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dc.contributor.authorMiguel Moneo, Jorge-
dc.contributor.authorCaballé Llobet, Santi-
dc.contributor.authorXhafa, Fatos-
dc.contributor.authorPrieto Blázquez, Josep-
dc.contributor.otherUniversitat Politècnica de Catalunya-
dc.contributor.otherUniversitat Oberta de Catalunya (UOC)-
dc.date.accessioned2019-04-02T13:44:33Z-
dc.date.available2019-04-02T13:44:33Z-
dc.date.issued2014-09-26-
dc.identifier.citationMiguel, J., Caballé, S., Xhafa, F. & Prieto, J. (2015). A massive data processing approach for effective trustworthiness in online learning groups. Concurrency Computation, 27(8), 1988-2003. doi: 10.1002/cpe.3396-
dc.identifier.issn1532-0626MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/92789-
dc.description.abstractThis paper proposes a trustworthiness-based approach for the design of secure learning activities in online learning groups. Although computer-supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks that limit its potential. Among these limitations, we investigate on information security vulnerabilities in learning activities, which may be developed in online collaborative learning contexts. Although security advanced methodologies and technologies are deployed in learning management systems, many security vulnerabilities are still not satisfactorily solved. To overcome these deficiencies, we first propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness approach. However, as learners' trustworthiness analysis involves large amount of data generated along learning activities, processing this information is computationally costly, especially if required in real time. As the main contribution of this paper, we eventually propose a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherConcurrency Computation-
dc.relation.ispartofConcurrency Computation, 2015, 27(8)-
dc.relation.urihttps://upcommons.upc.edu/handle/2117/79974-
dc.rights(c) Author/s & (c) Journal-
dc.subjecttrustworthinessen
dc.subjecte-learning activitiesen
dc.subjectcomputer-supported collaborative learningen
dc.subjectinformation securityen
dc.subjectparallel processingen
dc.subjectlog filesen
dc.subjectmassive data processingen
dc.subjectHadoopen
dc.subjectMapReduceen
dc.subjectfiabilidades
dc.subjectactividades de e-learninges
dc.subjectaprendizaje colaborativo asistido por computadoraes
dc.subjectseguridad de la informaciónes
dc.subjectprocesamiento en paraleloes
dc.subjectarchivos de registroes
dc.subjectprocesamiento masivo de datoses
dc.subjectHadoopes
dc.subjectMapReducees
dc.subjectfiabilitatca
dc.subjectactivitats d'aprenentatge virtualca
dc.subjectaprenentatge col·laboratiu assistit amb l'ordinadorca
dc.subjectseguretat de la informacióca
dc.subjectprocessament paral·lelca
dc.subjectfitxers de registreca
dc.subjectprocessament massiu de dadesca
dc.subjectHadoopca
dc.subjectMapReduceca
dc.subject.lcshWeb-based instructionen
dc.titleA massive data processing approach for effective trustworthiness in online learning groups-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/submittedVersion-
dc.subject.lemacEnsenyament virtualca
dc.subject.lcshesEnseñanza virtuales
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.doi10.1002/cpe.3396-
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