The purpose of the research presented in this dissertation is to improve virtual reality (VR) training systems by enhancing their understanding of users. While the field of intelligent tutoring systems (ITS) has seen value in this approach, much research into making use of biometrics to improve user understanding and subsequently training, relies on specialized hardware. Through the presented research, I show that with machine learning (ML), the VR system itself can serve as that specialized hardware for VR training systems. I begin by discussing my explorations into using an ecologically valid, specialized training simulation as a testbed to predict knowledge acquisition by users unfamiliar with the task being trained. Then I look at predicting the cognitive and psychomotor outcomes retained after a one week period. Next I describe our work towards using ML models to predict the transfer of skills from a non-specialized VR assembly training environment to the real-world, based on recorded tracking data. I continue by examining the identifiability of participants in the specialized training task, allowing us to better understand the associated privacy concerns and how the representation of the data can affect identifiability. By using the same tasks separated temporally by a week, we expand our understanding of the diminishing identifiability of user's movements. Finally, I make use of the assembly training environment to explore the feasibility of across-task identifiability, by making use of two different tasks with the same context.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2415 |
| Date | 01 January 2022 |
| Creators | Moore, Alec |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
Page generated in 0.0018 seconds