Thanks to networkedlearning opportunities I am able to follow the hot seats, although I don’t have the chance to participate in the conference this year. Special thanks goes to organizers and contributors: @maarten, jeffreykeefer and the other hidden heroes
Also, I would like to thank @gruzd for providing a great tool and exercise in this hot seat. As a PHD candidate using Pajek, it was a pleasure for me to experience Netlytic.
When it comes to the answers of the specific questions, here are my answers:
1-What can we learn about a Twitter-based class and its participants based on the node-level SNA measures such as in-degree and out-degree centrality?
profesortbaker and shellterrell seems to have the highest in-degree values. And, cck11feeds have the highest out-degree values.
2-What can we learn about a Twitter-based class and its participants based on the network-level SNA measures such as density, reciprocity, centralization, modularity?
This is a huge question We are provided with some metrics regarding these in netlytic and it also provides comparison charts. Let’s have a look at the measures.
It is not a very dense network. It has some isolates at the periphery. It shows some reciprocal relationship. Moreover, Netlytic points 5 different clusters and a relatively high modularity score. Of course we can say more. On the other hand, in order to talk more about these concepts, we should know more about the context! Maybe at the begining of the course some people are determined as a group?
3-What we cannot learn by using SNA?
Although we can learn much about the interaction and communication in the networked learning environments with SNA, the question of “what forms a learning tie?” is not answered yet. (see reference) I should also underline the importance of the assumptions when forming a network. Both the SNA related assumptions (how you form the network) and the underlying theoretical assumptions shape the conclusions gathered via SNA. I also think that SNA has some shortcomings when we try to embed the qualitative data within a network.
That’s all for now.
Hope to have a great discussion
Middle East Technical University (METU)
Haythornthwaite, C., & De Laat, M. (2010). Social networks and learning networks: Using social network perspectives to understand social learning. In 7th International Conference on Networked Learning.