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dc.contributor.authorPEREZ GUTIERREZ, BORIS RAINIERO
dc.contributor.authorCastellanos, Camilo
dc.contributor.authorCorreal, Dario
dc.date.accessioned2021-12-02T14:56:38Z
dc.date.available2021-12-02T14:56:38Z
dc.date.issued2018-12-14
dc.identifier.urihttp://repositorio.ufps.edu.co/handle/ufps/1650
dc.description.abstractThe prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression, Naive Bayes and Random Forest results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.eng
dc.format.extent15 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherIEEE Colombian Conference on Applications in Computational Intelligencespa
dc.relation.ispartofIEEE Colombian Conference on Applications in Computational Intelligence
dc.rights© Springer Nature Switzerland AG 2018eng
dc.sourcehttps://link.springer.com/chapter/10.1007/978-3-030-03023-0_10spa
dc.titlePredicting student drop-out rates using data mining techniques: A case studyeng
dc.typeArtículo de revistaspa
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dc.identifier.doihttps://doi.org/10.1007/978-3-030-03023-0_10
dc.publisher.placeColombiaspa
dc.relation.citationeditionVol.833 (2018)spa
dc.relation.citationendpage125spa
dc.relation.citationissue(2018)spa
dc.relation.citationstartpage111spa
dc.relation.citationvolume833spa
dc.relation.citesPérez, B., Castellanos, C., & Correal, D. (2018, May). Predicting student drop-out rates using data mining techniques: A case study. In IEEE Colombian Conference on Applications in Computational Intelligence (pp. 111-125). Springer, Cham.
dc.relation.ispartofjournalIEEE Colombian Conference on Applications in Computational Intelligencespa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.proposalStudent drop outeng
dc.subject.proposalStudent desertion predictioneng
dc.subject.proposalEducational data miningeng
dc.subject.proposalPrediction modelseng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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