Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7922
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dc.contributor.authorProf. LAU Kung Wong, Nicken_US
dc.date.accessioned2023-06-06T06:16:28Z-
dc.date.available2023-06-06T06:16:28Z-
dc.date.issued2023-
dc.identifier.citationVirtual Reality: the Journal of the Virtual Reality Society, 2023, vol. 27, pp. 2331-2339.en_US
dc.identifier.issn1434-9957-
dc.identifier.issn1359-4338-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7922-
dc.description.abstractThe research is designed to trigger students’ game innovations and facilitate their creative learning process by providing them with a “stimulated” immersive game environment to achieve self-regulated learning practice. This research has tailor-made three situated immersive games for data collection. The Self-regulated Learning model is applied to investigate students’ cognitive, motivational, and emotional aspects of their learning process in these immersive game environments. This study involved a sample of 64 undergraduate students in interactive media design. The three factors and foci in this research are, (1) Cognitive Processing for Creative Thinking, (2) Personal Learning Motivation (PLM), and (3) Environmental Stimulation in Game. The positive results of this research indicated the use of immersive games in game innovation education is promising. The use of situated learning pedagogy provides students with an effective learning process for discovery learning. Students are experiencing “learn-by-doing” and exploring diverse solutions through the experiences gathered from the stimulated immersive environments. The result also provides educators with an alternative pedagogical approach to refining their future curriculums.en_US
dc.language.isoenen_US
dc.relation.ispartofVirtual reality : the journal of the Virtual Reality Societyen_US
dc.titleLearning game innovations in immersive game environments: a factor analytic study of students' learning inventory in virtual realityen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1007/s10055-023-00811-1-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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