Retraction: using Big Data to determine potential dropouts in higher education

dc.creatoramelec, viloria
dc.creatorSenior Naveda, Alexa
dc.creatorAngulo Palma, Hugo Javier
dc.creatorNiebles Núñez, William
dc.creatorNiebles Nuñez, Leonardo David
dc.date2020-12-19T23:12:44Z
dc.date2020-12-19T23:12:44Z
dc.date2020
dc.date.accessioned2023-10-03T19:49:12Z
dc.date.available2023-10-03T19:49:12Z
dc.identifier1742-6588
dc.identifier1742-6596
dc.identifierhttps://hdl.handle.net/11323/7616
dc.identifierdoi:10.1088/1742-6596/1432/1/012106
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9172339
dc.descriptionIn 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceJournal of Physics: Conference Series
dc.sourcehttps://iopscience.iop.org/article/10.1088/1742-6596/1432/1/012077
dc.subjectBig Data
dc.subjectHigher education
dc.subjectDropouts
dc.titleRetraction: using Big Data to determine potential dropouts in higher education
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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