[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118849-en":3,"doc-seo-118849-105":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},118849,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Design of a Machine Learning Model to Predict Student Attrition","Higher education faces high and persistent student dropout rates, especially among first-year learners, creating economic and social impacts and direct personal consequences. Effective prevention depends on identifying at-risk students early and delivering targeted support without lowering educational quality. Machine learning models have demonstrated strong accuracy for detecting dropout risk. This study builds a machine learning model using data extracted from the Neptun administration system to predict student attrition in the Business Informatics BSc program at the Budapest Business School, Faculty of Finance and Accounting.","[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \n JET InternationEmeragl JurnalngofTechnologies in Learning   \niJET | eISSN: 1863-0383 | Vol. 18 No. 17 (2023) |   \n[https://doi.org/10.3991/ijet.v18i17.41449](https://doi.org/10.3991/ijet.v18i17.41449)  \nPAPER  \nDesign of a Machine Learning Model to Predict Student Attrition  \nTibor Fauszt1(􀀍), Katalin Erdélyi1, Dóra Dobák1, László Bognár2, Endre Kovács1  \n1Budapest Business School University of Applied Sciences, Budapest, Hungary  \n2University of Dunaújváros, Dunaújváros, Hungary  \n[fauszt.tibor@uni-bge.hu](fauszt.tibor@uni-bge.hu)  \nABSTRACT  \nHigher education institutions are facing a major issue with student dropout rates, which is a global phenomenon that affects a significant portion of enrolled students, particularly those in their first year. The challenge is how to retain students who do not meet requirements during their first year and are at high risk of dropping out, which can have significant economic and social consequences as well as personal ramifications for the students themselves. Universities must prioritize identifying at-risk students and providing targeted assistance to prevent them from leaving the system. Machine learning (ML) models have proven effective in identifying students at risk of dropping out with a high degree of accuracy. In this study, we aim to construct a machine learning model using data extracted from the administration system (Neptun) to predict student dropout rates in the Business Informatics BSc course at the Faculty of Finance and Accounting of Budapest Business School.  \nKEYWORDS  \nstudent dropout, learning analytics, machine learning (ML)  \n1 INTRODUCTION  \nThe BSc. in Business Informatics, offered by the Faculty of Finance and Accounting at Budapest Business School, was introduced in the academic year 2011–12 with an initial enrollment of 149 full-time students. Since its inception, the program has been consistently popular among students, with a steady increase in the number of both full-time and part-time students over the years. While approximately 400 students begin the program each year, only approximately half complete it, with the other half dropping out of the university. This dropout rate is not exceptional and is on par with the average rate for higher education institutions in Hungary [24] . However, reducing the dropout rate is a top priority for universities, as it is an important indicator of educational quality [17] [19] [22] . It is crucial to implement measures to prevent students from dropping out without compromising the quality of education.  \nFauszt, T., Erdélyi, K., Dobák, D., Bognár, L., Kovács, E. (2023) . Design of a Machine Learning Model to Predict Student Attrition. International Journal of Emerging Technologies in Learning (iJET), 18(17), pp. 184–195. [https://doi.org/10.3991/ijet.v18i17.41449](https://doi.org/10.3991/ijet.v18i17.41449)[ ](https://doi.org/10.3991/ijet.v18i17.41449)[Article submitted 2023-05-16. Revision uploaded 2023-06-16. Final acceptance 2023-06-16.](Article submitted 2023-05-16. Revision uploaded 2023-06-16. Final acceptance 2023-06-16.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n184 International Journal of Emerging Technologies in Learning (iJET) iJET | Vol. 18 No. 17 (2023)  \nDesign of a Machine Learning Model to Predict Student Attrition  \nImproving student performance is vital and should be the focus of interventions aimed at reducing dropout rates while meeting training and output requirements. Defining the concept of dropout is challenging, and its lack of clarity has been a recurring theme in research [20][25]. Broadly speaking, it refers to instances where a student exits higher education without earning a degree, either voluntarily or due to institutional factors [9]. However, leaving an educational program does not necessarily equate to ending one’s higher education studies altogethe","cbCait0xqycirTso","https://ap.wps.com/l/cbCait0xqycirTso","pdf",574955,1,12,"English","en",105,"# Introduction\n## Dropout as a key educational challenge\n## Definition and factors related to attrition\n## Indicators and early identification approaches","[{\"question\":\"Why is reducing student attrition a priority for universities?\",\"answer\":\"Dropout affects educational quality and creates major economic and social consequences, along with personal ramifications for students.\"},{\"question\":\"How does the paper define student dropout for its study?\",\"answer\":\"Dropouts are students who withdraw from the Business Informatics program without completing their studies.\"},{\"question\":\"What data source and educational context are used to build the prediction model?\",\"answer\":\"The model uses data extracted from the Neptun administration system to predict dropout rates in the Business Informatics BSc course at Budapest Business School’s Faculty of Finance and Accounting.\"}]","Design of a Machine Learning Model to Predict Student Attrition | PDF",1785720603,30,{"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},"design-of-a-machine-learning-model-to-predict-student-attrition","",{"@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/design-of-a-machine-learning-model-to-predict-student-attrition/118849/",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},"Why is reducing student attrition a priority for universities?","Question",{"text":75,"@type":76},"Dropout affects educational quality and creates major economic and social consequences, along with personal ramifications for students.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper define student dropout for its study?",{"text":80,"@type":76},"Dropouts are students who withdraw from the Business Informatics program without completing their studies.",{"name":82,"@type":73,"acceptedAnswer":83},"What data source and educational context are used to build the prediction model?",{"text":84,"@type":76},"The model uses data extracted from the Neptun administration system to predict dropout rates in the Business Informatics BSc course at Budapest Business School’s Faculty of Finance and Accounting.","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"]