Analysis of data mining techniques for constructing a predictive model for academic performance

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dc.contributor.authorMerchán Rubiano, Sandra Milena
dc.contributor.authorDuarte Garcia, Jorge Alberto
dc.contributor.orcidMerchán Rubiano, Sandra Milena [0000-0003-3142-1417]
dc.date.accessioned2020-07-16T15:31:23Z
dc.date.available2020-07-16T15:31:23Z
dc.description.abstractenglishThis paper presents and analyzes the experience of applying certain data mining methods and techniques on 932 Systems Engineering students data, from El Bosque University in Bogotá, Colombia; effort which has been pursued in order to construct a predictive model for students academic performance. Previous works were reviewed, related with predictive model construction within academic environments using decision trees, artificial neural networks and other classification techniques. As an iterative discovery and learning process, the experience is analyzed according to the results obtained in each of the process iterations. Each obtained result is evaluated regarding the results that are expected, the datas input and output characterization, what theory dictates and the pertinence of the model obtained in terms of prediction accuracy. Said pertinence is evaluated taking into account particular details about the population studied, and the specific needs manifested by the institution, such as the accompaniment of students along their learning process, and the taking of timely decisions in order to prevent academic risk and desertion. Lastly, some recommendations and thoughts are laid out for the future development of this work, and for other researchers working on similar studies.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/TLA.2016.7555255
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.issn1548-0992
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlhttps://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/3527
dc.language.isoeng
dc.publisherIEEEspa
dc.publisher.journalIEEE Latin America transactionsspa
dc.relation.ispartofseriesIEEE Latin America transactions, 1548-0992, Vol. 14, Nro. 6, 2016, p. 2783-2788spa
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/7555255
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf2
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightsAcceso abierto
dc.rights.creativecommons2016-06
dc.rights.localAcceso abiertospa
dc.subject.ieeeCoursewarespa
dc.subject.ieeeEducationspa
dc.subject.ieeeArtificial intelligencespa
dc.subject.keywordsData miningspa
dc.subject.keywordsPredictive modelingspa
dc.subject.keywordsAcademic risk preventionspa
dc.titleAnalysis of data mining techniques for constructing a predictive model for academic performancespa
dc.title.translatedAnalysis of data mining techniques for constructing a predictive model for academic performancespa
dc.type.coarhttps://purl.org/coar/resource_type/c_6501
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículo de revista

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