abstract: The science, technology, engineering, and math (STEM) education community is interested in using virtual reality (VR) to help students learn STEM knowledge. Prior research also provided evidence that VR learning can increase students’ motivation and learning achievement. However, it was not clear whether the effect of VR on learning was partly from sensory novelty and whether the effectiveness was sustainable. This study was to satisfy the concern on the sustainability of VR STEM learning in instruction and address the research gaps in exploring the effect of VR on a STEM learning experience with a consideration of novelty.
The study used a mixed-methods experimental design and involved a three-session VR STEM learning intervention. The quantitative data was collected through the intervention by survey questionnaire, session quiz, and pre- and post-tests, while the interviews were taken after the intervention. The structural equation modeling method was used to explore the relationships among factors in the VR learning experience. Longitudinal quantitative comparisons were conducted with the multiple imputation method. Its purpose was to evaluate the changing magnitude of factors across sessions. After quantitative analysis, interview transcripts were analyzed. They were used to triangulate or provide context for understanding of quantitative results.
The results showed that motivation and engagement play a critical mediation role in an effective VR learning experience. While individuals’ psychological responses and motivation may significantly increase in a VR learning experience for novelty, the novelty effect may not steeply decrease when individuals are becoming familiar with the novelty. This phenomenon is more observable in a VR condition having a high degree of immersion and embodiment. In addition, novelty does not necessarily increase learning achievement. The increase of learning achievement is more dependent on a match between the learning content and the learning method. The embodied learning method is appropriate for instructing difficult knowledge and spatial knowledge. Reserving enough time for reflection is important to deep learning in a VR environment. / Dissertation/Thesis / Doctoral Dissertation Engineering Education Systems and Design 2020
| Identifer | oai:union.ndltd.org:asu.edu/item:57391 |
| Date | January 2020 |
| Contributors | huang, wen (Author), Roscoe, Rod (Advisor), Johnson, Mina (Committee member), Craig, Scotty (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 252 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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