[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127056-en":3,"doc-seo-127056-105":30,"detail-sidebar-cat-0-en-105":96},{"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":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},127056,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predictive Power of Machine Learning Models on Degree Completion Among Adult Learners - Research Report","Integration of machine learning (ML) in higher education is increasingly viewed as a transformative force for adult learners, who face distinctive barriers to persistence and completion. This study assesses the predictive strength of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—for forecasting degree completion among adult learners. Using data from academic years 2013–14 to 2021–22 with demographic and performance indicators, evaluation relies on accuracy, precision, recall, and F1 score. Results show Gradient-Boosting Machine delivers the strongest predictions, supporting data-driven policies and targeted interventions that improve retention and graduation.","Lindenwood University  \nDigital Commons@Lindenwood University  \n\n| Faculty Scholarship | Research and Scholarship |\n| --- | --- |\n\n6-2024  \nPredictive Power of Machine Learning Models on Degree Completion Among Adult Learners  \nEmily 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 Education Commons  \nPredictive Power of Machine Learning Models on Degree Completion Among Adult Learners  \nEmily Barnes1, James Hutson2*, Karriem Perry3  \n1Capitol Technology University, Maryland 2Lindenwood University, United States 3Capitol Technology University, Maryland  \n*Corresponding Author: James Hutson, Lindenwood University, United States.  \nReceived: June 03, 2024  \nPublished: June 24, 2024  \n© All rights are reserved by James Hutson., et al.  \nAbstract  \nThe integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly enhance data-driven decision-making in educational settings. By highlighting the factors influencing adult learners' educational success, such as age and socioeconomic status, this research supports the strategic implementation of tailored educational policies and interventions, aimed at improving the retention and graduation rates of adult learners in higher education institutions.  \nKeywords: Machine Learning; Adult Learners; Educational Outcomes; Predictive Analytics; Degree Completion  \nIntroduction  \nThe incorporation of machine learning into higher education is increasingly acknowledged as a transformative force, adeptly responding to the changing needs of varied student populations, especially those of adult learners [1] . Studies have traced the historical evolution of higher education, noting a significant shift towards adult learners, who face unique challenges like balancing education with other commitments and adapting to new learning environments [1-3] . The function of ML in higher education surpasses conventional educational frameworks and the analysis of student performance data and the forecasting of educational results. Various ML algorithms, including Random Forests, Decision Trees, and neural networks, have demonstrated effectiveness in forecasting student achievement, retention, and graduation likelihoods [4,5] . ML’s capability in enhancing data-driven decision-making in higher education and aids in the development of inclusive and flexible educational models that cater to the needs of adult learners. This shift towards ML-driven analytics represents a significant advancement in predicting and improving educational strategies and student support mechanisms [1-2,4-5] .  \nBuenaño-Fernández., et al. [6] and Korkmaz and Correia [7] identified the capability of ML to refine personalized learning and  \nenhance student progress monitoring. Oliveira., et al. [8] and Kannan [9] advocated for additional research on feature selection and class balancing, along with exploring human-centric methodologies in education. Hilbert., et al. [10] and Son., et al. [11] investigated the potential and challenges of ML in educational sciences, considering the prospects of automated e","cbCaihjahx7gmSF9","https://ap.wps.com/l/cbCaihjahx7gmSF9","pdf",538555,1,19,"English","en",105,"# Introduction\n## Statement of Problem\n# Methodology\n## Models and Evaluation Metrics\n## Dataset and Features\n# Results\n## Model Performance\n# Discussion\n## Influential Factors and Implications","[{\"question\":\"Which machine learning models are evaluated for predicting degree completion among adult learners?\",\"answer\":\"The study evaluates Random Forest, Gradient-Boosting Machine, and Decision Trees to forecast degree completion for adult learners.\"},{\"question\":\"How is model performance measured in the study?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and the F1 score to compare the effectiveness of the models.\"},{\"question\":\"What dataset time span and data types are used?\",\"answer\":\"The dataset covers academic years 2013–14 to 2021–22 and includes demographic information along with academic performance metrics.\"},{\"question\":\"Which model outperforms the others, and what does it imply for education policy?\",\"answer\":\"Gradient-Boosting Machine outperforms the other models, indicating that ML can strengthen data-driven decision-making and support tailored interventions aimed at improving adult learner retention and graduation.\"}]","Predictive Power of Machine Learning Models on Degree Completion Among Adult Learners - Research Report | PDF",1785936581,48,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"predictive-power-of-machine-learning-models-on-degree-completion-among-adult-learners-research-report","",{"@graph":36,"@context":90},[37,54,69],{"@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/predictive-power-of-machine-learning-models-on-degree-completion-among-adult-learners-research-report/127056/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning models are evaluated for predicting degree completion among adult learners?","Question",{"text":76,"@type":77},"The study evaluates Random Forest, Gradient-Boosting Machine, and Decision Trees to forecast degree completion for adult learners.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is model performance measured in the study?",{"text":81,"@type":77},"Performance is assessed using accuracy, precision, recall, and the F1 score to compare the effectiveness of the models.",{"name":83,"@type":74,"acceptedAnswer":84},"What dataset time span and data types are used?",{"text":85,"@type":77},"The dataset covers academic years 2013–14 to 2021–22 and includes demographic information along with academic performance metrics.",{"name":87,"@type":74,"acceptedAnswer":88},"Which model outperforms the others, and what does it imply for education policy?",{"text":89,"@type":77},"Gradient-Boosting Machine outperforms the other models, indicating that ML can strengthen data-driven decision-making and support tailored interventions aimed at improving adult learner retention and graduation.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":111,"slug":142},"General","general"]