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An investigation into the effectiveness of virtual reality-based learning

Lee, Elinda Ai Lim (2011) An investigation into the effectiveness of virtual reality-based learning. PhD thesis, Murdoch University.

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      Abstract

      This study focused on the effectiveness of using desktop virtual reality (VR) for learning. It addressed the question: Does, and how does, desktop VR influence the cognitive and affective learning outcomes? Cognitive outcome was measured through academic performance whereas affective learning outcomes were measured through perceived learning effectiveness and satisfaction.

      The main aims of this study were thus two-fold. First, it investigated ‘‘Does desktop VR influence the learning outcomes?’’ by comparing a desktop VR-based learning environment and a conventional classroom learning practice, and it further conducted the aptitude-by-treatment interaction research to determine if individual differences interact with different learning environments. Two learners’ aptitudes were studied: spatial ability and learning style. In addition, individual differences were further analyzed for the VR-based learning environment because their influence in desktop VR-based learning has been rarely studied. An evaluation that employed a quasi-experimental design was conducted to investigate the learning effectiveness of desktop VR-based learning, and to investigate the effect of learners’ aptitudes on learning. A total of 370 students, aged between 15 to 17 years old from four randomly selected co-education Malaysian secondary schools participated in this study. The findings of this study have supported the general hypothesis that the VR-based learning environment positively affects the cognitive and affective domains of learners. This study has provided empirical evidence on the merit of using desktop VR for learning. Furthermore, it was found that desktop VR could accommodate learners’ individual differences in terms of learning styles.

      Next, the research focused on the development of a theoretical model of determinants for effective desktop VR-based learning to understand how a desktop VR system is capable of enhancing and improving the quality of student learning, and the types of students that would benefit from this technology. Various relevant constructs and measurement factors were identified to examine how desktop VR enhances the learning outcomes and the hypothesized model was analyzed using structural equation modeling (SEM). By tradition, the practice of applying correlation analysis to data and hypotheses does not reflect the causal relationships between constructs, but SEM produces a highly viable alternative in determining the causal relationships among constructs. This type of analysis is lacking in desktop VR-based learning.

      In the hypothesized model of this study, VR features indirectly influenced the learning outcomes through the mediation of usability (interaction experience) and learning experience. Learning experience which was individually measured by the psychological factors—that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking—took central stage in affecting the learning outcomes. The moderating effects of student characteristics such as spatial ability and learning style were also examined. Moreover, latent mean difference testing in SEM was conducted to determine the influence of student characteristics on the perception of VR features in the desktop VR-based learning environment. The findings have supported the indirect effect of VR features on the learning outcomes, which was mediated by the usability and learning experience. The results show instructional designers and VR developers how to improve the learning effectiveness and further strengthens their desktop VR-based learning implementation. Furthermore, academia can use the findings of this study as a basis to initiate other related studies in the desktop VR-based learning area.

      Publication Type: Thesis (PhD)
      Murdoch Affiliation: School of Information Technology
      Supervisor: Wong, Kevin and Fung, Lance
      URI: http://researchrepository.murdoch.edu.au/id/eprint/4070
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