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A Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environments

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorLeony Arreaga, Derick Antonio
dc.contributor.authorParada Gélvez, Hugo A.
dc.contributor.authorMuñoz Merino, Pedro José
dc.contributor.authorPardo Sánchez, Abelardo
dc.contributor.authorDelgado Kloos, Carlos
dc.date.accessioned2014-04-02T09:21:13Z
dc.date.available2014-04-02T09:21:13Z
dc.date.issued2013-08-01
dc.description.abstractCloud technology has provided a set of tools to learners and tutors to create a virtual personal learning environment. As these tools only support basic tasks, users of learning environments are looking for specialized tools to exploit the uncountable learning elements available on the internet. Thus, one of the most common functionalities in cloud-based learning environments is the recommendation of learning elements and several approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner; and services offering this functionality on cloud environments are scarce. Hence in this paper, we propose a generic cloud-based architecture for a system that recommends learning elements according to the affective state of the learner. Furthermore, we provide the description of some use cases along with the details of the implementation of one of them. We also provide a discussion on the advantages and disadvantages of the proposal.es
dc.description.sponsorshipWork partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project 2009/TIC-1650), and “Consejo Social - Universidad Carlos III de Madrid”.es
dc.description.statusPublicadoes
dc.format.extent18 p.es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of Universal Computer Science (2013), 19 (14), 2075-2092es
dc.identifier.doi10.3217/jucs-019-14-2075
dc.identifier.issn0948-6968 (online)
dc.identifier.issn0948-695X (print)
dc.identifier.publicationfirstpage2075es
dc.identifier.publicationissue14es
dc.identifier.publicationlastpage2092es
dc.identifier.publicationtitleJournal of universal computer sciencees
dc.identifier.publicationvolume19es
dc.identifier.urihttps://hdl.handle.net/10016/18702
dc.identifier.uxxiAR/0000014396
dc.language.isoenges
dc.publisherJ.UCSes
dc.relation.projectIDComunidad de Madrid. S2009/TIC-1650/E-MADRIDes
dc.relation.publisherversionhttp://dx.doi.org/10.3217/jucs-019-14-2075
dc.rights© J.UCSes
dc.rights.accessRightsopen accesses
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherRecommender systemses
dc.subject.otherAffective computinges
dc.subject.otherCloud learning environmentses
dc.subject.otherSoftware architecturees
dc.titleA Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environmentses
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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