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dc.contributor.authorGil González, Ana Belén 
dc.contributor.authorPrieta Pintado, Fernando de la 
dc.contributor.authorPaz Santana, Juan Francisco de 
dc.contributor.authorMartín, Beatriz
dc.contributor.authorRodríguez González, Sara 
dc.date.accessioned2017-09-06T09:14:06Z
dc.date.available2017-09-06T09:14:06Z
dc.date.issued2012
dc.identifier.citationAdvances in Intelligent and Soft Computing International Workshop on Evidence-Based Technology Enhanced Learning. pp. 115-123.
dc.identifier.issnhttp://id.crossref.org/isbn/978-3-642-28800-5(Print)/ http://id.crossref.org/isbn/978-3-642-28801-2(Online)
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-642-28801-2_14
dc.identifier.urihttp://hdl.handle.net/10366/134841
dc.description.abstractA major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users. This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR). AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is a multi-agent architecture that can search and integrate heterogeneous educational content within the CBR model proposes. The recommendation model and the technologies reported in this research applied to educational content are an example of the potential for personalizing labeled educational content recovered from heterogeneous environments. 
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleCBR Proposal for Personalizing Educational Content
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported