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dc.contributor.authorMiguel, Jorge
dc.contributor.authorCaballé Llobet, Santiago
dc.contributor.authorXhafa Xhafa, Fatos
dc.contributor.authorPrieto, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2015-11-26T15:10:35Z
dc.date.available2016-06-10T00:30:38Z
dc.date.issued2015-06-10
dc.identifier.citationMiguel, J., Caballé , Santi, Xhafa, F., Prieto, J. A massive data processing approach for effective trustworthiness in online learning groups. "Concurrency and computation. Practice and experience", 10 Juny 2015, vol. 27, núm. 8, p. 1988-2003.
dc.identifier.issn1532-0626
dc.identifier.urihttp://hdl.handle.net/2117/79974
dc.descriptionThis article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html
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.
dc.format.extent16 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Ensenyament i aprenentatge::Metodologies docents::Aprenentatge cooperatiu
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Seguretat informàtica
dc.subject.lcshTeam learning approach in education
dc.subject.lcshComputer-assisted instruction
dc.subject.lcshComputer security
dc.subject.lcshElectronic data processing--Distributed processing
dc.subject.othertrustworthiness
dc.subject.othere-Learning activities
dc.subject.othercomputer-supported collaborative learning
dc.subject.otherinformation security
dc.subject.otherparallel processing
dc.subject.otherlog files
dc.subject.othermassive data processing
dc.subject.otherHadoop
dc.subject.otherMapReduce
dc.subject.otherImplementation
dc.subject.otherlogs
dc.titleA massive data processing approach for effective trustworthiness in online learning groups
dc.typeArticle
dc.subject.lemacAprenentatge--Treball en equip
dc.subject.lemacEnsenyament assistit per ordinador
dc.subject.lemacSeguretat informàtica
dc.subject.lemacProcessament distribuït de dades
dc.identifier.doi10.1002/cpe.3396
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://onlinelibrary.wiley.com/doi/10.1002/cpe.3396/full
dc.rights.accessOpen Access
drac.iddocument15643488
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorMiguel, J.; Caballé, Santi; Xhafa, F.; Prieto, J.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameConcurrency and computation. Practice and experience
upcommons.citation.volume27
upcommons.citation.number8
upcommons.citation.startingPage1988
upcommons.citation.endingPage2003


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