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Can I read Level of Immersion Affects Spatial Learning in Virtual Environments: Results of a Three-condition Within-subjects Study with Long Intersession Intervals on EtoBox?
Level of Immersion Affects Spatial Learning in Virtual Environments: Results of a Three-condition Within-subjects Study with Long Intersession Intervals by Kimberly A. Pollard; Ashley H. Oiknine; Benjamin T. Files; Anne M. Sinatra; Debbie Patton; Mark Ericson; Jerald Thomas; Peter Khooshabeh is a Computer Science article available to read on EtoBox.
What is Level of Immersion Affects Spatial Learning in Virtual Environments: Results of a Three-condition Within-subjects Study with Long Intersession Intervals about?
Virtual reality and immersive technologies are used in a variety of learning and training applications. However, higher levels of immersion do not always improve learning. The mixed results in the literature may partly arise from the use of betweensubjects designs, insufficient time intervals between sessions in within-subjects designs, and/or overreliance on binary comparisons of immersion levels. Our study examined the influence of three levels of audiovisual immersive technology on spatial learning in virtual environments, using a within-subjects design with long intersession intervals. Performance on object recognition and discrimination was improved in the highest immersion condition, whereas performance on directional bearings showed a U-shaped relationship with level of immersion. Examination of our data suggests that these results likely would not have been found had we used a between-subjects design or a binary comparison, thus demonstrating the value of our approach. Results suggest that different levels of immersion may be better suited to more or less cognitively complex types of spatial learning. We discuss challenges and opportunities for future work.
Who reads Level of Immersion Affects Spatial Learning in Virtual Environments: Results of a Three-condition Within-subjects Study with Long Intersession Intervals?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Kimberly A. Pollard; Ashley H. Oiknine; Benjamin T. Files; Anne M. Sinatra; Debbie Patton; Mark Ericson; Jerald Thomas; Peter Khooshabeh
- Publisher
- Springer-Verlag; Springer London; Springer; Springer Science and Business Media LLC (ISSN 1359-4338)
- Published
- 2020
- Language
- EN
- Field
- Computer Science (Physical Sciences)