[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123019-en":3,"doc-seo-123019-105":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},123019,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning Model for Student Drop-out Prediction Based on Student Engagement","Student drop-out represents a complex challenge that extends beyond pedagogy, affecting professors, tutors, and learners. This paper presents a predictive approach that estimates drop-out risk from student performance using multiple machine learning paradigms, including supervised learning, unsupervised learning, and clustering. Results indicate that different dimensions of student engagement—behavioral, emotional, and cognitive—are key determinants for forecasting drop-out and for explaining final ECTS achievement.","Machine Learning Model for Student Drop-out Prediction Based on the Student Engagement  \nLucija Brezonik 1 , Giacomo Nalli2 , Renato De Leone2 , Sonia Val Blasco3 , Vili Podgorelec 1 , and Saˇso Karakati1  \nAbstract Nowadays, the issue of student drop-out is not only addressed through the prism of pedagogy but also by technological practices. In this paper, we demonstrate how a student drop-out could be predicted through a student’s performance using different machine learning techniques, i.e., supervised learning, unsupervised learning, and clustering. The results show that various types of student engagement are essential factors in predicting drop-out and the final ECTS points achievements.  \nKey words: Machine Learning, Student Drop-out, Academic Drop-out, Student Engagement, Student Drop-out Prediction  \n1 Introduction  \nThe problem of student drop-out has been increasingly raising concern because of the complexity of the issue [1] . It is relevant not only for the professors who want to minimize the number of students that do not finish their studies but also to the tutors who work with students and, nevertheless, to the students themselves.  \nMany papers were written in the mentioned problem domain, but mainly from the pedagogical point of view [2–4] . For this research, one of the most meaningful results from their studies was the proven correlation between overall student engagement (behavioral, emotional, and cognitive) and academic achievement [5] . It is vital to emphasise, that some student engagements can be easily tracked, e.g., demographic and academic background, but behavior ones can be trickier. Usually,  \nIntelligent Systems Laboratory, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia  \ne-mail: [lucija.brezocnik@um.si](lucija.brezocnik@um.si), [saso.karakatic@um.si](saso.karakatic@um.si)  \n· School of Science and Technology, University of Camerino, Italy e-mail: [giacomo.nalli@unicam.it](giacomo.nalli@unicam.it), [renato.deleone@unicam.it](renato.deleone@unicam.it)  \n· School of Engineering and Architecture, University of Zaragoza, Spain e-mail: [sonia@unizar.es](sonia@unizar.es)  \n2 Brezonik et al.  \nthey are being tracked by faculties student ID cards, but the faculty or university must provide them. Hence, it is not a norm around EU Universities.  \nA few attempts at applying different Machine Learning (ML) techniques to the student drop-out prevention have been made [6–10] . Usually, the used dataset comprised a small number of features being collected from one faculty. Because of that, the main missions of this paper are:  \n• to define a set of features relevant to be collected for the later student drop-out identification;  \n• to define ML models able to predict student drop-out based on the student performance;  \n• to provide a list of the most informative features for student drop-out.  \n2 Machine Learning Algorithms  \nThe prediction of students’ drop-out based on their performance can be tackled through different approaches. For this reason, sections 2.1 and 2.2 present a brief overview of the most prominent learning approaches that were also used in our proposed method.  \n2.1 Supervised Learning  \nIn Supervised Learning [11], the training set is made ofP input vectors x with corresponding P output vectors y (labels) . Therefore, data and their corresponding “correct” answers are available in this paradigm. The aim is to learn a rule linking the inputs to their corresponding output values (see Eq. 1) .  \nf : x ∈ RN → y ∈ RM (1)  \nMoreover, the machine must then be able to predict the output for new input values. The two main problems that fall into this category are Classification problems and Regression problems. In Classification problems, the goal is to classify data into a finite number of categories. In general, unless some specific encoding is utilized, M = 1 (there is only a single output value) and y p ∈ {1,..., K} . A special case is a binary classific","cbCaine0KgD37ZIY","https://ap.wps.com/l/cbCaine0KgD37ZIY","pdf",363036,1,12,"English","en",105,"# Abstract\n# Introduction\n# Machine Learning Algorithms\n## Supervised Learning\n## Unsupervised Learning\n# Student Engagement Data","[{\"question\":\"How does the paper predict student drop-out?\",\"answer\":\"It predicts drop-out based on student performance using supervised learning, unsupervised learning, and clustering methods.\"},{\"question\":\"Which student engagement factors are important for the prediction?\",\"answer\":\"The study finds that types of student engagement (behavioral, emotional, and cognitive) are essential factors for predicting drop-out and ECTS outcomes.\"},{\"question\":\"What is the role of supervised learning in the approach?\",\"answer\":\"Supervised learning uses input vectors with corresponding labeled outputs, learning a function that maps new inputs to predicted outputs.\"}]","Machine Learning Model for Student Drop-out Prediction Based on Student Engagement | PDF",1785814202,30,{"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},"machine-learning-model-for-student-drop-out-prediction-based-on-student-engagement","",{"@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/machine-learning-model-for-student-drop-out-prediction-based-on-student-engagement/123019/",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},"How does the paper predict student drop-out?","Question",{"text":75,"@type":76},"It predicts drop-out based on student performance using supervised learning, unsupervised learning, and clustering methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which student engagement factors are important for the prediction?",{"text":80,"@type":76},"The study finds that types of student engagement (behavioral, emotional, and cognitive) are essential factors for predicting drop-out and ECTS outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of supervised learning in the approach?",{"text":84,"@type":76},"Supervised learning uses input vectors with corresponding labeled outputs, learning a function that maps new inputs to predicted outputs.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]