and accuracy is employed as a performance parameter.