Club Admiralty

v7.2 - moving along, a point increase at a time

Multilitteratus Incognitus

Pondering what to learn next 🤔

DALMOOC episode5: Fun with Gephi

CCK11 Tweet visualization
Alright, after a few days of being sidelined with a seasonal cold, I'm back on #dalmooc.  Still catching up, but I have a feeling I am getting closer to being at the same pace as the rest of the MOOC ;-)  In any case, this is a reflection on week 3 where we started messing around with social network analysis (SNA).  This is cool because it's something that I had started doing on another MOOC on coursera, with Gephi, so it was an opportunity to get back on and messing with the tool.

So, what is SNA?  SNA is the use of network theory to analyze social networks.  Each person in this network is represented by a node (or edge), and nodes can be connected to other nodes with a vertex (or many vertices). These connections can indicate a variety of things (depending on what you are examing), however for my usage in educational contexts I am thinking of vertices as indicators of message flow, who sends messages to whom in a network, and also who refers to whom in a network. I think this latter one is interesting from an academic citation point of view as well.

As was pointed out in week 3, SNA can help discover patterns of interaction in online learning environments. I think that it can also help up discover patterns in physical environments, however this is harder because we don't have big brother watching the physical environments as much as we can collect data about patterns of participation in virtual environments. It's much more labor intensive to keep accurate track in a physical environment.

An interesting application of SNA is its use in understanding learning design (Lockyer et al - in video). We can use SNA to analyze patterns of interaction in courses that we design an implement, thus we can (to some extent) see how our designs are affecting the learner's patterns of participation.  While this is feasible, I think that it's also hard to keep the variables pinned down so that there are no confounding errors. If you've designed an online course (that is NOT self-paced) you can see the same course design taught different ways if you put different faculty in the driver's seat.  As such I think that in studies using SNA to analyze course design (and/or teaching methods) it's important to account for all variables.

Other interesting things from Week 3:

An Instructor-centered network is one where the instructor is central node in network. These are recognized in literature as only leading to lower levels of knowledge construction (see Bloom's taxonomy). Related to this type of network is having one learner have a dominant role in a course, thus the instructor is replaced (or shares the spotlight) with a dominant learner.  This is also not desirable from a pedagogical point of view. One can start with an instructor-centered environment and facilitate the change to a P2P interaction. Students will need scaffolding in order to reach that P2P network.

Sense of community is predictor of success in educational endeavors. A common way of collecting this type of data is questionnaires, and I think that in education this can be both in-class as part of a mid-term temperature check in the course, but also in the final course evaluation.  I am wondering, however, how accurate this self-reporting is. Is this just an affective measure? Or can learners feel like they are lacking a sense of community but in reality have it but not get as much as they feel they need?

Network brokers are nodes that connect two or more communities in a network and have a high degree of centrality.  These network brokers can see information across many different communities, and as such can have access to many different ideas flow through them. Network brokers are associated with high levels of achievement and creativity. So, in an educational setting it's good to be a network broker.

Cross-Class networks are latent ties by attending the same events, so even though I am not connected with many people in #dalmooc (at least on twitter I don't retweet or write to many others in the MOOC - maybe I should...) I am connected to other people through the course hashtag and by attending the same event. In a traditional learning setting this could be likened to participating in a learner community such as UMassID.com (our instructional design community) or the Athabasca University Landing network.

CCK11 Blogs, week 6
Next up, the Gephi portion of this post.  I've been messing around with Gephi data from CCK11. I was quite excited to get my hands on the CCK11 data to mess around with in Gephi until I remembered that I didn't tweet all that much in CCK11...D'oh! I was curious to see where I was  in the network of connections.  Even if I were active I don't think I'd be able to see myself there because the data appears to be anonymized (and rightfully so).

I did run some analysis of the blog connections in CCK11 using Gephi again (part of the data dump available in #dalmooc) and here was a place where I expected to see myself and see who I was connecting to, however, again, the data was anonymized. My question entering into this analysis was more about analyzing my own patterns of interaction.  I was new to MOOCs back in 2011 and CCK11 was the MOOC where I really started learning about connecting with others for academic purposes. Thus, I wanted to see what my initial and developing connected literacies pointed to. Alas, this was not to be :-)


As Dragan mentioned in one of the videos of this week, analytics should be question-driven, not data-driven. Just because you have data, it doesn't mean that you should use it, or that you will find anything meaningful in it.  This was the case with me and this data. There were some interesting visualizations, but I wanted to have a look at the people involved, who connected to whom, and look more at the qualitative connections: what posts, what ideas, what types of know-how got distributed throughout the network and by whom. It's a little hard to do this with anonymized data, so you really need to abstract and think at higher levels when working with this.  If we had data from other MOOCs, this type of anonymized data could be useful to compare patterns of participation of one MOOC to another.

Thus concludes my week 3 experiences.  What were your thoughts?
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