Machine Learning Models for Salary Prediction in Peruvian Teachers of Regular Basic Education

Tinoco Ramos José, Yupanqui Arellano Jhoset, Juan J. Soria, Nemias Saboya

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper presents an analysis of machine learning (ML) models to predict the salaries of 108,317 appointed teachers in Ventanilla, Lima, Peru, using recent data. The focal point of the study is appointed teachers, deliberately excluding salaries of hired teachers’ from the analysis. A significant result of this research is the identification of a new ML model capable of predicting teacher salaries with considerable accuracy, based on regressor variables closely related to salary. This finding is noteworthy because it fills a gap in existing ML applications for salary prediction, indicating a promising direction for future research in this area. The methodology used to analyze the wage data, while comprehensive, does not account for gender differences, which may affect wage variation over the five-year period considered. This oversight suggests that future research should include a wider range of variables, including gender, to improve the accuracy and applicability of salary predictions for both appointed and contract faculty. Such an approach could provide more nuanced information on the factors influencing teacher salaries and help develop more equitable and effective salary models. One of the key contributions of the article is the detailed examination of the factors influencing salaries of appointed teachers, including age, educational level, length of service, teaching scale, and hours worked. The use of linear regression, Ridge, Lasso, and Elastic Net models yielded accurate metrics for choosing the best model for salary prediction. This research not only advances our understanding of the determinants of teacher salaries in Peru, but also provides a valuable framework for similar studies in other contexts. Comparison with other research highlights the robustness of the chosen ML models, underscoring the potential of ML in educational administration and policymaking.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence Algorithm Design for Systems - Proceedings of 13th Computer Science Online Conference 2024
EditoresRadek Silhavy, Petr Silhavy
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas534-552
Número de páginas19
ISBN (versión impresa)9783031705175
DOI
EstadoPublicada - 2024
Evento13th Computer Science Online Conference, CSOC 2024 - Virtual, Online
Duración: 25 abr. 202428 abr. 2024

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen1120 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia13th Computer Science Online Conference, CSOC 2024
CiudadVirtual, Online
Período25/04/2428/04/24

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