Twitter Visualisation
A few thoughts on the Twitter visualisation exercise.
Click below to see the visualisation:
Sources:
Gašević, D., Dawson, S. and Siemens, G. (2015), “Let’s not forget: learning analytics are about learning”, TechTrends, Vol. 59 No. 1, pp. 64–71
Williamson, B., Bayne, S., Shay, S. (2020), “The datafication of teaching in Higher Education: critical issues and perspectives”, Teaching in Higher Education, 25:4, 351-365
Gasevic, D., Tsai, Y-S., Dawson, S. & Pardo, A. 2019. How do we start? An Approach to Learning Analytics Adoption in Higher Education, International Journal of Information and Learning Technology.
Knox, J., Williamson, B. & Bayne, S. 2020. Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45
Good to hear your reflections on the visualisations of this data. It is worth remembering that this visualisation is shaped by a the TAGs coding and its limitations. I could load this data in to a different social network analysis software that would then generate a lot more ‘data’ about the Tweetorial and give a range of options of how to visualise it (and, therefore, what aspects get surfaced more strongly. You highlight the very definite teacher presence of Jeremy and Ben which I can contrast with a study on an informal learning event on Twitter where the two main figures appear similarly central to the coherence and linking between the participants. However, one of the key figures achieved this is the same way as Ben and Jeremy – by providing original tweets and substantively replying – while the other key figure had a important influence on the visualisation of the closely knit grouping but achieved this through retweeting and posting almost no substantive tweets of their own. Perhaps this gives an illustration of a contrast between a teaching event and the informal learning community? But the difference in behaviours would only be apparent through other forms of analysis than these visualisations. Similarly, you note that not everyone participated in the tweetorial and that most of the posts came from a smaller minority. Regarding the former some traditions of education would include careful listening and reading to be forms of active participation – so the data measures of participation can be culturally specific as well as missing some learning activities (but your point about a more strategic approach to the course will also be a factor) – while the latter reflects a wider trend in social media where the vast bulk of content (80%+) comes from a small minority of users (2-5%). There are some examples in other programmes in the School of Education that do give marks for ‘participation’ in discussion boards etc.. which requires the sort of monitoring you mention. Predictive analytics is a more tricky proposition. It is worth noting that some of the sort of monitoring you mention (particularly attendance) is monitored through digital means for on-campus students (when we have them on campus of corse).
I know next to nothing about coding and analysis software, so I didn’t know about TAGS coding and its limitations.
Interesting to think about the different roles one can play on Twitter – tweeting original comments or re-tweeting and posting nothing substantively original.I wonder if some people never tweet original comments and whether one could associate this to any particular character traits or ways people process information?
You are absolutely right that some people will be hidden participants in that they are reading and listening, but not actively writing and posting. Interesting that 80%+ of content comes from 2-55% users.
Could you monitor ‘attendance’ by look at Moodle and user logins? Is that relevant in any way for a post graduate course?
The different roles on Twitter are interesting. The person that mainly retweeted still played an important part in linking different clusters of the community together by bringing people into conversations but in other events they did play a more traditional role balancing substantive contributions and retweets. Maybe I just found them on a funny day?
There is a very light touch monitoring on this course for example – at the end of week two, we check that everyone has logged in to Moodle and posted something and if they haven’t done both, we check to see if there are any problems. I can’t say I look at the data collected after that unless there is a student having problems with moodle (very rare).