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In a previous post, I explored different layouts and performed in details the SNA analysis methods on the blog and Twitter networks for week12 from the Connectivism and Connective Knowledge 2011 course (CCK11) into Gephi.

It’s now time to explore different layouts for the representation of a small network (e.g., Fruchterman Reingold and Yinfan Hu) and experiment with their configuration parameters.

A few weeks ago I posted some measurements commonly seen in social network studies and now let’s play with it!

Layout Algorithms

Figures 1 and 2 show a graph of what might be a small social network.

Fig 1. Applying the Fruchterman Reingold algorithm.

Fig 1. Applying the Fruchterman Reingold algorithm.

 

 

 

 

 

 

 

 

 

 

 

Fig 2. Applying the Yifan Hu algorithm

Fig 2. Applying the Yifan Hu algorithm

 

 

 

 

 

 

 

 

 

 

 

Calculating the network properties Social

SNA draws on concepts from graph theory and structural theory to evaluate network properties such as density, diameter and centralities calculations (Dawson, Tan, and McWilliam, 2011).

  • Diameter : the length of the longest path through the network between any pair of two nodes in the social network.

Diameter = 5.

  • Density: the number of existing connections and the possible connections in the graph.

Density = 0.108.

  • Degree Centrality: the total number of social ties a node has.

From the figures 3, 4 and 5 we can see that Emma, Jill and Shane are the students (nodes) that have the highest number of connections in the network. They have six individual connections. Based on this, they are quite central in most of the potential conversations.

Fig 3. Degree distribution

Fig 3. Degree distribution

 

 

 

 

 

 

 

 

Fig 4. Degree Centrality. Applying nodes size = degree and nodes label = label

Fig 4. Degree Centrality. Applying nodes size = degree and nodes label = label

 

 

 

 

 

 

 

 

 

 

 

 

Fig 5. Degree Centrality. Applying nodes size = degree and nodes label = degree

Fig 5. Degree Centrality. Applying nodes size = degree and nodes label = degree

 

 

 

 

 

 

 

 

 

 

 

 

  • In-degree Centrality: the number of edges coming in. In other words, it indicates popularity or prestige that an individual has in the community. It’s possible to note from Figure 6 the number of other students that are, for example, seeking Jill’s help.
Fig 6. In-degree Centrality. Applying nodes size = In-degree, nodes label = label and nodes color = in-degree

Fig 6. In-degree Centrality. Applying nodes size = In-degree, nodes label = label and nodes color = in-degree

 

 

 

 

 

 

 

 

 

 

 

 

  • Out-degree Centrality: the number of edges leading out. In other words, it indicates gregariousness about an individual. Clearly,  Emma and Bob  influence the greatest number of other students. They have direct influence over 4 students.
Fig 7. Out-degree Centrality. Applying nodes size = Out-degree, nodes label = label and nodes color = out-degree

Fig 7. Out-degree Centrality. Applying nodes size = Out-degree, nodes label = label and nodes color = out-degree

 

 

 

 

 

 

 

 

 

 

 

 

  • Betweenness Centrality: the ease of connection with any other node in the network. If Allen or Liz are removed from network, the entire connection would be completely collapsed with the rest of the community, and so you will notice separated subgroups. It plays an important role which is called “Network Broker”.
Fig 8. Betweenness Centrality. Applying nodes size = betweenness, nodes label = label and nodes color = betweenness

Fig 8. Betweenness Centrality. Applying nodes size = betweenness, nodes label = label and nodes color = betweenness

 

 

 

 

 

 

 

 

 

 

 

 

Network Modularity and Community Identification

It’s a cluster detection algorithm. Students of the same cluster are colored with the same color.

Fig 9. Modularity statistic. Applying nodes color = modularity class

Fig 9. Modularity statistic. Applying nodes color = modularity class

 

 

 

 

 

 

 

 

 

 

 

 

References

Dawson, S., Tan, J. P., McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technolog, 27(6), 924-942.

Additional resources

Hirst, T. (2010, April 16). Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/04/16/getting-started-with-gephi-network-visualisation-app-my-facebook-network-part-i/

Hirst, T. (2010, April 23). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters I, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/04/23/getting-started-with-gephi-network-visualisation-app-%E2%80%93-my-facebook-network-part-ii-basic-filters/

Hirst, T. (2010, May 10). Getting Started With Gephi Network Visualisation App – My Facebook Network, Part III: Ego Filters and Simple Network Stats, Retrieved October 18, 2014, from http://blog.ouseful.info/2010/05/10/getting-started-with-gephi-network-visualisation-app-%E2%80%93-my-facebook-network-part-iii-ego-filters-and-simple-network-stats/

This is my 3rd course/MOOC, and this course is way ahead of anything else I’ve seen. Data, Analytics, and Learning provides “An introduction to the logic and methods of analysis of data to improve teaching and learning”. The most surprising to me is its non-linear structure and the several social tools involved.

You will experiment multiple learning pathways/dual layer MOOC. The instructors referred to this experiment as a metaphor. First of all, you basically have two choices, the “red pill” and the “blue pill”. The blue pill is what you are the most familiar with: it is the typical classroom environment, where you will be guided through a linear path of material, such as videos and assignments.

On the other hand, if you choose the red pill then you are about to choose a totally different way of learning (yeah, give it a shot!). You will be self-guided and responsible to connect with other learners to work on problems and assignments. Learners are encouraged to interact through various social tools (e.g. Blogs, Facebook, G+, Twitter, edX Forum) and share their artifacts. These artifacts can be videos, blog posts, graphics, images, or any other resources that you can share.

There are several new tools in DALMOOC, including Prosolo, Bazaar, Quick Helper, Visual Syllabus, Assignment Bank, and so on. Sometimes I feel overwhelmed but it has been a good experience anyway.

I created a ProSolo profile (it represents my online identity for DALMOOC). ProSolo is a social learning platform connected to edX. My profile includes my personal data, my social media and my geographic location. Moreover, it points out my geographic neighbors and I can buddy up with them. ProSolo provides learners with the ability to share knowledge, expertise, form groups, develop their competencies, work on assignments and peer assessment

Another interesting aspect is that it tracks my content published in the web and it integrates the social media. For example, this blog post will be tracked and thereafter available for peer assessment. If you are a twitter user then you can share the content using #DALMOOC tag.

There’s also another social learning tool named “Bazaar”. This is a collaborative activity that will allow you to connect with other learners – in real time – to complete exercises related to course topics. Once you log in, you will enter a lobby program and a virtual instructor will guide you and your partner throughout the discussion. In my case, I had the opportunity to learn more about the topic with my partner due to our level of expertise.

At first I found Prosolo difficult to manage, but now you can find some video tutorials to help users how to use ProSolo.

By the way, I’m taking the red pill of #DALMOOC. Wait for next posts. Meanwhile, let’s check out the red pill effect.

Ready for social learning ? Go for it!

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