Optimising virtual reality training in industry using crowdsourcing

Optimising virtual reality training in industry using crowdsourcing

Proceedings of the 12th Conference on Learning Factories (CLF 2022)

20 Pages Posted: 12 Apr 2022

See all articles by Paul-David Zuercher

Paul-David Zuercher

University of Cambridge

Thomas Bohné

University of Cambridge

Vera Maria Eger

Ludwig Maximilian University of Munich (LMU)

Felix Mueller

RWTH Aachen University

Date Written: April 4, 2022

Abstract

The ability of Immersive Virtual Reality (IVR) to induce any training scenario in a safe and scalable manner makes it a particularly interesting technology for virtual learning factories. However, both an opportunity and a challenge is to empirically test and optimise virtual environments. Conducting scientifically robust in-person experiments is often not feasible using traditional approaches, given limited resources of training providers and near limitless opportunities to design virtual training environments. Distributed crowdsourcing techniques using Desktop Virtual Reality (DVR) with a PC may offer an alternative and more scalable approach to experimentally test and optimise virtual environments. An interesting question is therefore if such approaches using DVR are a suitable alternative to current experimental designs to enable large-scale optimisation in contexts such as virtual learning factories. While crowdsourcing has been validated for its suitability in several research applications and domains, there is limited research available on training and, to the best of our knowledge, no previous research has evaluated the suitability of crowdsourcing to optimise immersive training in industrial or learning factory contexts. With our paper we contribute the first experiment to address this research gap. Our hypothesis is that crowdsourcing is a suitable technique for IVR training optimisation if it yields equivalent results to traditional experimentation at every training optimisation level. To test this hypothesis we designed an industrial learning experiment to evaluate key performance and affective indicators of IVR training at three levels of optimisation. The experiment was conducted using traditional and crowdsourcing techniques. The results show that crowdsourcing can be a suitable alternative to traditional optimisation techniques depending on: (1) the desired operative mental state of the participants, (2) the investigated key performance indicators, and (3) the kind of optimisation performed. We contribute new data allowing important insights and an integrated training evaluation concept which can be applied when doing crowdsourcing studies.

Keywords: Immersive Virtual Reality, Desktop Virtual Reality, Crowdsourcing, Training, Optimisation

JEL Classification: I2, J8

Suggested Citation

Zuercher, Paul-David and Bohné, Thomas Marc and Eger, Vera and Mueller, Felix, Optimising virtual reality training in industry using crowdsourcing (April 4, 2022). Optimising virtual reality training in industry using crowdsourcing, Proceedings of the 12th Conference on Learning Factories (CLF 2022), Available at SSRN: https://ssrn.com/abstract=4075130

Paul-David Zuercher (Contact Author)

University of Cambridge ( email )

Alan Reece Building
17 Charles Babbage Rd,
Cambridge, CB3 0FS
United Kingdom

Thomas Marc Bohné

University of Cambridge

Department of Engineering
17 Charles Babbage Road
Cambridge, CB3 0FS
United Kingdom

HOME PAGE: http://www.ifm.eng.cam.ac.uk/people/tmb35/

Vera Eger

Ludwig Maximilian University of Munich (LMU) ( email )

Munich
Germany

Felix Mueller

RWTH Aachen University ( email )

Templergraben 55
52056 Aachen, 52056
Germany
+491637514567 (Phone)

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
132
Abstract Views
459
Rank
384,538
PlumX Metrics