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|Title:||On the Adoption of Social Network Analysis Methods in CSCL Research – A Network Analysis|
Hoppe, H. Ulrich
|Publisher:||Philadelphia, PA: International Society of the Learning Sciences.|
|Citation:||Dado, M., Hecking, T., Bodemer, D., & Hoppe, H. U. (2017). On the Adoption of Social Network Analysis Methods in CSCL Research – A Network Analysis In Smith, B. K., Borge, M., Mercier, E., and Lim, K. Y. (Eds.). (2017). Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, Volume 1. Philadelphia, PA: International Society of the Learning Sciences.|
|Abstract:||Originating from mathematical sociology, social network analysis (SNA) is a method for analyzing and representing relational structures in online communities. SNA applications in learning settings and CSCL scenarios are growing in popularity, which is in-line with new trends in learning analytics. For the CSCL research community, the adoption of SNA techniques as part of the methodological repertoire requires adequate understanding of the core concepts, their potential contributions, and limitations. We started from the hypotheses that (1) most applications of SNA in CSCL research make use of a small set of basic methods; and (2) the discourse related to SNA is partly inadequate or imprecise. To further analyze and corroborate these “issue hypotheses” we have used network analysis techniques in order to reveal relations between SNA measures and specific aspects of CSCL research (activities, contexts, research methods) based on a corpus of 90 published studies. Based on the results we pinpoint specific issues and outline new opportunities.|
|Appears in Collections:||CSCL 2017|
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