Visualizing social learning ties by type and topic: rationale and concept demonstrator

Authors:
Bieke Schreurs;Chris Teplovs;Rebecca Ferguson;Maarten de Laat;Simon Buckingham Shum
Affiliations:
Open Universiteit NL, LooK, DL Heerlen, The Netherlands;University of Windsor, Windsor, Ontario, Canada;The Open University UK, Milton Keynes, UK;Open Universiteit NL, LooK, DL Heerlen, The Netherlands;The Open University UK, Milton Keynes, UK
Venue:
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Year:
2013

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Abstract

Social Learning Analytics (SLA) are designed to support students learning through social networks, and reflective practitioners engage in informal learning through a community of practice. This short paper reports work in progress to develop SLA motivated specifically by Networked Learning Theory, drawing on the related concepts and tools of Social Network Analytics and Social Capital Theory, which provide complementary perspectives onto the structure and content of such networks. We propose that SLA based on these perspectives needs to devise models and visualizations capable of showing not only the usual SNA metrics, but the types of social tie forged between actors, and topic-specific subnetworks. We describe a technical implementation demonstrating this approach, which extends the Network Awareness Tool by automatically populating it with data from a social learning platform SocialLearn. The result is the ability to visualize relationships between people who interact around the same topics.