Haoqian Chang
Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering
Chang, Haoqian; Pan, Ziqi; Cristea, Alexandra I.
Authors
Ziqi Pan ziqi.pan2@durham.ac.uk
PGR Student Doctor of Philosophy
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Professor
Abstract
Safety education and training are vital in the mining industry. However, traditional training relies on passive modalities, such as lectures, videos and brochures. These suffer from sever limitations - poor reproducibility, inefficient resource utilisation, and a lack of interactive feedback in one-size-fits-all training scenarios. Addressing these challenges, we introduce a novel hybrid approach combining virtual reality (VR) and electroencephalography (EEG) for training in the use of underground self-contained self-rescuers (SCSR). The VR component provides an interactive and immersive training experience, to facilitate a higher level of engagement compared to traditional methods. Initial EEG testing showed that VR training could elevate trainees’ brain activity, which may result in higher ratings and satisfaction. Beyond EEG we use also after-scenario questionnaire (ASQ) and system usability scale (SUS).
Citation
Chang, H., Pan, Z., & Cristea, A. I. (2024, July). Virtual Reality (VR) in Safety Education: A Case Study of Mining Engineering. Presented at AIED 2024: Artificial Intelligence in Education, Recife, Brazil
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | AIED 2024: Artificial Intelligence in Education |
Start Date | Jul 8, 2024 |
End Date | Jul 12, 2024 |
Online Publication Date | Jul 2, 2024 |
Publication Date | Jul 2, 2024 |
Deposit Date | Nov 13, 2024 |
Publisher | Springer Nature |
Peer Reviewed | Peer Reviewed |
Pages | 382-387 |
Series Title | Communications in Computer and Information Science |
Series ISSN | 1865-0929 |
Book Title | Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky |
ISBN | 9783031643118 |
DOI | https://doi.org/10.1007/978-3-031-64312-5_47 |
Public URL | https://durham-repository.worktribe.com/output/3093249 |
You might also like
Editorial: New challenges and future perspectives in cognitive neuroscience
(2024)
Journal Article
Using deep learning to analyze the psychological effects of COVID-19
(2023)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search