Predictive modelling of burnout among Kazakhstani English teacher candidates

Abstract

Introduction. In Kazakhstan, 55% of English pre-service teacher candidates do not plan to work in public schools after graduation. The current quantitative study, using statistical analysis, sought to determine the causes of this phenomenon.The aim of the present research was to determine the probability of early career burnout among future English teachers using predictive modeling.Methodology and research methods. To create a predictive model, the authors used the following statistical data analysis tools: factor analysis, ordinal regression, and the Сhi-square test. The selection of the above tools provides to predict the probability of work intention in a Kazakhstani public school for more than five years. This period acts as a proxy variable for predicting future teachers’ burnout rates. The study involved 160 pre-service teachers, who received State scholarships for free tuition, with an obligation to work in public schools after graduation.Results and scientific novelty. The data indicated that some respondents would only work for a period of less than five years, due to a sense of despair about their profession. The results demonstrate that pre-service teachers require better training. Furthermore, the authors found a correlation in teaching career expectations and the gender gap, where mature female participants were more likely to carry an intention to work for more than five years.Practical significance. The present study makes a contribution to further research aimed at preventing the shortage and turnover of teaching staff in the workplace by detecting early warning signs of burnout among prospective teachers. The authors identified the indicators of the risk group of novice teachers, who are more prone to burnout due to emotional exhaustion, low self-efficacy, low expectations, and prospects from a teaching career. The authors believe that effective career guidance provides an adequate selection of the most motivated applicants with high occupational expectations.