[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128814-105":59,"doc-detail-128814-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","predicting-teacher-turnover-in-private-universities-a-machine-learning-approach-based-on-10-years-of-data-and-satisfaction-factors","Predicting teacher turnover in private universities: a machine learning approach based on 10 years of data and satisfaction factors","","Teacher turnover threatens the sustainable development of private universities in China, yet many existing machine-learning studies underuse psychological variables and rarely exploit longitudinal evidence. This research combines a 10-year longitudinal dataset with satisfaction surveys from a private university in Western China, and applies exploratory factor analysis to derive key dimensions related to turnover. Three models (KNN, Naive Bayes, and BPNN) are compared using accuracy, F1-score, and AUC.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-teacher-turnover-in-private-universities-a-machine-learning-approach-based-on-10-years-of-data-and-satisfaction-factors/128814/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-teacher-turnover-in-private-universities-a-machine-learning-approach-based-on-10-years-of-data-and-satisfaction-factors/128814.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is predicting teacher turnover important for private universities?","Question",{"text":113,"@type":114},"Teacher turnover undermines recruitment and retention of high-quality faculty and increases training and recruitment costs, which can reduce teaching quality and long-term institutional planning.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the study use job satisfaction information?",{"text":118,"@type":114},"It integrates satisfaction survey data with a 10-year longitudinal dataset, then employs exploratory factor analysis to extract dimensions influencing turnover, including the compensation, benefits, and development dimension.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning model performs best and what metric supports it?",{"text":122,"@type":114},"The K-Nearest Neighbors (KNN) model achieves the highest predictive performance with accuracy of 83.64%, F1-score of 84.16%, and AUC of 0.901.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128814,1786003648,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","TYPE Original Research PUBLISHED 06 November 2025 DOI 10.3389/fpsyg.2025.1670195  \nOPEN ACCESS  \nEDITED BY  \nJun Yang,  \nUniversity of North Carolina at Greensboro, United States  \nREVIEWED BY  \nArlette J. Ngoubene-Atioky, Goucher College, United States Enrique H. Riquelme,  \nTemuco Catholic University, Chile  \n*CORRESPONDENCE  \nWang Jingwen  \n [1084313786@qq.com](1084313786@qq.com)  \nRECEIVED 21 July 2025  \nACCEPTED 14 October 2025  \nPUBLISHED 06 November 2025  \nCITATION  \nJingwen W, Yi L and Xiaohong Y (2025) Predicting teacher turnover in private universities: a machine learning approach based on 10 years of data and satisfaction factors.  \nFront. Psychol. 16:1670195 .  \ndoi: 10.3389/fpsyg.2025.1670195  \nCOPYRIGHT  \n© 2025 Jingwen, Yi and Xiaohong. This is an 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.  \nPredicting teacher turnover in private universities: a machine learning approach based on  \n10 years of data and satisfaction factors  \nWang Jingwen 1,2*, Liu Yi 1 and Yang Xiaohong 2  \n1General Office of the Party and Government, Xi'an Fanyi University, Xi'an, China, 2College of Educational Technology, Northwest Normal University, Lanzhou, China  \nBackground: Teacher turnover poses a significant challenge to the sustainable development of private universities in China. While machine learning (ML) has been increasingly applied to turnover prediction, existing studies often overlook psychological factors and lack longitudinal analysis.  \nMethods: This study integrates a 10-year longitudinal dataset with satisfaction surveys from a private university in Western China. Exploratory Factor Analysis (EFA) was employed to extract key dimensions influencing turnover. Three ML models—K-Nearest Neighbors (KNN), Naive Bayes (NB), and Backpropagation Neural Network (BPNN)—were constructed and evaluated using accuracy, F1-score, and AUC.  \nResults: The KNN model achieved the highest predictive performance (accuracy = 83.64%, F1 = 84.16%, AUC = 0.901) . The “Compensation, Benefits, and Development”dimension was identified as the most influential factor, accounting for 25 .41% of the variance.  \nConclusion: This study proposes an “EFA + ML” hybrid approach that enhances feature interpretability and prediction robustness, offering practical insights for human resource management in private higher education institutions.  \nKEYWORDS  \nteacher turnover, private universities, machine learning, exploratory factor analysis, job satisfaction  \n1 Introduction  \nWith the continuous development of China’s higher education system, private universities have become an indispensable part of this landscape, playing a crucial role in promoting educational equity and diversity. According to the “2023 National Education Development Statistical Bulletin” released by the Ministry of Education, there are 789 private universities in China, accounting for 25.67% of the total number of universities. Private universities also enroll 26.34% of the total student population across ordinary, vocational, and junior colleges, and employ 20.13% of all full-time teachers in Chinese universities (Ministry of Education of the People's Republic of China, 2024) .  \nDespite substantial growth in both number and student enrollment, private universities continue to experience difficulties in recruiting and retaining high-quality faculty. This challenge has emerged as a key constraint on the sustainable development of these institutions. Owing to their “non-enterprise” and “extra-system” status, faculty in private universities often experience systemic inequities compared to their public-univ","cbCairu2OBG6WfvX","https://ap.wps.com/l/cbCairu2OBG6WfvX","pdf",486814,13,"English","# Introduction\n## Background and problem motivation\n## Prior work and research gap\n# Methods\n## Data and satisfaction surveys\n## Exploratory Factor Analysis (EFA)\n## Machine learning models and evaluation\n# Results\n## Model performance comparison\n## Most influential satisfaction dimension\n# Conclusion\n## EFA + ML hybrid approach and implications","[{\"question\":\"Why is predicting teacher turnover important for private universities?\",\"answer\":\"Teacher turnover undermines recruitment and retention of high-quality faculty and increases training and recruitment costs, which can reduce teaching quality and long-term institutional planning.\"},{\"question\":\"How does the study use job satisfaction information?\",\"answer\":\"It integrates satisfaction survey data with a 10-year longitudinal dataset, then employs exploratory factor analysis to extract dimensions influencing turnover, including the compensation, benefits, and development dimension.\"},{\"question\":\"Which machine learning model performs best and what metric supports it?\",\"answer\":\"The K-Nearest Neighbors (KNN) model achieves the highest predictive performance with accuracy of 83.64%, F1-score of 84.16%, and AUC of 0.901.\"}]","Predicting teacher turnover in private universities: a machine learning approach based on 10 years of data and satisfaction factors | PDF",33]