Navigation in large spaces is essential in any environment (both the real world and the virtual world) because one of the human fundamental needs is to know the surrounding environment and to freely navigate within the environment. For successful navigation in large-scale virtual environments (VEs), accurate spatial knowledge is required, especially in training and learning application domains. By acquiring accurate spatial knowledge, people can effectively understand spatial layout and objects in environments. In addition, spatial knowledge acquired from a large- scale VE can effectively be transferred to the real world activities.
Numerous navigation techniques have been proposed to support successful navigation and effective spatial knowledge acquisition in large-scale VEs. Among them, walking-like navigation techniques have been shown to support spatial knowledge acquisition more effectively in large-scale VEs, compared to non-body-based and non-walking-based navigation techniques. However, walking-like navigation techniques in large-scale VEs still have some issues, such as whole-body fatigue, large-controlled-space and specialized system configuration that make the walking-like navigation techniques less convenient, and consequently less commonly used. Due to these issues, convenient non-walking-like navigation techniques are preferred although they are less effective for spatial learning. While most research and development efforts are centered around walking- like navigation techniques, a fresh approach is needed to effectively and conveniently support for human spatial learning.
We propose an action-inspired approach, to design convenient and effective navigation techniques for supporting people to acquire accurate spatial knowledge acquisition or improve spatial learning. The action-inspired approach is based on our insights from learning, neuropsychological and neurophysiological theories. The theories suggest that action and perception are closely related and core elements of learning. Our observations indicated that specific body-parts are not necessarily related to learning.
We identified two types of action-inspired approach, body-turn based and action-transferred. Body- turn based approach keeps body-turn but replaces cyclic leg-movements of original walking action with more convenient control to resolve the issues presented from walking-like navigation techniques. Action-transferred approach addresses the design trade-offs between effectiveness and convenience, the core concept of which is grounded in the motor equivalence theory.
We provided two navigation techniques, body-turn based and action-transferred based ones, and demonstrated the benefits of our approach by evaluating these two navigation techniques for spatial knowledge acquisition in several empirical studies. We also developed our own walking-like navigation technique, Sensor- Fusion Walking-in-Place (SF-WIP) because we needed a reference navigation technique for estimating the effect of the action-transferred navigation technique on spatial knowledge acquisition compared to that of a walking-like navigation technique.
We performed empirical user studies and the experimental results showed that body-turn based navigation technique was more effective for survey knowledge acquisition in a large-scale virtual maze, compared to a wand-joystick based common navigation technique (JS, i.e., non-body-based and non-walking-like navigation technique). However, no significant difference was found for route knowledge acquisition while the SF-WIP was more effective than the JS for both route and survey knowledge acquisition. The results of the SF-WIP were compatible to the results from other studies (using walking-like navigation techniques).
The action-transferred navigation technique, named Finger-Walking-in-Place (FWIP), was more effective for both route and survey knowledge acquisition than the JS in the same large-scale, large-extent and visually impoverished virtual maze. In addition, our empirical studies showed that the SF-WIP and the FWIP are similarly effective for route and survey knowledge acquisition, suggesting that human's spatial learning ability is still supported by the transferred action (FWIP) as much as the original action (SF-WIP).
Since there was no significant difference between FWIP and SF-WIP but the FWIP showed the better effect than the JS on spatial knowledge acquisition, we can infer that our action-transferred approach is useful for designing convenient and effective navigation techniques for spatial learning. Some design implications are discussed, suggesting that our action-transferred approach is not limited to navigation techniques and can be extensively used to design (general) interaction techniques. In particular, action-transferred design can be more effectively used for the users with disabilities (unable to use of a part of the body) or for fatigue/convenience reasons.
Related to our theoretical reasoning, we established another user study to explore if the transferred action is still coupled with the perception that is known as coupled with the original action. Our study results supported that there was a close connection between distance perception and transferred action as literature suggests.
Thus, this dissertation successfully supports our theoretical observations and our action-inspired approach to design of convenient and effective navigation techniques for spatial learning through our empirical studies. Although our conclusion is drawn from the empirical studies using a couple of NavTechs (body-turn and FWIP), and is therefore not the direct evidence at the neural level, it should be notable that our action-inspired design approach for effective spatial learning is strongly supported by the theories that have been demonstrated by a number of studies over time. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/50621 |
| Date | 07 May 2013 |
| Creators | Kim, Ji Sun |
| Contributors | Computer Science, Quek, Francis K. H., |Gracanin, Denis, Bukvic, Ivica Ico, Winchester, Woodrow W., Bowman, Douglas A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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