[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126189-en":3,"doc-seo-126189-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126189,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A machine learning approach to identifying key predictors of Peruvian school principals’ job satisfaction - Original Research","A machine learning study identifies key predictors of job satisfaction among Peruvian school principals using an ensemble of feature selection methods and five algorithms: Random Forest, Decision Trees (CART), Histogram-Based Gradient Boosting, XGBoost, and LightGBM. Data come from the 2018 National Survey of Directors. Variables include salary satisfaction, institutional location, relationships with students and teachers, workplace climate, learning achievements, job benefits, and economic and time-related factors. Histogram-Based Gradient Boosting with Bayesian optimization and Random Oversampling reaches balanced accuracy 0.63, while GAN balancing improves recall, precision, and F1. SHAP analysis reveals hierarchical influences of economic and interpersonal factors.","TYPE Original Research PUBLISHED 09 May 2025  \nDOI 10. 3389/feduc.2025.1580683  \nOPEN ACCESS  \nEDITED BY  \nAloysius Sequeira,  \nNational Institute of Technology, Karnataka, India  \nREVIEWED BY  \nJean-Baptiste M. B. Sanfo, University of Shiga Prefecture, Japan Vadivel S. M.,  \nVellore Institute of Technology (VIT), India  \n*CORRESPONDENCE  \nLuis Alberto Holgado-Apaza  \n [lholgado@unamad.edu.pe](lholgado@unamad.edu.pe)  \nRECEIVED 25 February 2025  \nACCEPTED 17 April 2025  \nPUBLISHED 09 May 2025  \nCITATION  \nHolgado-Apaza LA, Isuiza-Perez DD, Ulloa-Gallardo NJ, Vilchez-Navarro Y, Aragon-Navarrete RN, Quispe Layme W, Quispe-Layme M, Castellon-Apaza DD, Choquejahua-Acero R and Prieto-Luna JC (2025) A machine learning approach to identifying key predictors of Peruvian school principals’ job satisfaction.  \nFront. Educ. 10:1580683 .  \ndoi: 10.3389/feduc.2025.1580683  \nCOPYRIGHT  \n© 2025 Holgado-Apaza, Isuiza-Perez, Ulloa-Gallardo, Vilchez-Navarro, Aragon-Navarrete, Quispe Layme,  \nQuispe-Layme, Castellon-Apaza, Choquejahua-Acero and Prieto-Luna. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning approach to identifying key predictors of Peruvian school principals’ job satisfaction  \nLuis Alberto Holgado-Apaza1*, Dany Dorian Isuiza-Perez1 , Nelly Jacqueline Ulloa-Gallardo1 , Yban Vilchez-Navarro1 , Ruth Nataly Aragon-Navarrete2 , Wilian Quispe Layme3 , Marleny Quispe-Layme4 , Danger David Castellon-Apaza1 , Remo Choquejahua-Acero5 and Jaime Cesar Prieto-Luna1  \n1 Departamento Académico de Ingeniería de Sistemas e Informática, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado, Peru, 2 Departamento Académico de Ecoturismo, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado, Peru, 3 Departamento Académico de Educación, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado, Peru,  \n4 Departamento Académico de Contabilidad y Administración, Universidad Nacional Amazónica de Madre de Dios, Puerto Maldonado, Peru, 5 Departamento Académico de Ingeniería Estadística e Informática, Universidad Nacional del Altiplano-Puno, Puno, Peru  \nSchool principals encounter contemporary demands that impact their job satisfaction and leadership e􀀀ectiveness. Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. The principal variables identified included satisfaction with salary, geographic location of the educational institution, relationships with students and teachers, workplace climate, student learning achievements, and job benefits. Economic factors proved important, such as gross and net income, and the minimum monthly amount required to meet household needs. Timerelated aspects also exerted influence, including hours dedicated to training, time spent on administrative and/or teaching duties outside working hours, travel time to and from the Local Educational Management Unit (UGEL), duration of stays at the UGEL, and commuting time from principal residence to the educational institution. The Histogram-Based Gradient Boosting algorithm, optimized with Bayesian techniques and trained with data balanced through Random Oversampling, achieved a balanced accuracy of","cbCaie0gXHXZgnDB","https://ap.wps.com/l/cbCaie0gXHXZgnDB","pdf",12309006,7,1,23,"English","en",105,"# 1 Introduction\n## Job satisfaction and principal retention\n## Background on principal role demands\n## Empirical statistics and motivation","[{\"question\":\"What data source is used to identify predictors of job satisfaction?\",\"answer\":\"The study uses the 2018 National Survey of Directors.\"},{\"question\":\"Which factors are found as key predictors of job satisfaction?\",\"answer\":\"Key predictors include salary satisfaction, institutional location, relationships with students and teachers, workplace climate, student learning achievements, and job benefits, along with economic and time-related variables.\"},{\"question\":\"How do the models perform and what role does SHAP play?\",\"answer\":\"Histogram-Based Gradient Boosting achieves a balanced accuracy of 0.63 on a test set, while GAN-based balancing improves recall, precision, and F1. SHAP analysis shows economic factors mainly affect dissatisfied principals and interpersonal factors are more important for highly satisfied principals.\"}]","A machine learning approach to identifying key predictors of Peruvian school principals’ job satisfaction - Original Research | PDF",1785903705,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-machine-learning-approach-to-identifying-key-predictors-of-peruvian-school-principals-job-satisfaction-original-research","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-machine-learning-approach-to-identifying-key-predictors-of-peruvian-school-principals-job-satisfaction-original-research/126189/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data source is used to identify predictors of job satisfaction?","Question",{"text":77,"@type":78},"The study uses the 2018 National Survey of Directors.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which factors are found as key predictors of job satisfaction?",{"text":82,"@type":78},"Key predictors include salary satisfaction, institutional location, relationships with students and teachers, workplace climate, student learning achievements, and job benefits, along with economic and time-related variables.",{"name":84,"@type":75,"acceptedAnswer":85},"How do the models perform and what role does SHAP play?",{"text":86,"@type":78},"Histogram-Based Gradient Boosting achieves a balanced accuracy of 0.63 on a test set, while GAN-based balancing improves recall, precision, and F1. 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