[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125106-en":3,"doc-seo-125106-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125106,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning analysis of factors affecting college students’ academic performance","The study explores key factors influencing college students’ academic performance, focusing on metacognitive awareness, learning motivation, learning participation, environmental conditions, time management, and mental health. Using chi-square testing to identify features associated with academic outcomes, the work applies multiple machine learning models—LOG, SVC, RFC, and XGBoost—for prediction. Results show XGBoost achieves the strongest recall and accuracy, and the most influential drivers include metacognitive awareness, motivation, and participation. Time management, environmental factors, and mental health are also significant. The findings support educational management and guidance and validate professional training’s positive role by linking theory to practice.","TYPE Original Research PUBLISHED 23 December 2024 DOI 10.3389/fpsyg.2024.1447825  \nOPEN ACCESS  \nEDITED BY  \nHenri Tilga,  \nUniversity of Tartu, Estonia  \nREVIEWED BY  \nCristina Tripon,  \nPolytechnic University of Bucharest, Romania Ahmed Ramadan Khatiry,  \nUniversity of Technology and Applied Sciences, Oman  \n*CORRESPONDENCE  \nJingzhao Lu  \n [lujingzhao123@126.com](lujingzhao123@126.com)[ ](lujingzhao123@126.com)Peihong Zhao  \n [571509076@qq.com](571509076@qq.com)[ ](571509076@qq.com)Yaju Liu  \n [85069961@qq.com](85069961@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 12 June 2024  \nACCEPTED 26 November 2024  \nPUBLISHED 23 December 2024  \nCITATION  \nLu J, Liu Y, Liu S, Yan Z, Zhao X,  \nZhang Y, Yang C, Zhang H, Su W and Zhao P (2024) Machine learning analysis offactors affecting college students’ academic performance.  \nFront. Psychol. 15:1447825 .  \ndoi: 10.3389/fpsyg.2024.1447825  \nCOPYRIGHT  \n© 2024 Lu, Liu, Liu, Yan, Zhao, Zhang, Yang, Zhang, Su and Zhao. This is an open-access article distributed under the terms of the  \nCreative 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.  \nMachine learning analysis offactors affecting college students’academic performance  \nJingzhao Lu *†, Yaju Liu *†, Shuo Liu, Zhuo Yan, Xiaoyu Zhao, Yi Zhang, Chongran Yang, Haoxin Zhang, Wei Su and Peihong Zhao *  \nDepartment of Science and Technology, Hebei Agricultural University, Huanghua, China  \nThis study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning motivation, participation in learning, environmental factors, time management, and mental health. By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. Experimental results indicate that the XGBoost model performs the best in terms of recall and accuracy, providing a robust prediction for academic performance. Empirical analysis reveals that metacognitive awareness, learning motivation, and participation in learning are crucial factors influencing academic performance. Additionally, time management, environmental factors, and mental health are confirmed to have a significant impact on students’ academic achievements. Furthermore, the positive influence of professional training on academic performance is validated, contributing to the integration of theoretical knowledge and practical application, enhancing students’ overall comprehensive competence. The conclusions offer guidance for future educational management and guidance, emphasizing the importance of cultivating students’ learning motivation, improving participation in learning, and addressing time management and mental health issues, as well as recognizing the positive role of professional training.  \nKEYWORDS  \nXGBoost, machine learning models, learning motivation, academic performance, college students  \n1 Introduction  \nIn recent years, the importance of cultivating professional talents and innovation capability has become increasingly recognized. In order to become high-quality professionals, individuals need to possess not only excellent academic achievements and physical fitness but also have access to excellent universities that provide high-level learning platforms to enhance their overall qualities. Consequently, there is a current emphasis on discussing how to improve students’ learning levels in the education sector. A key aspect of this discussion involves understanding and analyzing the factors that correctly infl","cbCaiksthhnCHSbV","https://ap.wps.com/l/cbCaiksthhnCHSbV","pdf",1030771,1,14,"English","en",105,"# Introduction\n## Background and importance\n## Prior research and evaluation challenges\n## Motivation for predictive analysis","[{\"question\":\"Which factors are examined for their influence on college students’ academic performance?\",\"answer\":\"The study considers metacognitive awareness, learning motivation, learning participation, environmental factors, time management, and mental health.\"},{\"question\":\"How are related features identified and what role do statistical tests play?\",\"answer\":\"A chi-square test is used to find features closely related to academic performance, supporting the subsequent modeling and prediction.\"},{\"question\":\"Which machine learning model performs best and what does it imply?\",\"answer\":\"XGBoost performs best in recall and accuracy, 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