Week 3 was a useful refresher about SNA for me (after all I “successfully” completed a whole MOOC about it before;). The succinct presentation of the basic network structure and analysis was probably more useful than delving into mathematical details behind each of them – and certainly allowing to see applicability and meaning of such measures IRL settings.
Summarising it AND reflecting on it all in 300 words – that’s a bit of a challenge! But here goes it (word count starts now):
SNA aims to study relationships (e.g. communication, advice networks, hindrance) among actors that interact with one another in social networks via a combination of graphical representation and parameters describing the network structure. Dragan emphasised that, currently, SNA is the most popular form of LA.
Each actor is represented as a node (apex) and existing relationships with others as lines (edges/ties/acs/links). Relationships can be undirected or directional, and can be weighted (e.g. by volume of exchanges).
Connectivity of the entire network (ease of communication of information within the network):
- Diameter – the longest distance between the pair of nodes within the network
- Density – actual number of connections/potential number of connections
Centrality measures (identifying importance of individual actors within the network):
- Degree centrality = overall number of actor’s connections
- For directional networks:
- In-degree centrality aka popularity or prestige
- Out-degree centrality aka gregariousness
- Betweeness centrality aka network brokers
- Closeness centrality – shortest distance to anybody in the network for individual node
Analysis aimed at identification of communities within the overall social network – or groups of actors with higher density within them vs between them – and critical connectors between those communities.
Benefits of SNA
Social learning via discussion, collaboration and cooperation is currently a very popular paradigm in education and it has indeed been demonstrated to contribute to development of higher order skills such as critical thinking. Networking itself is considered an essential skill for a contemporary workplace (and they certainly want their potential employees to be able to demonstrate the ability to do it and be able to capitalise on the employees’ personal learning networks – PLNs). Applying SNA to ubiquitous data created through online interactions among learners and faculty may allow us to understand which types of online interactions are most beneficial for learning. But it should also benefit the learners directly by helping them with documentation and development of their lifelong online professional/personal learning networks.
Applications of SNA
In my experience, the richest source of Learning Analytics data within HE institutions is the institutional VLE. I had worked with distance learning postgraduate students using discussion board tools to complete discussion-based and collaborative learning activities. We provided some advice on how to effectively collaborate within such environments but this was largely based on common sense and personal experience.
SNA of discussion board interactions in correlation with student marks for the project and overall achievement could help provide better a priori advice to students in how to tackle such collaborations. Use of SNAPP tool may be a good start here.
This student cohort had minimal interactions outside the VLE, as they were not meeting f2f and shunned social media hence the data would capture majority of social interactions among the learners. This would not have been the case for the campus-based or younger cohort, where data from other social spaces, e.g. social media, may need to be included. I believe that at most of HE institutions in the UK students do sign off on their data use for improvement of learning which would cover data collected within insitutional VLEs. Even in this case students may perceive such use as invasion of privacy and some educators see it as an unethical grooming of students into surveillance culture. The ethical and legal implications of using data from students’ personal social networks such as Facebook (even when formally used for teaching) are likely to be more complex. The EU data protection legislation makes particularly so.
End of word count. That will be around 600 words folks!
My first impression of SNA application to learning is that this is very much still a work in progress – i.e. SNA is being used for discovery/research rather than as means of routine monitoring and of effective social networking patterns for learning. Perhaps I will be proved wrong over the next week;)