Doctor of Philosophy (PhD)
The traditional passive industrial energy assessment training methods included low direct involvement of students, and they lacked an engaging learning environment. Since the main task for energy assessment is visual inspection and data collection, traditional passive training methods were not able to provide hands-on experience for the students. An active training method was needed to make the current industrial energy assessment training more efficient and effective, a method which provides the opportunity for students to have more interaction with the training environment. In this study, an interactive industrial energy assessment learning platform using 360ᵒ virtual reality contents was developed. The real-world industrial facility was reproduced virtually, and scenes were generated for the training platform. Then, the industrial energy assessment educational content was produced inside the simulated platform and finally, the virtual measurement tools were generated to provide an interactive environment for learners to practice hands-on tasks inside the VR-supported training platform. Validity of the simulated environment in increasing the awareness of trainees related to industrial energy assessment was studied using two indicators: construct validity and psychological fidelity. Construct validity explores the ability of the training platform to distinguish the real-world experts from novices. Psychological fidelity examines the mental effort, gaze behavior or neural activity of learners when doing a task inside the training platform. The results showed that the simulated training platform was able to not only provide an accurate representation of the real-world tasks needed to learn industrial energy assessment (construct validity) but also to replicate the perceptual-cognitive demands of the real-world tasks that are used for learning industrial energy assessment (psychological fidelity).
Ghanbari, Laleh, "Interactive Industrial Energy Assessment Learning Platform Using 360-Degree Virtual Reality Contents" (2022). LSU Doctoral Dissertations. 6000.
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