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dc.creatoramelec, viloria
dc.creatorSenior Naveda, Alexa
dc.creatorHernández Palma, Hugo
dc.creatorNiebles Nuñez, William
dc.creatorNiebles Nuñez, Leonardo David
dc.date.accessioned2020-01-30T13:42:58Z
dc.date.available2020-01-30T13:42:58Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/11323/5950
dc.description.abstractIn higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.es_ES
dc.language.isoenges_ES
dc.publisherJournal of Physics: Conference Serieses_ES
dc.relation.ispartof10.1088/1742-6596/1432/1/012077/pdfes_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectBig Dataes_ES
dc.subjectDropoutses_ES
dc.subjectHigher educationes_ES
dc.titleUsing Big Data to determine potential dropouts in higher educationes_ES
dc.typeArticlees_ES
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dcterms.references[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Chames_ES
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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