An empirical evaluation of clustering processes for early detection of university dropout

An empirical evaluation of clustering processes for early detection of university dropout | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An empirical evaluation of clustering processes for early detection of university dropout Fran Melchor, José M. Conejero, Antonio Jesús Fernández-García, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6146415/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The elevated rates of dropout within academic institutions have prompted the use of Artificial Intelligence (AI) to tackle this issue. These efforts often rely mainly on administrative and academic data, lacking personal information about students. In a previous study, we explored machine learning models to leverage this data and harness their knowledge-extraction capabilities. However, a critical factor, the availability of labeled data, was not addressed. Obtaining these data may be challenging due to their distribution across different systems or the considerable time required to collect them, especially when new degrees are being implemented. The lack of labeled data is a major obstacle for institutions that do not possess them so that they are unable to take advantage of the full potential of AI for their purposes. Clustering algorithms have conventionally been employed to uncover latent patterns within unlabeled data. These unsupervised algorithms may reduce the need for data labeling; nonetheless, it necessitates rigorous validation of the resulting clusters, particularly when dealing with datasets encompassing numerical and categorical attributes. This paper introduces a comparison of various clustering algorithms to discern the most appropriate technique for uncovering the underlying factors contributing to university student attrition, employing unlabeled data. The novelty lies not only in the algorithmic comparison but also in their integration with diverse data preprocessing methodologies, streamlining the selection of the optimal combination including advanced data transformations for the harmonization of numerical and categorical information. It is illustrated through a real-world case utilizing academic data from a Spanish university, providing empirical validation for the proposed methodology. The insights gained can be extrapolated to analogous experiments where social or economic data is scarce, and most of the available attributes are academic in nature. Machine Learning Feature Engineering Not Supervised Models Clustering Models Real Experiences Student Dropout Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6146415","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423872896,"identity":"144987e7-d139-4b06-9eff-1695a14acf77","order_by":0,"name":"Fran Melchor","email":"","orcid":"","institution":"University of Extremadura","correspondingAuthor":false,"prefix":"","firstName":"Fran","middleName":"","lastName":"Melchor","suffix":""},{"id":423872897,"identity":"00058890-f20d-417a-9523-f10980bea283","order_by":1,"name":"José M. 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