Prediction of Iranian EFL teachers' burnout level using machine learning algorithms and maslach burnout inventory

Abstract

AbstractBurnout results from constantly feeling emotional, physical, and mental stress. Most of the time, it is related to one's job and involves a sense of reduced accomplishment and loss of personal identity. Because accountability pressures, workload, and hours can increase stress, teachers are usually high achievers who like to work hard. They confront significant challenges. They must adapt curricula to a wide range of learning styles, manage to shift education policies, attend to students with special needs, and juggle administrative work. In addition, pay remains low in comparison with other graduate roles. Therefore, after prolonged exposure to poorly managed emotional and interpersonal job stress, many experience teacher burnout, resulting in employee turnover and many socio-economic problems. In this regard, accurate prediction provides essential research and decision-making benefits. To this aim, the Maslach Burnout Inventory was administered to a sample of 1433 Iranian EFL teachers. Moreover, nine different machine learning algorithms were implemented on the data set to predict burnout levels through the Python programming language. The algorithms' performances were also investigated through accuracy. In conclusion, the results of this study demonstrate the prediction of teachers' burnout levels to prevent the destructive consequences of the issue.