Social network analysis for learning and development
In week three of my Learning Analytics course, we’ve been looking at social network analysis using the data visualisation tool Gephi. I’m not going to go into the details of how this takes place (though this tutorial is useful if you’re interested). Instead, I’d like to look at the principles of social network analysis and some of the applications for learning and development.
To get started, a network consists of two core components: nodes and edges.
If you imagine three computers networked together, then the computers are ‘nodes’ and the network cables are ‘edges’. Keep that visualisation in mind, because it can be applied to other types of network as well: buildings are connected by people who travel to and fro; airports are connected by flight routes; and people are connected by emails, tweets and friend requests.
This last network type is the focus of social network analysis. Tools like Node XL can be used to pull data from Twitter or Facebook, then export it to Gephi so that it can be visualised and analysed to reveal all sorts of information.
...social network analysis can help shape your ideas about what’s really going on, but it’s important to gather additional evidence before doing anything too drastic.
For example, who are the the people with a high degree of social connections? Who is relatively isolated? Who is popular (receives a lot of emails) and who is gregarious (sends a lot of emails)?
You might also identify network brokers, people who play a crucial role in joining together different subgroups. Previous analyses have shown that people who adopt this role are often the most creative people in a network. They have access to diverse ideas (or they seek out diverse ideas because they’re creative).
Performing an analysis like this is useful to L&D professionals for a number of reasons: It can be used to assess the extent to which a community is forming between participants on an online programme. Sense of community is a good predictor for students completing a programme, so if social ties are weak then it may be worth facilitating further conversations.
If some participants on your programme are isolated, social network analysis can give you an early warning to help them get more involved. Alternatively, if someone is in a network broker position, you may want to check that they are getting all they need from your programme.
After all, bouncing around different subgroups may be correlated with creativity, but it can also mean that an individual is dissatisfied with their existing connections.
For those who run offline workshops, but are disappointed to see the same faces at each session, social network analysis might be a reassuring exercise. Do your regulars keep information to themselves, or are they the brokers in their own network, disseminating their learning throughout the wider organisation?
Outside of L&D, social network analysis might also be used to assess the structure of your organisation. Who are the people everyone turns to for help? And who are the people that everyone avoids?
As with last week’s blog, however, I’d like to end with a note of caution. If you perform an analysis on email traffic, and find that a manager is rarely in touch with their team, this can be interpreted in a number of ways. Maybe they’re not giving enough direction, but maybe they’ve empowered their team to make decisions or prefer to pick up the phone when there’s a problem.
Which is to say, social network analysis can help shape your ideas about what’s really going on, but it’s important to gather additional evidence before doing anything too drastic.
If you’re interested in social network analysis, it’s worth looking further into the work of Professor Dragan Gasevic and Professor Shane Dawson.
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