Competency 4.2: Describe and interpret the results of social network analysis for the study of learning.
I’m going to use the analysis I did by extracting twitter data with hashtag #DALMOOC (explained in my post for competency 3.2) to interpret the results of modularity in the network. The density was 0.05 which means that the network is connected relatively well. There are a few sub-communities in the network, but still the overall network is connected to some extent.
In the modularity report, we can see the formation of 13 communities. There are some disconnected nodes in the network containing lesser people/ single person which form smaller sub-communities. We should try to connect the disconnected nodes in the network to others in the bigger community to ensure good sharing of information.
The giant component algorithm brought 5 main communities which are color-coded. From the centrality measures, we can see that dgasevic plays the role of a network broker by bridging many nodes in the network. The outdegree from that person is also high which means that he is involved in conversations with many people and is willing to help.
I’ve kept this part of “Social network analysis” short and tried to complete it as soon as possible since we are in the last week of the course. I will be doing the final week’s tasks from tomorrow and hopefully I will post a reflection about the entire course soon 🙂