Abstract
The purpose of this paper is to explore three computational approaches to ethnographic research within digital learning environments: virtual ethnography; quantitative ethnography; and computational ethnography. Recently, researchers have become more interested in computational ethnographic approaches due to their alignment with the massive datasets currently available in digital archives. Although these approaches have attracted growing attention in recent years, their methodological characteristics (e.g., goals, procedures, and techniques) have yet to be fully explored. In this scoping review, we explore existing and the three emerging computational methods for ethnographic research, along with their applications within digital learning environments. We first conduct a scoping review by sampling and synthesizing 86 articles using bibliometric and thematic analyses. This review is then used to reveal the similarities and differences among the three computationally-enabled ethnographic approaches and engage in a concomitant discussion of digital learning designs. In addition, this study explores implications of this type of research for learning design. This research contributes to a better understanding of the characteristics and applications of computational approaches across various digital learning environments.
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We provide our coding metadata as an online appendix at https://doi.org/10.17605/OSF.IO/WNJ8G
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Seo, J., Moon, J., Choi, G.W. et al. A Scoping Review of Three Computational Approaches to Ethnographic Research in Digital Learning Environments. TechTrends 66, 102–111 (2022). https://doi.org/10.1007/s11528-021-00689-3
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DOI: https://doi.org/10.1007/s11528-021-00689-3