Tweetorial: The people behind the numbers

In order to try to provide some order around the disordered elements of the Tweetorial I used a couple of different methods of visualisation, namely TAGS and Storify. Using these two together with the Tweet Archivist data provided by Jeremy I have answered the three questions below.

How has the Twitter archive represented our Tweetorial?

What do these visualisations, summaries and snapshots say about what happened during our Tweetorial, and do they accurately represent the ways you perceived the Tweetorial to unfold, as well as your own contributions?

What might be the educational value or limitations of these kinds of visualisations and summaries, and how do they relate to the ‘learning’ that might have taken place during the ‘Tweetorial’?


This visualisation gave me a deeper sense of the movement within our tutorial. Seeing the animation of how things originated with Jeremy and James and how we then all came in and moved around connecting with each other is fascinating to watch. It was this visualisation that also made me appreciate the strengths of such online networks as it clearly showed our guest lecturer being brought into conversation and moving within the network. No face-to-face experience could parallel that. The TAGS also was perfect for highlighting our connectedness. It clearly showed that each of us had multiple connections with others and that we moved around and within the network and we moved from conversation to conversation.

Snapshot of our network during the two day tutorial

Whilst I have only included the snapshot of the final connections there are many different ways to look and analyse the data in there, such as breaking down into Tweets, Replies and Mentions giving a wider picture of each person’s contribution.


This visualisation is set in time, containing, only outputs from the tutorial. Building it gave me a better all-round human impression with the tutorial seeing everyone’s picture as I built it. Even though I had tried to ‘be on top’ of it during the two days, it was only when I actually went through every single Tweet that I realised that I had missed a few conversations. It also allowed me to add some images for some of the more humorous parts of the conversations, which to me is the human-centred element.

The second thing that I got from this process is that the tutors kicked off new conversations but then seemed to step aside. Does this mean that they thought our contributions were all on track? Would they have intervened if we had veered off course significantly? Is our autonomy an important learning outcome in itself?

The downside of this visualisation, which only allows for chronological order, is that it is very difficult to connect all the different elements of a conversation into a continuous block and is a fantastic archive of the tutorial to refer back to. When building each Tweet sits in its own right and you can’t see which is connected to which. So it is great for getting the big picture but different to follow conversations.

Tweet Archivist

This tool seems to bring some of functionality from each of TAGS and Storify, as the Tweet history is displayed chronologically and the numbers are crunched to the side of them.

All in all the three different sets of data provided me with a historical perspective of the tutorial and a way to bring a bit of order. The numbers still mean a lot less to me than the people behind them however. The archives will provide me with a rich set of links/ideas/readings to follow up on and I did enjoy the whole process of having a conversation stretched over two days as it gave me time to think and follow up on things in between responses. On the other hand it was also quite frantic which was both fun and hard to keep up with.

My final thoughts focus on presence around the numbers. Whilst I felt very present in the tutorial despite being pulled this way and that during the two days by work and life, I was aware of those who were unable to be there, or indeed were there in the background, learning with the rest of us. This is highlighted in the numbers and I am left wondering if the numbers and data sets only augment their ‘unpresence’ and leave a negative impression on them? This has been my nagging worry about analytics over the week, their use must be approached with sensitivity.

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