FORECASTING WITH ARTIFICIAL NEURAL NETWORK OF SCIENCE TEACHERS’ PROFESSIONAL BURNOUT VARIABLES

Abstract

The aim of this study is to predict science teachers’ professional burnout and competence variables with artificial neural network. Therefore burnout, self-efficacy and competence surveys were carried out to science teachers. An artificial neural network has been established with the data obtained. According to the findings, self-efficacy and competence of science teachers may be forecasted professional burnout at various rates. Predictions of the network for the three dimensions of burnout: emotional exhaustion, depersonalization and personal accomplishment is as follows: The performance of network is 40% for “emotional exhaustion”, is 50% for “personal success”, is about 20% for “depersonalization” and is 80% for “competence”. Finally, according to all the results of the study, some suggestions have been developed.