[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121963-en":3,"doc-seo-121963-105":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},121963,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Exploring the Potential of Machine Learning to Predict Student Performance in an EM Course - Research Findings","This paper explores the use of machine learning to predict student performance in an Electromagnetics course using data from Student-Led Tutorials (SLTs) at Eindhoven University of Technology. The SLT approach since 2018 generates diverse student interaction data, supporting analysis toward an Automated Prognostic Student Progress Monitoring System (APSPMS) that enables timely feedback for teachers and students. Indicators and predictors derived from SLTs are evaluated, using exercise preparedness signals and voice recordings. Models predict pass/fail with measured accuracies on the dataset, reaching about 57.4% for voice-based prediction and 65%+ overall, suggesting learning analytics and machine learning are effective.","Exploring the Potential of Machine Learning to Predict Student Performance in an EM Course  \nCitation for published version (APA):  \nVertegaal, C. J. C. , Sundaramoorthy, P. , Martinez, C. , Serra, R. , & Bentum, M. J. (2023) . Exploring the Potential of Machine Learning to Predict Student Performance in an EM Course. In N. van der Aa (Ed.), 2023 32nd Annual Conference of the European Association for Education in Electrical and Information Engineering, EAEEIE 2023 Article 10181928 Institute of Electrical and Electronics Engineers.  \n[https://doi.org/10.23919/EAEEIE55804.2023.10181928](https://doi.org/10.23919/EAEEIE55804.2023.10181928)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.23919/EAEEIE55804.2023.10181928  \nDocument status and date:  \nPublished: 20/07/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Mar. 2025  \nExploring the Potential of Machine Learning to Predict Student Performance in an EM Course  \nC.J.C. Vertegaal  \nDepartment of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands [c.j.c.vertegaal@tue.nl](c.j.c.vertegaal@tue.nl)  \nP. Sundaramoorthy  \nDepartment of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands [p.p.sundaramoorthy@tue.nl](p.p.sundaramoorthy@tue.nl)  \nC. Martinez  \nNational University of Cordoba CONICET Cordoba, Argentina [cecimart@gmail.com](cecimart@gmail.com)  \nR. Serra  \nDepartment of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands [r.serra@tue.nl](r.serra@tue.nl)  \nM.J. Bentum  \nDepartment of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands [m.j.bentum@tue.nl](m.j.bentum@tue.nl)  \nAbstract—In this paper, machine learning is explored to predict student performance based on data from Student-Led Tutorials (SLT) in an Electromagnetics Course from Eindhoven University of Technology. Since 2018, the course has seen an innovative approach to increase students’ learning using SLTs. The amount and variety of student interactions captured through these SLTs provide data for gaining insight into the student learning process. The analysis presented here is part of the process towards the developmen","cbCaiiv7nSPXqhgq","https://ap.wps.com/l/cbCaiiv7nSPXqhgq","pdf",332218,1,7,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What data source does the study use to predict student performance?\",\"answer\":\"The study uses data collected from Student-Led Tutorials (SLTs) in an Electromagnetics course.\"},{\"question\":\"Which indicators are used in the machine learning models?\",\"answer\":\"Two main indicators are used: the number of exercises students report being prepared for, and voice recordings from the SLT sessions.\"},{\"question\":\"How accurate is the prediction model based on voice recordings?\",\"answer\":\"The paper reports a prediction accuracy of 57.4% on its dataset for voice-recording-based prediction.\"}]","Exploring the Potential of Machine Learning to Predict Student Performance in an EM Course - Research Findings | PDF",1785808049,18,{"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},"exploring-the-potential-of-machine-learning-to-predict-student-performance-in-an-em-course-research-findings","",{"@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/exploring-the-potential-of-machine-learning-to-predict-student-performance-in-an-em-course-research-findings/121963/",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 data source does the study use to predict student performance?","Question",{"text":75,"@type":76},"The study uses data collected from Student-Led Tutorials (SLTs) in an Electromagnetics course.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which indicators are used in the machine learning models?",{"text":80,"@type":76},"Two main indicators are used: the number of exercises students report being prepared for, and voice recordings from the SLT sessions.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the prediction model based on voice recordings?",{"text":84,"@type":76},"The paper reports a prediction accuracy of 57.4% on its dataset for voice-recording-based prediction.","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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"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"]