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CrowdXR - Pitfalls and Potentials of Experiments with Remote Participants
20th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) ; : 450-459, 2021.
Article in English | Web of Science | ID: covidwho-1735816
ABSTRACT
Although the COVID-19 pandemic has made the need for remote data collection more apparent than ever, progress has been slow in the virtual reality (VR) research community, and little is known about the quality of the data acquired from crowdsourced participants who own a head-mounted display (HMD), which we call crowdXR. To investigate this problem, we report on a VR spatial cognition experiment that was conducted both in-lab and out-of-lab. The in-lab study was administered as a traditional experiment with undergraduate students and dedicated VR equipment. The out-of-lab study was carried out remotely by recruiting HMD owners from VR-related research mailing lists, VR subreddits in Reddit, and crowdsourcing platforms. Demographic comparisons show that our out-of-lab sample was older, included more males, and had a higher sense of direction than our in-lab sample. The results of the involved spatial memory tasks indicate that the reliability of the data from out-of-lab participants was as good as or better than their in-lab counterparts. Additionally, the data for testing our research hypotheses were comparable between in- and out-of-lab studies. We conclude that crowdsourcing is a feasible and effective alternative to the use of university participant pools for collecting survey and performance data for VR research, despite potential design issues that may affect the generalizability of study results. We discuss the implications and future directions of running VR studies outside the laboratory and provide a set of practical recommendations.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 20th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 20th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) Year: 2021 Document Type: Article