Beyond Klout and PLNs: Towards an Understanding and Application of Network Theory to Education
The foundation of social network theory is the primacy of the relationship and that the ties one has in a network determine the resources to which one has access. These ties represent a complex system of opportunities and constraints. (Daly, 259)
When educators concern themselves with bogus metrics of influence on Twitter and use made-up acronyms like PLN to refer simply to their followers on Twitter, they do a great disservice to the power and wonderful complexity of networks, both face-to-face and online. Modern applications of social network theory, and especially social network analysis, allow us to describe and examine networks in great detail and in ways that are meaningful for individual and organizational learning.
With social network analysis, we can generate metrics or indicators such as density, closeness, centrality and betweenness that describe the shape of a network and the positions and roles individuals play within a network. Thus, rather than crude measures of influence, SNA generates real measures of positionality and importance while also painting useful pictures of networks.
Social network analysis has tremendous potential for examining important education policy-related questions including, but not nearly limited to:
To whom do educators turn for vital work-related information? With whom do educators collaborate regarding instructional issues? How do educators understand the roles of various individuals within a learning organization? How is leadership distributed within a schooing organization?
Come to this session to see the power of social network analsyis for educational improvement.
This session is a hands-on and minds-on workshop. Participants will be asked to bring their laptops loaded with social network analysis software: NodeXL and/or UCINet along with the graphing software Gephi. After a quick introduction to key concepts and metrics associated with social network theory, participants will have an opportunity to play and tinker with data and SNA software to learn how they can make social network analysis work towards improvement in their own learning organizations.
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