For week 3 of cfhe12 analysis I thought I’d turn back to the Twitter data. I’m currently trying to prepare a Shuttleworth Fellowship application which has got me thinking more about the general premise of cMOOCs that “knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks” (from week 1 of cck11).
The aspect, which features in my Shuttleworth application, is providing mechanisms that aggregate data from distributed sub-networks which then can be processed to produce actionable insights to tutors or participants. The process I plan to adopted is to look at the data using heavyweight tools, like NodeXL, or just applying a bit of curiosity (this person has stopped tweeting, why? etc), and then converting some of these patterns into very lightweight applications or views to remove the complexity and highlight key parts of the data.
Some examples for you:
Tweets from CFHE12 are being collected in this Google Spreadsheet. As part of this template there are a number of summary views, one of these being a breakdown of individual participant activity. As part of this sparklines are used to display someone’s twitter activity. Looking at gsiemens you can see there is steady activity posting 45 tweets tagged #cfhe12. Towards the bottom of the graph is ViplavBaxi, who after initial high activity is no longer contributing to the hashtag. So what has happened to ViplavBaxi? There are a number of possible answers but let me highlight a couple which also highlights the limitation of the technique:
- they have lost interest in the course ot time commitments prevent them from contributing (high drop outs aren’t unexpected in MOOCs)
- no longer using #cfhe12 hashtag – the archive is only of #cfhe12 so if the have joined a sub community communicating without the hashtag it’s not recorded
- found a different communication channel – this technique is only looking at Twitter activity, the person may have moved to another network channel like the discussion forum
Another interesting activity summary is for dieGoerelebt. They are one of the top 5 contributors in terms number of tweets, but recently their activity has trailed off. You can also see the ‘@s’ column, which is the number of times they’ve been mentioned in tweets is one of the lowest. Is the decline in activity a result of the lack of engagement?
The next question that springs to my mind is what did these people say. Within the spreadsheet it’s easy to filter what they said. To let you see too I’ve got this simple web interface primed with filtered tweets (I modified an existing tool I’ve developed to do this – unfortunately I’ve never documented it, but as I use it more and more I must get around to it):
From visual inspection dieGoerelebt had a high proportion of retweets. This is confirmed when I added a percentage of tweets that are retweets.
Something I noted in the filtered view for a persons tweets was that a lot of the context is lost (I can see they are @replying to someone, but I don’t know what they said.
To help with this I started looking at modifying the twitter questions filter I built to enable a view of the conversation.
This is a start, but as I noted when I published the question filter clicking through messages like the one showed below reveal there is more of the conversation that is missing.
So again I start exploring some ideas that branch off into many more avenues to follow. One thought is that the micro analysis of tweets might not my beneficial or practical, and given the issues with extracting a full conversation from Twitter a macro view might be better. Providing a summary of overall activity and the mode in which Twitter is being by people may be of the most use to tutors and participants to identify people they might want to connect with. As always your thoughts are greatly appreciated.
In this post I’ve taken an ego-centric approach contributions. In the next couple of days I’ll share an ego-centric approach to community connections.