Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?

dc.contributor.author	Rodríguez Muñiz, Luis José
dc.contributor.author	Bernardo Gutiérrez, Ana Belén
dc.contributor.author	Esteban García, María
dc.contributor.author	Díaz Rodríguez, Susana Irene
dc.date.accessioned	2019-11-14T10:21:46Z
dc.date.available	2019-11-14T10:21:46Z
dc.date.issued	2019
dc.identifier.citation	PLoS ONE, 14(6), p. e0218796- (2019); doi:10.1371/journal.pone.0218796
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10651/53049
dc.format.extent	p. e0218796-
dc.language.iso	eng
dc.relation.ispartof	PLoS ONE
dc.rights	©2019 Rodríguez-Muñiz
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.source	Scopus
dc.source.uri	https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85067628161&doi=10.1371%2fjournal.pone.0218796&partnerID=40&md5=d4d7ed2bfc29824022a861c8dc652934
dc.title	Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?
dc.type	journal article
dc.identifier.doi	10.1371/journal.pone.0218796
dc.relation.publisherversion	http://dx.doi.org/10.1371/journal.pone.0218796
dc.rights.accessRights	open access
dc.type.hasVersion	VoR

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