Link to the Presentation

You can find the Prezi presentation by Maarten de Laat here.

Webinar outline by Maarten de Laat and Shane Dawson:

During this Webinar we explored Social Network Analysis (SNA) to study social learning and interaction. It was a different webinar than usual because we used an online forum to discuss this topic for over a week, preceded with an uploaded introduction presentation. We followed the format used with the Networked Learning Hot Seats. We hope that this format allowed for a different kind of interaction and engagement.

Based on the dialogue in the forum it is obvious that SNA is becoming a popular topic to research social learning. There are quite a number of master and PhD students who participated and shared some of their questions and research issues. For those who would like to continue the conversation there is a European community of social network researchers being formed as we speak. The first meeting was in Antwerp April 29th on Network for Learning. If you would like to know more about this and upcoming events, please send me an This email address is being protected from spambots. You need JavaScript enabled to view it.. Also there is SoLAR (Society for learning analytics research) where there is a lot of interest in SNA research and the upcoming HICSS minitrack on Social Media & Learning.

During this Webinar week we talked about our research interests and expressed the importance of having a theoretical framework needed to interpret a relational approach to learning. SNA is a very useful and flexible method, and we need to be cautious when interpreting the results. It is relatively easy to misread the data (especially network visualizations) and draw some quick conclusions. Especially in the domain of learning. Learning is not something that can be easily observed or assessed. SNA can help to find patterns of participation and understand how networks change over time and locate. But most of the SNA indicators are developed within different domains. Understanding the flow of information and communication through networks is not the same as trying to explore learning. Learning ties or learning relationships are essentially different kinds of relationships. Density for example is a popular indicator to express the extent of interaction amongst all members in a network. A density of 100% means that all members in a network are connected. But does a higher density imply more learning?

Over the week we concluded that we need a multi-method approach to study the full complexity of learning and that SNA can be one of the useful data sources to triangulate with additional data. A method we often use combines SNA, with content and contextual analysis. This way we can find out who is talking to whom? (SNA), what they are talking about (content analysis) and why they are talking as they do? (situational analysis).

Towards the end of the week we moved into discussing more advanced SNA methods to explore the possibility of longitudinal analyses. This is a very promising research direction since this will help to study network dynamics and student engagement with learning over time. Concepts as ‘legitimate peripheral participation’, expertise development and construction of knowledge can be seen for example through the lens of social participation. This is quite exciting, but these methods are still being developed and rely currently mostly on complete network data sets. A popular method is based on modelling and simulation, but again here we need to be critical for its use within research on learning. The main question is about whether and to what extent we can predict learning and expect learning ties to be developed. But this kind of research will help us to further research the nature and antecedents of learning ties.

We hope this Webinar contributed to our basic understanding of SNA and its promise for researching social learning and interaction.

Also we hope that using a forum for a week may have helped to explore some questions a bit deeper than just a standard Q&A session and even better that it would have helped to get to know other researchers within the NAPLeS network who are doing similar things and have a shared interest.

Thanks,
Shane and Maarten

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