Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73956
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dc.contributor.authorTapia, Franciscoen_US
dc.contributor.authorLopez, C. A.en_US
dc.contributor.authorGalan, M. J.en_US
dc.contributor.authorRubio Royo, Enriqueen_US
dc.date.accessioned2020-08-05T09:12:32Z-
dc.date.available2020-08-05T09:12:32Z-
dc.date.issued2008en_US
dc.identifier.issn1868-8799en_US
dc.identifier.otherScopus-
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/73956-
dc.description.abstractIn this paper, a Bayesian-Network-based model is proposed to optimize the Global Adaptive e-Learning Process (GAeLP). This model determines the type of personalization required for a learner according to his or her real needs, in which we have considered both objects and objectives of personalization. Furthermore, cause-andeffect relations among these objects and objectives with the learning phases, the learner, and the Intelligent Tutorial System (ITS) are accomplished. These cause-and-effect relations were coded into a Bayesian Network (BN), such that it involves the entire GAeLP. Four fundamental phases that have a direct effect in the learner's learning process are considered: Learner's previous knowledge Phase, Learner's Progress Knowledge Phase, Learner's /Teacher's Aims and Goals Phase, and Navigation Preferences and Experiences Phase. The efficacy of the Bayesian networks is proven through the first phase, in which learners of different knowledge area were select. The main results in this work are: causal relations among objects and objectives of personalization, knowledge phases, learner and electronic system. Personalization profiles set and their probabilities in the first phase were obtained to diagnose the type of personalization of the learner.en_US
dc.languageengen_US
dc.relation.ispartofInternational Journal of Emerging Technologies in Learningen_US
dc.sourceInternational Journal of Emerging Technologies in Learning [ISSN 1868-8799], v. 3 (2), p. 38-52, (Diciembre 2008)en_US
dc.subject33 Ciencias tecnológicasen_US
dc.subject.otherBayesian Networksen_US
dc.subject.otherE-Learningen_US
dc.subject.otherLearning Metricsen_US
dc.titleBayesian model for optimization adaptive e-learning processen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.scopus84862981345-
dc.identifier.isi000215426300006-
dc.contributor.authorscopusid55266091100-
dc.contributor.authorscopusid57212937730-
dc.contributor.authorscopusid15924912300-
dc.contributor.authorscopusid7101626071-
dc.identifier.eissn1863-0383-
dc.description.lastpage52en_US
dc.identifier.issue2-
dc.description.firstpage38en_US
dc.relation.volume3en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid32087870-
dc.contributor.daisngid12519627-
dc.contributor.daisngid32217737-
dc.contributor.daisngid8386397-
dc.description.numberofpages15en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Tapia, FJ-
dc.contributor.wosstandardWOS:Lopez, CA-
dc.contributor.wosstandardWOS:Galan, MJ-
dc.contributor.wosstandardWOS:Rubio, E-
dc.date.coverdateDiciembre 2008en_US
dc.identifier.ulpgces
dc.description.esciESCI
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IUCTC: Centro de Innovación para la Sociedad de la Información-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameRubio Royo,Enrique-
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