Making 'us' visible: Using membership categorisation analysis to explore young people's accomplishment of collective identity‐in‐interaction in relation to digital technology.
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| Title: | Making 'us' visible: Using membership categorisation analysis to explore young people's accomplishment of collective identity‐in‐interaction in relation to digital technology. |
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| Authors: | McLay, Katherine Frances, Renshaw, Peter David |
| Source: | British Educational Research Journal. Feb2020, Vol. 46 Issue 1, p44-57. 14p. |
| Subjects: | Membership, Group identity, Digital technology, Student participation, Educational technology research |
| Abstract: | This article examines young people's sense of self and collective identity in relation to their use of specific digital tools available at their school. We use membership categorisation analysis (MCA) to explore how a group of young people produce a collective identity‐in‐interaction as captured in concrete relational speech acts. Fine‐grained MCA analysis of group interview talk reveals participant students operating as a collectivity to accomplish a sense of shared identity in relation to the iPad. This focus on the ways in which young people's identities are intertwined with digital technology distinguishes this article from the technicist and operational perspectives that dominate the field of educational technology research and demonstrates MCA's potential for illuminating the relationship that young people have with technology. The article contributes to a growing body of research that engages with more nuanced ways of understanding contemporary, technology‐mediated learning as a process of producing not only knowledge and skills, but also selfhood—both private and shared. [ABSTRACT FROM AUTHOR] |
| Copyright of British Educational Research Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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