Automatic detection of learner-style for adaptive eLearning

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dc.contributor.advisor Pitt, Ian en
dc.contributor.advisor Connolly, Tracey en
dc.contributor.author Mehigan, Tracey J.
dc.date.accessioned 2014-01-27T15:12:36Z
dc.date.available 2014-01-27T15:12:36Z
dc.date.issued 2013
dc.date.submitted 2013
dc.identifier.citation Mehigan, T. J. 2013. Automatic detection of learner-style for adaptive eLearning. PhD Thesis, University College Cork. en
dc.identifier.endpage 259
dc.identifier.uri http://hdl.handle.net/10468/1334
dc.description.abstract The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined. en
dc.description.sponsorship Digital Hub, Ireland (William Burgess Bursary) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2013, Tracey J. Mehigan. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject eLearning en
dc.subject Adaptive systems en
dc.subject Learner-Style en
dc.subject Biometrics en
dc.subject.lcsh Computer-assisted instruction en
dc.subject.lcsh Internet in education en
dc.title Automatic detection of learner-style for adaptive eLearning en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD (Science) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Irish Research Council for Science Engineering and Technology en
dc.contributor.funder Digital Hub, Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Computer Science en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor i.pitt@cs.ucc.ie
dc.internal.conferring Autumn Conferring 2013 en


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© 2013, Tracey J. Mehigan. Except where otherwise noted, this item's license is described as © 2013, Tracey J. Mehigan.
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