Modeling the teacher job satisfaction by artificial neural networks

Abstract

This article uses the artificial neural networks (ANNs) method to investigate the association between various dimensions of demographic and coaching leadership with the job satisfaction of teachers in Korean schools. ANN models demonstrate a superior capability to model the relationship with higher predictive accuracy than multiple regression analysis. A user-friendly standalone software is developed for prediction and estimating the relative importance of independent variables on job satisfaction. The graphical representation of results provides strong evidence of complexity, signifying that nonlinear representations understand the relationship between demographic and coaching dimensions with job satisfaction. Eventually, the proposed framework is a practical and accurate method to tackle influential factors and assessment problems in the organization.