[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124070-en":3,"doc-seo-124070-105":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},124070,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing adult learner success in higher education through decision tree models - A machine learning approach","This article investigates how machine learning, using Classification and Regression Trees (CART), can respond to socio-cultural, economic, and institutional barriers faced by adult learners in higher education. It applies decision tree models to assess their value for predicting graduation outcomes and to support tailored educational strategies. Analysis is conducted on a dataset covering academic years 2013–2014 through 2021–2022, evaluating CART performance with precision, recall, and F1 score. Results show attendance, age, and Pell Grant eligibility as key predictors, demonstrating strong predictive capability. The findings support using decision tree models to enable data-driven, more inclusive institutional decision-making and improved support systems for adult learners.","Lindenwood University  \nDigital Commons@Lindenwood University  \n\n| Faculty Scholarship | Research and Scholarship |\n| --- | --- |\n\n7-2024  \nEnhancing adult learner success in higher education through  \ndecision tree models: A machine learning approach Emily Barnes  \nJames Hutson  \nKarriem Perry  \nFollow this and additional works at: [https://digitalcommons.lindenwood.edu/faculty-research-papers](https://digitalcommons.lindenwood.edu/faculty-research-papers)[ ](https://digitalcommons.lindenwood.edu/faculty-research-papers) Part of the Artificial Intelligence and Robotics Commons, and the Higher Education Commons  \nArticle  \nEnhancing adult learner success in higher education through decision tree models: A machine learning approach  \nEmily Barnes1,*, James Hutson2, Karriem Perry3  \n1 Capitol Technology University, AI Center of Excellence (AICE), Laurel, MD 20708, USA  \n2 Lindenwood University, Saint Charles, MO 63301, USA  \n3 Capitol Technology University, Laurel, MD 20708, USA  \n* Corresponding authors: Emily Barnes, [ejbarnes035@gmail.com](ejbarnes035@gmail.com)  \nCITATION  \n\n| Barnes E, Hutson J, Perry K. Enhancing adult learner success in higher education through decision tree models: A machine learning approach. Forum for Education Studies. 2024; 2(3): 1415. [https://doi.org/10.59400/fes.v2i3.1415](https://doi.org/10.59400/fes.v2i3.1415)\u003Cbr>ARTICLE INFO |\n| --- |\n| Received: 3 June 2024\u003Cbr>Accepted: 12 June 2024\u003Cbr>Available online: 9 July 2024\u003Cbr>COPYRIGHT |\n\nCopyright © 2024 author(s) .  \nForum for Education Studies is published by Academic Publishing Pte. Ltd. This work is licensed under the Creative Commons Attribution (CC BY) license. [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by/4.0/](by/4.0/)  \nAbstract: This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive datasetspanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key predictors of academic success, demonstrating the strong capability of the model across various educational metrics. This highlights the potential of machine learning (ML) to improve data-driven decision-making in educational settings. The results affirm the effectiveness of Decision Tree (DT) models in meeting the educational needs of adult learners and underscore the need for institutions to adapt their strategies to provide more inclusive and supportive environments. This study advocates for a shift towards nuanced, data-driven approaches in higher education, emphasizing the development of strategies that address the distinct challenges of adult learners, aiming to enhance inclusivity and support within the sector.  \nKeywords: adult learners; decision tree models; machine learning; higher education; predictive accuracy  \n1. Introduction  \nIn the landscape of higher education, the utility of machine learning (ML), especially Decision Tree (DT) models, has gained significant traction as a means to address the unique and multifaceted challenges encountered by adult learners [1,2] . Adult learners represent a substantial and growing segment of the student population, yet they face numerous socio-cultural, economic, and institutional obstacles that shape their educational experiences. These challenges include pervasive stereotypes that question their ability to learn, financial constraints th","cbCaiiPp3DcdpZou","https://ap.wps.com/l/cbCaiiPp3DcdpZou","pdf",436099,1,18,"English","en",105,"# Introduction\n## Research gap and study aims\n# Method and data\n## Decision tree modeling approach\n## Evaluation metrics\n# Results and key predictors\n## Graduation outcome prediction\n# Implications for higher education\n## Data-driven strategies for inclusivity","[{\"question\":\"What machine learning method does the study use to support adult learners?\",\"answer\":\"The study uses Classification and Regression Trees (CART) as decision tree models to evaluate how well they can predict outcomes for adult learners.\"},{\"question\":\"Which factors are identified as key predictors of academic success?\",\"answer\":\"Attendance, age, and Pell Grant eligibility are reported as key predictors of academic success and graduation outcomes.\"},{\"question\":\"How is the model performance evaluated in the research?\",\"answer\":\"Model performance is assessed using precision, recall, and F1 score to measure predictive accuracy across educational metrics.\"}]","Enhancing adult learner success in higher education through decision tree models - A machine learning approach | PDF",1785820184,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-adult-learner-success-in-higher-education-through-decision-tree-models-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-adult-learner-success-in-higher-education-through-decision-tree-models-a-machine-learning-approach/124070/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning method does the study use to support adult learners?","Question",{"text":75,"@type":76},"The study uses Classification and Regression Trees (CART) as decision tree models to evaluate how well they can predict outcomes for adult learners.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors are identified as key predictors of academic success?",{"text":80,"@type":76},"Attendance, age, and Pell Grant eligibility are reported as key predictors of academic success and graduation outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance evaluated in the research?",{"text":84,"@type":76},"Model performance is assessed using precision, recall, and F1 score to measure predictive accuracy across educational metrics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]