Author
Listed:
- Eleni A. Kyza
(Media, Cognition and Learning Research Group, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus)
- Yiannis Georgiou
(Media, Cognition and Learning Research Group, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus)
- Markos Souropetsis
(Media, Cognition and Learning Research Group, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus)
- Andria Agesilaou
(Media, Cognition and Learning Research Group, Department of Communication and Internet Studies, Cyprus University of Technology, Limassol, Cyprus)
Abstract
Collecting data in a mobile augmented reality (AR) settings is challenging, as participants are dispersed in the physical space and move often; therefore, it is imperative that new techniques are investigated to facilitate richer and more ecologically-valid data collection. This study examined three in vivo techniques for collecting authentic data in mobile, AR learning situations: (a) tablet-based audio recording, (b) students' researcher-led videotaping, and (c) head-mounted wearable cameras. Participants were eighteen 11th grade students, working in pairs. All students completed individual questionnaires examining their perception of the intrusiveness of the data collection technique and participated in interviews about the intrusiveness of technique. Audio and video from students' work was also collected. Findings are used to discuss the advantages and disadvantages of each data collection technique as a method for collecting data in location-aware augmented reality studies.
Suggested Citation
Eleni A. Kyza & Yiannis Georgiou & Markos Souropetsis & Andria Agesilaou, 2019.
"Collecting Ecologically Valid Data in Location-Aware Augmented Reality Settings: A Comparison of Three Data Collection Techniques,"
International Journal of Mobile and Blended Learning (IJMBL), IGI Global Scientific Publishing, vol. 11(2), pages 78-95, April.
Handle:
RePEc:igg:jmbl00:v:11:y:2019:i:2:p:78-95
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