Investigating immersion in relation to science learning in location-based augmented reality settings
Date Issued
2017
Author(s)
Advisor
Abstract
Immersion, which can be defined as a multi-level continuum of cognitive and emotional involvement, has been argued to facilitate science learning in technology-rich environments. Nonetheless, empirical evidence is often contradictory; this may be partially attributed to the effects of students' individual differences and cognitive load. To-date, there is scant research investigating the relationship between immersion and science learning, while accounting for the potential effects of cognitive load and students' individual differences. The overarching research goal of this research was to explore the impact of immersion on students' conceptual learning in environmental science in the context of augmented reality (AR) settings.
This goal was addressed through a combinatio η of studies, using mixed methods. The studies culminated with the empirical investigation of a proposed cognitive model of immersion in AR setting with 135 10th graders. The proposed cognitive model of immersion acknowledges the potential effects of domain-specific motivation, cognitive motivation and cognitive load on high school students' immersion. To investigate this model three methodological challenges needed to be first addressed. First, an AR development platform was designed, to allow the development of a location-aware AR app in Greek. Second, the Augmented Reality Immersion (ART) questionnaire was developed to measure students' immersion in location-based AR settings. Third, the Need for Cognition Scale - Short Form (NfC-SF GR) questionnaire was adapted, thus ensuring a reliable measurement of high school students' cognitive motivation.
Statistical analyses, which included pre- and post-test comparisons, correlations, multiple regressions and cluster analyses, contributed to the model's validation and provided empirical substantiation for two claims: Immersion is positively predicted by domain-specific motivation and cognitive motivation, but negatively predicted by experienced cognitive load. In turn, learning gains are dependent on the level of immersion that students achieve. This work contributes to theory development (through the validation of the cognitive model of immersion); methodology (through the validation of the ARI and NfC-SF GR questionnaires); and design (through the development of the TraceReaders AR platform).
This goal was addressed through a combinatio η of studies, using mixed methods. The studies culminated with the empirical investigation of a proposed cognitive model of immersion in AR setting with 135 10th graders. The proposed cognitive model of immersion acknowledges the potential effects of domain-specific motivation, cognitive motivation and cognitive load on high school students' immersion. To investigate this model three methodological challenges needed to be first addressed. First, an AR development platform was designed, to allow the development of a location-aware AR app in Greek. Second, the Augmented Reality Immersion (ART) questionnaire was developed to measure students' immersion in location-based AR settings. Third, the Need for Cognition Scale - Short Form (NfC-SF GR) questionnaire was adapted, thus ensuring a reliable measurement of high school students' cognitive motivation.
Statistical analyses, which included pre- and post-test comparisons, correlations, multiple regressions and cluster analyses, contributed to the model's validation and provided empirical substantiation for two claims: Immersion is positively predicted by domain-specific motivation and cognitive motivation, but negatively predicted by experienced cognitive load. In turn, learning gains are dependent on the level of immersion that students achieve. This work contributes to theory development (through the validation of the cognitive model of immersion); methodology (through the validation of the ARI and NfC-SF GR questionnaires); and design (through the development of the TraceReaders AR platform).
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