[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121036-en":3,"doc-seo-121036-105":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},121036,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","A Comparative Study of Machine Learning Techniques for College Student Success Prediction","The study compares the predictive performance of multiple machine learning models for estimating college student persistence and success. It reviews historical work on retention and the evolution of predictive modeling, then uses an anonymized ResearchGate dataset covering 2008–2018 with 37 features and 4,424 records. Ten algorithms are evaluated using accuracy, precision, recall, and F1-score. Results indicate Random Forest performs better than Logistic Regression, especially with SMOTE to handle class imbalance, supporting improved retention interventions.","A Comparative Study of Machine Learning Techniques for College  \nStudent Success Prediction  \nZaiyong Tang  \nSalem State University  \nAnurag Jain  \nSalem State University  \nFernando E. Colina  \nSalem State University  \nThe study aims to compare the performance of various machine learning models for student persistence prediction. The research starts with a historical review of student retention studies and the evolution of predictive models in the field. It highlights the importance of predicting student persistence for educational institutions and individuals. It then describes a dataset from ResearchGate, consisting of anonymized undergraduate student data collected between 2008 and 2018, with 37 features and 4,424 records. Ten machine learning algorithms are considered, with two popular machine learning algorithms, Logistic Regression, and Random Forest classification, being compared in more detail for their performance in predicting student persistence. Evaluation metrics such as prediction accuracy, precision, recall, and F1-score are used. Results show that the Random Forest model outperforms Logistic Regression in predicting student outcomes, particularly when using the synthetic minority oversampling technique (SMOTE) to address the class imbalance. Overall, this study contributes to student retention research and provides insights for developing targeted support measures to enhance student success in higher education.  \nKeywords: student success, prediction, model comparison, logistic regression, random forest  \nINTRODUCTION  \nStudent persistence is the ability and willingness of students to continue their educational journey and persevere despite challenges and obstacles they may encounter. Research in this domain of student success has consistently been involved in the business of predicting success rates. Over time, multiple areas of challenges faced by students have been discovered. These challenges include factors such as academic difficulties, financial pressures, personal and family issues, and social /cultural pressures that may impact a student ’s ability to traverse the educational system and succeed academically.  \nStudent persistence studies have been a critical area of research for decades. The National Center for Education Statistics (Kuh et al., 2006) undertook a comprehensive review. The focus of research efforts in this area is to understand the existing and new factors that influence whether a student successfully  \ncompletes their program. The study of student persistence is important because it has significant implications for the effectiveness and efficiency of educational institutions and, in some measures, for the social and economic outcomes of individuals and society.  \nThe study of student persistence in higher education began in earnest in the late 60s and early 70s, with research focused on understanding why students leave college before completing their degrees (Bean & Metzner, 1985) . Earlier research on student success identified several factors contributing to student attrition, including academic preparedness, financial need, and institutional characteristics. Tinto’s (1975) seminal work on student retention, which proposed a theoretical model of student persistence based on social and academic integration, has been highly influential in the field of higher education research. Early instances of using statistical techniques such as factor analysis were deployed to verify and validate Tinto ’s model (Pascarella & Terenzini, 1980) . Other testing models, such as multiple regression (a classic machine learning model), were deployed (Bean, 1980) . These aforementioned studies are considered path-setting works.  \nWith the availability of multiple machine learning models, large data sets, computation speed, and advancements in data science and analytics, there has been a growing interest in using machine learning to develop predictive models to study and improve student success. Various type","cbCaim00kdioufv6","https://ap.wps.com/l/cbCaim00kdioufv6","pdf",381846,1,16,"English","en",105,"# Introduction\n## Student persistence and its significance\n## Historical foundations of retention research\n## Machine learning for student success prediction","[{\"question\":\"What does the study focus on predicting for college students?\",\"answer\":\"It predicts student persistence, which reflects students’ ability and willingness to continue their education despite challenges.\"},{\"question\":\"What dataset is used in the research?\",\"answer\":\"The study uses an anonymized undergraduate student dataset from ResearchGate, collected between 2008 and 2018, containing 37 features and 4,424 records.\"},{\"question\":\"Which model performs best and how is class imbalance handled?\",\"answer\":\"Random Forest outperforms Logistic Regression, and its advantage is especially strong when using SMOTE to address class imbalance.\"}]","A Comparative Study of Machine Learning Techniques for College Student Success Prediction | PDF",1785733424,40,{"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},"a-comparative-study-of-machine-learning-techniques-for-college-student-success-prediction","",{"@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/a-comparative-study-of-machine-learning-techniques-for-college-student-success-prediction/121036/",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","2026-08-03",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 does the study focus on predicting for college students?","Question",{"text":75,"@type":76},"It predicts student persistence, which reflects students’ ability and willingness to continue their education despite challenges.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used in the research?",{"text":80,"@type":76},"The study uses an anonymized undergraduate student dataset from ResearchGate, collected between 2008 and 2018, containing 37 features and 4,424 records.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and how is class imbalance handled?",{"text":84,"@type":76},"Random Forest outperforms Logistic Regression, and its advantage is especially strong when using SMOTE to address class imbalance.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]