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?


#1

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?


#2

These macro level properties can be helpful when you’re trying to understand how a network may influence learning, or even use them to compare or evaluate the types of interactions that are happening versus the initial hope, design or intention of an instructor or facilitator had in moving a class onto Twitter. This is especially the case if you can compare measures across networks, so that you can understand if measures are higher or lower than other networks.

For example, Reciprocity can be used to indicate how many participants are having two-way conversations, and how many people are actively contributing to discussion. This can be an indicator of how involved learners are (i.e. number of ‘one-off’ contributions, versus ongoing dialogue), and can also be used as a marker to know where and when further analysis efforts are warranted: One may want to get a better understanding of what topics garner more or less dialogue, in order to provide prompts or scaffolding where needed, or to inform the next iteration of the course design so that it invites as much discussion as possible.

Modularity can be used to understand coherence across a network: is it one large group engaged in a single conversation, attentive to each other? Or, is it a number of different conversations occurring with little overlap? This can be used to see if the class is meeting expectations towards certain goals: If you have a more ‘formal’ class and want a strong sense of “whole” collaboration and a strong sense of community, you’ll expect to see lower modularity. If you are designing activities to support informal learning and want a community with a number of overlapping groups and discussions, you’d want to see higher levels of modulairty, which may indicate that participatns are exposed to diverse sources of information and perspectives, exercising the strength of weak ties within the network.


#3

I have tried the Twitter option in Netlytics. No data on my first one. Viewed the YouTube and it mentioned that only data from a week previously is found. So the second one works ok. But too little data to show very much. What would work over six months or so?

I am just trying things out at small scale.


#4

Good points, Drew! To add to that, centrality can be used to identify who in the learning network has the most influence. Since those who occupy central positions in the social network facilitate knowledge flow, identifying these learners provides insight into the type and nature of information learners are exposed to and if they’re sharing it, the type of information to which they respond positively.

Learners in highly central positions may also be apt models for other students. For example, SNA of the #hcsmca Tweet-chat community showed that the community founder had one of the highest levels of centrality. Through interviews, several community members described how her successful Twitter presence served as a model for how they could be successful Tweeters.