Design and evaluation of a virtual reality training system for new underground rockbolters

"The need for development of better training techniques for underground rock bolters is signified by the fact that a large number of experienced miners (Baby Boomers) will be retiring soon. This means that there will be very little interface time between the new miners and the experienced miners, which could have been used for training the new inexperienced miners. A new generation of workers (Nexters), who have grown up with computers and video games will replace the Baby Boomers, and the most efficient way to introduce them to the safety and health issues of the mining industry is to use a new form of training to take advantage of their technological skills. MinerSIM is an augmented virtual reality system with integrated hypermedia that is suitable for training underground miners in the basics of using a jackleg drill to install rockbolts. The first part of MinerSIM, which is a Web tutorial on underground rockbolting consisting of text material, three dimensional images, animations, and videos was evaluated using qualitative and quantitative methodologies. Usability experiments performed underscore the need to evaluate a new product to ensure that it meets the requirements of users. Statistical results from comparative experiment to investigate the effectiveness of computer based training versus the standard or traditional method indicate that a task must be sufficiently complex in order to render a traditional paper based method less effective than a computer based method. Additionally, participants in the computer based group (n = 25) rated the task more positively than those in the paper based group (n = 25)"--Abstract, page iii.

Name thesis:degree_name

Ph. D. in Mining Engineering

Grantor

Missouri University of Science and Technology

Year dc:date.available

2016

Author dc:creator

• Nutakor, David

• Mining Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2151

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3153