An adaptive model for digital game based learning

dc.check.embargoformatEmbargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo)en
dc.check.infoNot applicableen
dc.check.opt-outNoen
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dc.contributor.advisorMurphy, Orlaen
dc.contributor.advisorCosgrave, Michaelen
dc.contributor.authorCunningham, Larkin
dc.date.accessioned2019-09-19T11:57:26Z
dc.date.available2019-09-19T11:57:26Z
dc.date.issued2019
dc.date.submitted2019
dc.description.abstractDigital Game-based Learning (DGBL) has the potential to be a more effective means of instruction than traditional methods. However meta-analyses of studies on the effectiveness of DGBL have yielded mixed results. One of the challenges faced in the design and development of effective and motivating DGBL is the integration of learning and gameplay. A game that is effective at learning transfer, yet is no fun to play, is not going to engage learners for very long. This served as the motivation to devise a systematic approach to the design, development and evaluation of effective and engaging DGBL. A comprehensive literature review examined: how games can be made engaging and how the mechanics of learning can be mapped to the mechanics of gameplay; how learning can be designed to be universal to all; how learning analytics can empower learners and educators; and how an agile approach to the development of instructional materials leads to continuous improvement. These and other considerations led to the development of the Adaptive Model for Digital Game Based Learning (AMDGBL). To test how successful the model would be in developing effective, motivating and universal DGBL, a Virtual Reality (VR) game that teaches graph theory was designed, built and evaluated using the AMDGBL. An accompanying platform featuring an Application Programming Interface (API) for storing learner interaction data and a web-based learning analytics dashboard (LAD) were developed. A mixed methods approach was taken for a study of learners (N=20) who playtested the game and viewed visualizations in the dashboard. Observational and think aloud notes were recorded as they played and gameplay data was stored via the API. The participants also filled out a questionnaire. The notes taken were thematically analysed, and the gameplay data and questionnaire responses were statistically analysed. Triangulation of data improved confidence in findings and yielded new insights. The learner study became a case study for a second, qualitative study of DGBL practitioners (N=12). The VR game was demonstrated and a series of visualizations presented to the participants. They then completed a questionnaire featuring open questions about: the need for the model; the benefits of VR; and the embedding of learning analytics, universal design for learning, iteration with formative evaluation, and triangulation at the heart of the model. The responses were thematically analysed. The results of both studies supported the following assertions: that the AMDGBL would allow for iterative improvement of a DGBL prototype; that employing the AMDGBL would lead to an effective DGBL solution; that the inclusion of UDL would lead to a more universally-designed game; that the LAD would help learners with executive functions; and that VR would foster learner autonomy.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCunningham, L. 2019. An adaptive model for digital game based learning. PhD Thesis, University College Cork.en
dc.identifier.endpage377en
dc.identifier.urihttps://hdl.handle.net/10468/8578
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2019, Larkin Cunningham.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectGame based learningen
dc.subjectSerious gamesen
dc.subjectVirtual realityen
dc.subjectLearning analyticsen
dc.subjectUniveral designen
dc.subjectEducationen
dc.subjectAgile software developmenten
dc.thesis.opt-outfalse
dc.titleAn adaptive model for digital game based learningen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
ucc.workflow.supervisoro.murphy@ucc.ie
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