Automatic detection of learner-style for adaptive eLearning

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dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNot applicableen
dc.check.reasonNo embargo requireden
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dc.contributor.advisorPitt, Ianen
dc.contributor.advisorConnolly, Traceyen
dc.contributor.authorMehigan, Tracey J.
dc.contributor.funderIrish Research Council for Science Engineering and Technologyen
dc.contributor.funderDigital Hub, Irelanden
dc.date.accessioned2014-01-27T15:12:36Z
dc.date.available2014-01-27T15:12:36Z
dc.date.issued2013
dc.date.submitted2013
dc.description.abstractThe 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.sponsorshipDigital Hub, Ireland (William Burgess Bursary)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMehigan, T. J. 2013. Automatic detection of learner-style for adaptive eLearning. PhD Thesis, University College Cork.en
dc.identifier.endpage259
dc.identifier.urihttps://hdl.handle.net/10468/1334
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2013, Tracey J. Mehigan.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjecteLearningen
dc.subjectAdaptive systemsen
dc.subjectLearner-Styleen
dc.subjectBiometricsen
dc.subject.lcshComputer-assisted instructionen
dc.subject.lcshInternet in educationen
dc.thesis.opt-outfalse
dc.titleAutomatic detection of learner-style for adaptive eLearningen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisori.pitt@cs.ucc.ie
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