Last week’s tweetorial was the first time I participated in a so-called “tweetstorm” on a topic. As I am not really an active Twitter user outside of this course it was a new experience for me. Reflecting on it I’ve noticed that the 140 character limit per tweet has very interesting and real consequences for a discussion and my own participation within it. Obviously, the limit causes people to express their opinions in very brief statements which can leave room for interpretation. To counteract the limitations one can keep sending out tweets to get one’s message across – in my mind a rather inefficient way compared to other mediums such as blog posts. The consequence is that it will likely clog up the twitter feed and potentially drown out other voices. Another way is to think hard about how to best come up with an answer that is deliberately vague and open to interpretation yet still conveys meaning. I wasn’t too comfortable with overshadowing the conversation with too many messages (and I unfortunately couldn’t participate on Friday) but I tried to come up with messages that were appropriate for the medium.
The Tweet Archivist and Keyhole analyses of our tweetorial show a discrepancy in the number of posts people were willing to share. While I was on the lower end with 6 tweets, the top tweeter by far was my colleague PJ with 59 contributions.
As I was unavailable for most of Friday I unfortunately missed the peak of the discussion.
Once I came back, I was feeling overwhelmed by the fragmented nature of tweets and retweets on a variety of topics. People hadn’t just stuck to the questions Sian and Jeremy had prepared but instead switched to other topics as well, such as the topic of learning to code – as seen in this keyword cloud:
Looking at the data sets that these analytical tools generate I can’t help but question their value in terms of how they can help us learn. The word cloud is the best example of how data needs to be interpreted to create information, let alone to generate knowledge or wisdom. Atomising the conversation and displaying the frequency distribution of words visually might give an outsider a quick overview of the topics discussed but there doesn’t seem to promote much learning. Perhaps analytic algorithms will in the future be able to extract the meaning of such conversations and assist the learner in getting the gist of it but in their current state these analytical tools don’t seem to offer much value in terms of content.
There is, however, an interesting observation that we can glean from the analysis on a meta perspective: the social dynamics of the conversation.
Compared to the tweet count from earlier we can see that Sian, even though she only posted half as many tweets as PJ was being addressed the most. As she is the tutor in this course this does not seem all too surprising but considering the scope that learning analytics could be scaled up to, identifying such influencers might turn out to be valuable meta data.
Overall I have to say that the algorithmic snapshots of our tweetorial have not given me any particularly valuable insight that could significantly support me in learning from the tweets. Perhaps more context aware algorithms will one day be able to better distill the meaning of such conversations. For now, the meta data, particularly regarding the social structure of the conversations, are the most useful parts generated by these analytic tools.
