Modeling Job Satisfaction of Peruvian Basic Education Teachers Using Machine Learning Techniques

Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 9...

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主要な著者:	Luis Alberto Holgado-Apaza, Edgar E. Carpio-Vargas, Hugo D. Calderon-Vilca, Joab Maquera-Ramirez, Nelly J. Ulloa-Gallardo, María Susana Acosta-Navarrete, José Miguel Barrón-Adame, Marleny Quispe-Layme, Rossana Hidalgo-Pozzi, Miguel Valles-Coral
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2023-03-01
シリーズ:	Applied Sciences
主題:	feature selection predictive model random forest supervised learning XGBoost
オンライン･アクセス:	https://www.mdpi.com/2076-3417/13/6/3945
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