evaluating the impact of virtual reality-based training on workers' competences in the mining...
DESCRIPTION
A presentation by SMART Infrastructure Facility Research Director Dr Pascal Perez to the 11th International Multidisciplinary Modeling and Simulation Multiconference (I3M), Bordeaux, September 2014.TRANSCRIPT
Evaluating the impact of
Virtual Reality-based training
on worker’s competences in the
mining industry
Shiva Pedram
Pascal Perez
Stephen Palmisano
September 2014
Actual Training Needs
Constraints of Real world
Training
VR Capabilities
VR Utilisation
FRAMEWORK
METHODOLOGY
Factors Interview with Aim
Actual Training Needs
• Subject matter experts (SMEs), such as team supervisors and mine managers
• Identifying human mistakes in mining environments,
• Identifying potential training needs.
Real-World Constraints
• Subject matter experts (SMEs), such as team supervisors and mine managers
• Constraints associated with real-world training,
• Potential for VR-based training to overcome these limitations.
VR-based Training Capabilities
• VR designers and trainers • Current VR capabilities and limitations,
• Potential upgrading for VR to become more relevant.
VR-based Training Utilisation
• Rescue brigades (trainees) and trainers
• Expectations and responses to VR environments,
• Self-assessment of individual performance
METHODOLOGYPast Current
Training
Sessions
Analysing previous records and data Interviewing past trainees & trainers Interviewing VR designers
Attending training sessions Interviewing and observing trainees Interviewing and observing trainers
Mining
Management
Analysing industry assessment reports Interviewing technical management Interviewing senior management
Interviewing technical management Interviewing senior management
Pre-training Questionnaire
• Professional experience
• Gaming experience
• Individual characteristics (age, motion sickness, anxiety…)
• Expectations from training session
Post-training Questionnaire
• Engagement
• Reality/Presence
• Interest/Enjoyment
• Pressure/stress
• Distraction
• Simulator Sickness
• Perceived Competence
Assessing Training Effectiveness
ANALYSIS
Training
Competition
Study Period
BrigadeIndividual
Characteristics &
History
Performance(Competition)
Competence(Training)
Hidden Markov Model (HMM)
METHODOLOGY
Woonona Newcastle Lithgow Singleton
Mines 7 7 6 6
Brigades 156 144 115 139
Sessions50
(25 with VR)50 (?) 30 (?) 40(?)
Technical support from Mines Rescue Services to access training sessions and records, as well as facilitating contacts with mine managers.Financial support from the Health & Safety Trust to undertake the study across 4 training facilities.
Preliminary Results
Overall, useful and successful training sessions….
Preliminary Results
…Regardless of the degree of consistency with reality!
Action Research
Addressing the lack of task allocation and coordination
RESEARCH OUTCOMES
Outcome 1 – Better TrainingThis study will estimate expected and actual training transfer capacity associated with IVR technology and identify the most efficient training sequences. This will help Mines Rescue to develop better tailored training programs for existing and future rescue brigades.
Outcome 2 – Better TechnologyThis study will provide a better understanding of the gaps between training challenges and simulation capabilities. This study will demonstrate Mines Rescue’s dedication to upmost quality control of its procedures and outcomes. The findings will also provide evidence for investment decisions on training and simulation capacity.
Outcome 3 – Better PeopleThis study will provide quantitative evidence of the improved competences of rescue brigades over time. Finally, the study will provide ample material for Mines Rescue and the coal mining industry to celebrate all the brave individuals who volunteer to the Rescue Brigades and give their time to maintain and improve their competences.
Shiva Pedram [email protected] Perez [email protected] Palmisano [email protected]