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A massive data processing approach for effective trustworthiness in online learning groups

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ACCEPTED_COPY_A massive data processing approach for effective trustworthiness in online learning groups_CCPE-2014_MiguelEtAl_camera.pdf (1,502Mb)
 
10.1002/cpe.3396
 
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hdl:2117/79974

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Miguel, Jorge
Caballé Llobet, Santiago
Xhafa Xhafa, FatosMés informacióMés informacióMés informació
Prieto, Josep
Document typeArticle
Defense date2015-06-10
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
This 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.
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This 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
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. 
URIhttp://hdl.handle.net/2117/79974
DOI10.1002/cpe.3396
ISSN1532-0626
Publisher versionhttp://onlinelibrary.wiley.com/doi/10.1002/cpe.3396/full
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