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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.authorMartínez-Navarro Á.
dc.contributor.authorVerdú E.
dc.contributor.authorMoreno-Ger P.
dc.date.accessioned2024-12-02T20:15:46Z
dc.date.available2024-12-02T20:15:46Z
dc.date.issued2021
dc.identifier.issn21964963
dc.identifier.urihttps://hdl.handle.net/20.500.14112/28958
dc.description.abstractDigital transformation is enabling institutions to enhance their processes by using data and technology. In education, digital transformation allows improving the learning experience as well as the institution processes. Within education 4.0, artificial intelligence applied to learning analytics is playing a key role for universities, particularly in the dropout issue, especially in STEM with the highest dropout rates. This is particularly relevant in the Latin American Higher Education scope, given the low labour productivity in these countries. In these countries, universities often have more demand than supply, and achieving an adequate balance between admission rates and dropout rates is a key issue. A high dropout rate harms the prestige of the university and damages students who were admitted without being adequate candidates. Understanding why students abandon their studies help to know what a university can do to avoid it. Data mining (DM) techniques can help discover the individual features that influence the dropout. There are different studies proposing models to predict dropout, and most are based on data that are not at the admission stage. We propose an approach that uses DM techniques to predict dropout based on data at the admission stage. We discover factors influencing dropout by a decision tree and association rules. We use a dataset of students of a computer science degree from a University in South America and achieve good performance when predicting dropout. The most attributes influencing dropout are the pre-grade performance in STEM subjects and the location of the city of residence. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.format18
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights.uriAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.sourceLecture Notes in Educational Technology
dc.sourceLect. Notes Educ. Technol.
dc.sourceScopus
dc.titleMining Pre-Grade Academic and Demographic Data to Predict University Dropout
datacite.contributorUniversidad Mariana, Calle 18 No. 34-104, Pasto, Nariño, Colombia
datacite.contributorEdifico Bella Vista del Norte, Calle 11 No. 40-54, Pasto, Nariño, AP301, Colombia
datacite.contributorUniversidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain
datacite.contributorMartínez-Navarro Á., Universidad Mariana, Calle 18 No. 34-104, Pasto, Nariño, Colombia, Edifico Bella Vista del Norte, Calle 11 No. 40-54, Pasto, Nariño, AP301, Colombia
datacite.contributorVerdú E., Universidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain
datacite.contributorMoreno-Ger P., Universidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain
datacite.rightshttp://purl.org/coar/access_right/c_abf2
oaire.resourcetypehttp://purl.org/coar/resource_type/c_3248
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.contributor.contactpersonÁ. Martínez-Navarro
dc.contributor.contactpersonUniversidad Mariana, Pasto, Nariño, Calle 18 No. 34-104, Colombia
dc.contributor.contactpersonemail: amartinez@umariana.edu.co
dc.identifier.doi10.1007/978-981-16-3941-8_11
dc.identifier.instnameUniversidad Mariana
dc.identifier.reponameRepositorio Clara de Asis
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111657353&doi=10.1007%2f978-981-16-3941-8_11&partnerID=40&md5=a5002ca709e8035b07d302a422517ce0
dc.relation.citationendpage215
dc.relation.citationstartpage197
dc.relation.iscitedby3
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsComputer science education
dc.subject.keywordsDecision trees
dc.subject.keywordsDigital transformation
dc.subject.keywordsMachine learning
dc.subject.keywordsPredictive models
dc.type.driverinfo:eu-repo/semantics/bookPart
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.redcolhttp://purl.org/redcol/resource_type/CAP_LIB
dc.type.spaLibro


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