[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119403-en":3,"doc-seo-119403-105":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},119403,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Using Machine Learning to Cluster and Predict Students’ Learning Styles - Completed Research Full Paper","It is crucial to understand individual learning styles when designing personalized and effective educational experiences. This research applies machine learning techniques to predict high school students’ learning styles using data collected via a structured questionnaire. The elbow method and k-prototype identify three optimum cluster numbers, and a four-cluster setup is also examined under the Felder-Silverman Learning Style Model dimensions. Supervised models validate predictions and report peak performance with Random Forest and SVM.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| AMCIS 2025 Proceedings | Americas Conference on Information Systems\u003Cbr>(AMCIS) |\n| --- | --- |\n| August 2025\u003Cbr>Using Machine Learning to Cluster and Predict Students’ Learning Styles\u003Cbr>Lord Coffie\u003Cbr>Georgia Southern University, [lc21685@georgiasouthern.edu](lc21685@georgiasouthern.edu)\u003Cbr>Hayden Wimmer\u003Cbr>Georgia Southern, [hayden.wimmer@gmail.com](hayden.wimmer@gmail.com)\u003Cbr>Jie Du\u003Cbr>Grand Valley State University, [dujie@gvsu.edu](dujie@gvsu.edu)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/amcis2025](https://aisel.aisnet.org/amcis2025) |  |\n\nRecommended Citation  \nCoffie, Lord; Wimmer, Hayden; and Du, Jie, \"Using Machine Learning to Cluster and Predict Students’Learning Styles\" (2025) . AMCIS 2025 Proceedings. 29.  \n[https://aisel.aisnet.org/amcis2025/is_education/is_education/29](https://aisel.aisnet.org/amcis2025/is_education/is_education/29)  \nThis material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in AMCIS 2025 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please contact [elibrary@aisnet.org](elibrary@aisnet.org).  \nUsing Machine Learning to Predict Learning Styles  \nUsing Machine Learning to Cluster and Predict Students’Learning Styles  \nCompleted Research Full Paper  \nLord Coffie  \nDepartment of Information Technology  \nInstitute for Health Logistics & Analytics Georgia Southern University [lc21685@georgiasouthern.edu](lc21685@georgiasouthern.edu)  \nHayden Wimmer  \nDepartment of Information Technology  \nInstitute for Health Logistics & Analytics Georgia Southern University [hwimmer@georgiasouthern.edu](hwimmer@georgiasouthern.edu)  \nJie Du  \nCollege of Computing  \nGrand Valley State University  \n[dujie@gvsu.edu](dujie@gvsu.edu)  \nAbstract  \nIt is crucial to understand individual learning styles when designing personalized and effective educational experiences. This research applies machine learning techniques to predict high school students’ learning styles. With data collected via a structured questionnaire, the elbow method and the k-prototype algorithm were used to identify three optimum numbers of clusters: the pragmatic/goal-oriented learners, the reflective/resilient learners, and the meticulous/methodical learners. A four-cluster configuration was also examined according to the four dimensions in the Felder-Silverman Learning Style Model. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes were employed to validate the clustering results. Random Forest and SVM recorded the maximum performance rates at 95% and 90% for the three-cluster and four-cluster configurations respectively. The results suggest incorporating machine learning in educational systems to encourage adaptive and inclusive learning environments and highlight the implication of predictive modeling in enhancing student engagement and academic results.  \nKeywords  \nMachine learning, learning style, cluster, education, student.  \nIntroduction  \nThe evolution of educational practices has highlighted the necessity of understanding and addressing individual learning preferences to enhance academic outcomes. Students exhibit diverse learning styles, which significantly influence how they process, comprehend, and retain information. Traditional teaching methods often fail to cater to these variations, resulting in suboptimal learning experiences for many students. The need for adaptive and personalized learning approaches has, therefore, become a critical area of focus in educational research.  \nThirty-first Americas Conference on Information Systems, Montréal, 2025 1  \nUsing Machine Learning to Predict Learning Styles  \nLearning style models, such as the Felder-Silverman Learning Style Model (FSLSM) (Felder & Silverman, 1988), provide a structured framework for c","cbCaiqAO1DTiKYpl","https://ap.wps.com/l/cbCaiqAO1DTiKYpl","pdf",539889,1,11,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n# Learning Style Models and Motivation\n# Study Aim and Approach\n# Paper Structure","[{\"question\":\"How are students’ learning styles identified in this study?\",\"answer\":\"The study uses a structured questionnaire and applies clustering to group students’ learning styles based on the Felder-Silverman Learning Style Model dimensions.\"},{\"question\":\"Which clustering methods are used to determine the optimum number of clusters?\",\"answer\":\"The elbow method is used alongside the k-prototype algorithm to identify three optimum cluster numbers, and a four-cluster configuration is also evaluated.\"},{\"question\":\"How are the clustering results validated and what models perform best?\",\"answer\":\"Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes validate the results. Random Forest and SVM achieve the highest recorded performance rates (up to 95% and 90% respectively).\"}]","Using Machine Learning to Cluster and Predict Students’ Learning Styles - Completed Research Full Paper | PDF",1785724114,28,{"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},"using-machine-learning-to-cluster-and-predict-students-learning-styles-completed-research-full-paper","",{"@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/using-machine-learning-to-cluster-and-predict-students-learning-styles-completed-research-full-paper/119403/",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},"How are students’ learning styles identified in this study?","Question",{"text":75,"@type":76},"The study uses a structured questionnaire and applies clustering to group students’ learning styles based on the Felder-Silverman Learning Style Model dimensions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which clustering methods are used to determine the optimum number of clusters?",{"text":80,"@type":76},"The elbow method is used alongside the k-prototype algorithm to identify three optimum cluster numbers, and a four-cluster configuration is also evaluated.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the clustering results validated and what models perform best?",{"text":84,"@type":76},"Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes validate the results. 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