The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning

Holgado-Apaza L.A.; Ulloa-Gallardo N.J.; Aragon-Navarrete R.N.; Riva-Ruiz R.; Odagawa-Aragon N.K.; Castellon-Apaza D.D.; Carpio-Vargas E.E.; Villasante-Saravia F.H.; Alvarez-Rozas T.P.; Quispe-Layme M. · Sustainability (Switzerland) (2024) · DOI: 10.3390/su16177532

Resumen

Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.

Instituciones: Universidad Nacional del Altiplano; Universidad Nacional Amazónica de Madre de Dios; Universidad Nacional de San Martín