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dc.contributor.authorMillán, Eva
dc.contributor.authorBelmonte-Martinez, Maria Victoria 
dc.contributor.authorJiménez, Guiomar
dc.contributor.authorPérez-de-la-Cruz, José-Luis
dc.date.accessioned2015-07-03T09:55:41Z
dc.date.available2015-07-03T09:55:41Z
dc.date.created2015
dc.date.issued2015-07-03
dc.identifier.urihttp://hdl.handle.net/10630/10012
dc.description.abstractIn the last decade, there has been a growing interest in using Bayesian Networks (BN) in the student modelling problem. This increased interest is probably due to the fact that BNs provide a sound methodology for this difficult task. In order to develop a Bayesian student model, it is necessary to define the structure (nodes and links) and the parameters. Usually the structure can be elicited with the help of human experts (teachers), but the difficulty of the problem of parameter specification is widely recognized in this and other domains. In the work presented here we have performed a set of experiments to compare the performance of two Bayesian Student Models, whose parameters have been specified by experts and learnt from data respectively. Results show that both models are able to provide reasonable estimations for knowledge variables in the student model, in spite of the small size of the dataset available for learning the parameterses_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Teches_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectEnseñanza asistida por ordenadores_ES
dc.subject.otherStudent modelinges_ES
dc.subject.otherBayesian networkses_ES
dc.subject.otherMachine learninges_ES
dc.titleLearning Bayesian Networks for Student Modelinges_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational ConferenceINTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN EDUCATION 2015es_ES
dc.relation.eventplaceMadrides_ES
dc.relation.eventdate22 Junio 2015es_ES
dc.identifier.orcidhttp://orcid.org/0000-0001-9178-7600es_ES
dc.cclicenseby-nc-ndes_ES


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