A Data-driven Approach to Identify Feedback Elements for Effective VR Training

Abstract

Over the past decade Virtual reality (VR) technology has become more readily available. Although VR still suffers from inducing simulation sickness and having cumbersome controllers, industrial companies have embraced the technology as an alternative to overly complex and expensive simulators. A primary pedagogical consideration for effective VR training is feedback. While research has proven the positive training impact of feedback over having no feedback, very few studies explore techniques to help pinpoint the exact training outcomes that require feedback in a VR training simulator. This study illustrates how collecting human interaction metrics data from a VR fire extinguisher training application without a feedback mechanism can identify the areas where feedback for effective training is most required. We conducted a descriptive comparison study with 36 participants. We collected and analysed the participants’ VR interaction data across nine validated metrics and compared the results to their practical fire extinguishing evaluation on the same metrics. Our results show that the interaction data from the VR application presents curious behaviour regarding the distance participants kept between themselves and the fire, and their regard for wind direction when approaching the fire. The same elements surfaced in the practical evaluation. We conclude that collecting and analysing metrics from a VR training application is a suitable technique for identifying training aspects or outcomes that require a feedback mechanism.