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dc.creatorViloria Silva, Amelec Jesus
dc.creatorCastro Sarmiento, Alex
dc.creatorMaría Santodomingo, Nicolás
dc.creatorMaría Santodomingo, Nicolas Elias
dc.creatorMárquez Blanco, Norka
dc.creatorCadavid Basto, Wilmer
dc.creatorHernández P, Hugo
dc.creatorNavarro Beltrán, Jorge
dc.creatorde la Hoz Hernández, Juan
dc.creatorRomero, Ligia
dc.date.accessioned2019-08-31T03:07:51Z
dc.date.available2019-08-31T03:07:51Z
dc.date.issued2019-07-26
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/11323/5225
dc.description.abstract. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university dropout.spa
dc.description.sponsorshipUniversidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria Latinoamericana.spa
dc.language.isoengspa
dc.publisherCommunications in Computer and Information Sciencespa
dc.relation.ispartofhttps://doi.org/10.1007/978-981-32-9563-6_5spa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectKnowledge extraction processspa
dc.subjectTutoringspa
dc.subjectDecision makingspa
dc.subjectData miningspa
dc.titleData mining to identify risk factors associated with university students dropoutspa
dc.typePreprintspa
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