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

Author

Listed:
  • Luis Alberto Holgado-Apaza

    (Departamento Académico de Ingeniería de Sistemas e Informática, Facultad de Ingeniería, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

  • Nelly Jacqueline Ulloa-Gallardo

    (Departamento Académico de Ingeniería de Sistemas e Informática, Facultad de Ingeniería, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

  • Ruth Nataly Aragon-Navarrete

    (Departamento Académico de Ecoturismo, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

  • Raidith Riva-Ruiz

    (Departamento Académico de Ciencias Económicas, Facultad de Ciencias Económicas, Universidad Nacional de San Martin, Tarapoto 22200, Peru)

  • Naomi Karina Odagawa-Aragon

    (Escuela Profesional de Ingeniería de Sistemas e Informática, Facultad de Ingeniería, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

  • Danger David Castellon-Apaza

    (Departamento Académico de Ingeniería de Sistemas e Informática, Facultad de Ingeniería, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

  • Edgar E. Carpio-Vargas

    (Departamento Académico de Ingeniería Estadística e Informática, Universidad Nacional del Altiplano-Puno, Puno 21001, Peru)

  • Fredy Heric Villasante-Saravia

    (Departamento Académico de Ingeniería Estadística e Informática, Universidad Nacional del Altiplano-Puno, Puno 21001, Peru)

  • Teresa P. Alvarez-Rozas

    (Departamento Académico de Ingeniería Estadística e Informática, Universidad Nacional del Altiplano-Puno, Puno 21001, Peru)

  • Marleny Quispe-Layme

    (Departamento Académico de Contabilidad y Administración, Facultad de Ecoturismo, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado 17001, Peru)

Abstract

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.

Suggested Citation

  • Luis Alberto Holgado-Apaza & Nelly Jacqueline Ulloa-Gallardo & Ruth Nataly Aragon-Navarrete & Raidith Riva-Ruiz & Naomi Karina Odagawa-Aragon & Danger David Castellon-Apaza & Edgar E. Carpio-Vargas & , 2024. "The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning," Sustainability, MDPI, vol. 16(17), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7532-:d:1467898
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    Cited by:

    1. Young Mee Jung & Hyeon Jo, 2025. "Understanding Continuance Intention of Generative AI in Education: An ECM-Based Study for Sustainable Learning Engagement," Sustainability, MDPI, vol. 17(13), pages 1-21, July.

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