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dc.contributor.advisorCoghlan, Brian
dc.contributor.authorCassidy, Kathryn
dc.date.accessioned2016-11-07T14:03:31Z
dc.date.available2016-11-07T14:03:31Z
dc.date.issued2012
dc.identifier.citationKathryn Cassidy, 'Adaptive eLearning for grid computing', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012, pp 276
dc.identifier.otherTHESIS 9622
dc.identifier.urihttp://hdl.handle.net/2262/77605
dc.description.abstractAdaptive eLearning appears well suited to Grid education because of the distributed and heterogeneous nature of grid users and their asynchronous training demand. This thesis explores the application of adaptive eLearning techniques to grid computing education. It looks at integration of an adaptive eLearning application, and associated courses, with a training infrastructure which provides a fair replica of a production Grid. It explores the potential for extending this integration to the practical components of online courses, allowing learners to access practical environments within the eLearning application and enabling the adaptive eLearning tool to capture relevant information about learners’ conduct of practical exercises within this environment, so-called ”XeL” for eXecutable eLearning, the core hypothesis of this thesis. Captured information can then be used by the adaptive eLearning application for further course personalisation, potentially enhancing the learning experience. A prototype implementation of an infrastructure and tools to explore these ideas is described. The prototype validated that the proposed infrastructure is feasible. Several experiments were conducted to gather data about the efficacy or otherwise of the approach and the results of these experiments are presented and some tentative conclusions drawn as to the benefits of the XeL approach. Learners found XeL both intuitive and useful, and there is some evidence for increased learning, and enhanced suitability for remote learners when using XeL. Future areas of research are proposed in order to further elaborate on the ideas presented here.
dc.format1 volume
dc.language.isoen
dc.publisherTrinity College (Dublin, Ireland). School of Computer Science & Statistics
dc.relation.isversionofhttp://stella.catalogue.tcd.ie/iii/encore/record/C__Rb15124914
dc.subjectComputer Science, Ph.D.
dc.subjectPh.D. Trinity College Dublin
dc.titleAdaptive eLearning for grid computing
dc.typethesis
dc.type.supercollectionrefereed_publications
dc.type.supercollectionthesis_dissertations
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (Ph.D.)
dc.rights.ecaccessrightsopenAccess
dc.format.extentpaginationpp 276
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