Applying Classification Techniques in Machine Learning to Predict Job Satisfaction of University Professors: A Sociodemographic and Occupational Perspective

This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) in Ecuador were stratified, resulting in a total of 1600 data points collected through online forms. The research was of a cross-sectional design and quantitative and used machine learning techniques of classification and prediction to analyze variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress, and occupational risk. The results indicate that the best classification model is neural networks with a precision of 0.7304; the most significant variables for predicting the job satisfaction of university teachers are: the number of children they have, scores related to perceived stress, professional risk, and burnout, province of the university at which the university teacher surveyed works, and city where the teacher works. This is in contrast to marital status, which does not contribute to its prediction. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the university academic environment.