Adaptive eLearning for grid computing
Citation:
Kathryn Cassidy, 'Adaptive eLearning for grid computing', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012, pp 276Download Item:
Cassidy, Kathryn_TCD-SCSS-PHD-2012-08.pdf (PDF) 2.692Mb
Abstract:
Adaptive 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.
Author: Cassidy, Kathryn
Advisor:
Coghlan, BrianQualification name:
Doctor of Philosophy (Ph.D.)Publisher:
Trinity College (Dublin, Ireland). School of Computer Science & StatisticsNote:
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