An adaptive model for digital game based learning

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dc.contributor.advisor Murphy, Orla en
dc.contributor.advisor Cosgrave, Michael en Cunningham, Larkin 2019-09-19T11:57:26Z 2019-09-19T11:57:26Z 2019 2019
dc.identifier.citation Cunningham, L. 2019. An adaptive model for digital game based learning. PhD Thesis, University College Cork. en
dc.identifier.endpage 377 en
dc.description.abstract Digital 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2019, Larkin Cunningham. en
dc.rights.uri en
dc.subject Game based learning en
dc.subject Serious games en
dc.subject Virtual reality en
dc.subject Learning analytics en
dc.subject Univeral design en
dc.subject Education en
dc.subject Agile software development en
dc.title An adaptive model for digital game based learning en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en Not applicable en
dc.description.version Accepted Version
dc.description.status Not peer reviewed en Digital Arts and Humanities en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
dc.internal.conferring Autumn 2019 en

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