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Using Social Network Analysis to predict online contributions: The impact of network diversity in cross-cultural collaboration

Mittelmeier, J, Heliot, Y, Rienties, B and Whitelock, D (2016) Using Social Network Analysis to predict online contributions: The impact of network diversity in cross-cultural collaboration In: 8th International ACM Web Science Conference 2016, 2016-05-22 - 2016-05-25, Hannover.

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Abstract

Although collaborative web-based tools are often used in blended environments such as education, little research has analysed the predictive power of face-to-face social connections on measurable user behaviours in online collaboration, particularly in diverse settings. In this paper, we use Social Network Analysis to compare users’ pre-existing social networks with the quantity of their contributions to an online chat-based collaborative activity in a higher education classroom. In addition, we consider whether the amount of diversity present in one’s social network leads to more online contributions in an anonymous cross-cultural collaborative setting. Our findings indicate that pre-existing social connections can predict how much users contribute to online education-related collaborative activities with diverse group members, even more so than academic performance. Furthermore, our findings suggest that future Web Science research should consider how the more traditionally ‘qualitative’ socio-cultural influences affect user participation and use of online collaborative tools.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
AuthorsEmailORCID
Mittelmeier, JUNSPECIFIEDUNSPECIFIED
Heliot, YUNSPECIFIEDUNSPECIFIED
Rienties, BUNSPECIFIEDUNSPECIFIED
Whitelock, DUNSPECIFIEDUNSPECIFIED
Date : 25 May 2016
Identification Number : 10.1145/2908131.2908169
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 10 Jun 2016 12:14
Last Modified : 10 Jun 2016 12:14
URI: http://epubs.surrey.ac.uk/id/eprint/810993

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