In a previous post, I outlined how creating an image, initially for a competition, also illustrated how that visualisation process often became an analytical (flânalytical?) technique. Having been inspired by the @metropologeny city maps, as I began planning the vis, it always struck me that tweets seemed to naturally fit the mainly rectangular shapes of the buildings on the map. In being drawn towards the tweets however, I wondered about the other data sources which were part of my study, but temporarily parked that aspect until I’d resolved the technical aspects of producing the image. Now, with that task completed and the image submitted for the competition, I now turned back to the other data. How might blog posts or interviews also contribute to the vis?
Before delving into how I moved forward, perhaps it might help to rewind somewhat and look at how the map was built in the first place. This animation shows the different stages
Here at SHU there’s a couple of PhD researcher competitions on at the moment as part of the forthcoming Doctoral Showcase series. There’s the ‘Three Minute Thesis’ heats and local final, but the one that attracted my interest was the ‘SHU Doctoral Research Image Competition 2018.’ I’ve been producing visualisations throughout my study and I had in mind one I wanted to produce, but hadn’t because I knew it would suck up time. The competition provided the final impetus and although I suspect from the information and instructions, the organisers are expecting photographic images, I thought I’d have a shot at pushing the boundaries.
We welcome attention-grabbing images to intrigue, inform or excite a lay/non-specialist research audience about your research. Images may be arresting, beautiful, moving or even amusing but they must relate to your doctoral research project.
Entrants are also allowed 150 words of accompanying text; here are mine:
The flâneur of 19th Century Paris was an observer and chronicler of city life. In exploring the bold claims some teachers make that ‘Twitter is the best PD ever!’, I called on the spirit of the flâneur to guide my ethnographic approach.
One of several methods I employed in the study was participant observation; this image is formed from tweets collected during that process. Each of the districts or ‘quartiers’ contains tweets on one of the emerging themes, each typified by a magnified example.
Since flânerie inspired my approach to observation, analysis of the data, and presentation of the findings, I sought an image which spoke to that activity. Although somewhat playful, creating this image, and other visualisations during the study, was more than simple representation. On each occasion I found the attention to compositional detail which was demanded also yielded additional analytical insights.
Clearly no marks for originality, but there’s my first tweet. Those which followed illustrate that Twitter for me was more about learning with and from other educators. It still is … but I digress. As I’ve been analysing the data from my research, the routes by which people come to Twitter to support their learning are rather different. My tweet above was at 18:33 on the 19th February 2009, and was prompted by a fellow Master’s course member, Geoff, who suggested I might find Twitter interesting. The path for me then began with a course (Technology Enhanced Learning, Innovation and Change), followed by a nudge from someone whose opinion mattered. Can you remember the route by which you came to use Twitter to support your professional learning?Read More »
Having decided to attempt to describe certain phenomena on Twitter as learning assemblage, I now find myself in somewhat of a quandary. Earlier yesterday, whilst teaching a group of undergrad BEd with Science QTS students about circular motion, we were discussing the importance of sketching free-body diagrams to aid understanding and problem solving. So perhaps it’s the scientist in me that generates the proclivity to want to summarise situations by using visualisations of one sort or another. A quick scan through the back catalogue of this blog will reveal many examples, however I now find myself struggling and somewhat dissatisfied.
I’ve recently been drafting vignettes in which I describe groups and activity on Twitter as assemblage, but I feel the need to produce a visualisation which captures a sense of what that is. The problem of course is that I’m trying to render assemblage, a dynamic process, as a static representation. But why should that be a problem? That’s precisely what I’ve been doing when producing physics free-body diagrams isn’t it? Representing a dynamic situation through a static diagram?
During the past few months, I’ve participated in a number of exchanges on Twitter that have been part of my research. Sometimes this has been no more than a couple of tweets back and forth with one other person. At other times it’s been a more extended discussion involving several people; multiple voices, multiple tweets. What I’ve struggled with over the past year or so, is finding a tool which will display the exchange in a way that simplifies reading the thread(s). If you’ve ever tried reading and making sense from a string of replies to a tweet, you’ll know how tricky this can sometimes be.
When there are a number of responses to a tweet, Twitter lists them in chronological order with the most recent at the top. If someone replies to one of those initial responses though, Twitter begins to thread those discussions together by grouping them under one another. So in each group, tweets are arranged chronologically as before, and all groups are arranged chronologically too. Within a group then, things are fine, but it becomes difficult to appreciate the overall timeline, especially if new channels of conversation open up. Here, the vertical, linear display just gets in the way.Read More »
Whilst out for a run this week, I was catching up my podcast listening. On my playlist was Episode 91 of Data Stories in which the creators of RAW were sharing what is, what it does and how it came into being. RAW claims to be ‘The missing link between spreadsheets and data visualization.’ Back when I wrote my research proposal, I thought that social network analysis (SNA) would be one technique I might use to learn more about teacher learning on Twitter. There are a raft of tools that can help with this, which exist on a spectrum from those which rely on having expertise in coding, to those (like TAGS and NodeXL) which are usable by novice like me. In addition to gathering tweets, they often allow you to produce visualisations of the connections between those tweets:
In the preceding post I considered one possible way to visualise a Twitter exchange, but expressed concern that the temporal separation of events had become lost. Seeking to redress this shortfall, I thought that timeline tools might offer a way forward. So finding myself needing to ‘kick the tyres’ of TimelineJS, that’s where I turned. Here the data that you use to compose your timeline is kept in a Google sheet, which means that adjusting or amending your timeline only requires a change to the contents of a spreadsheet cell. It also makes consistency across the elements of the timeline relatively easy by cutting and pasting cell contents. Adding each tweet is no more complex than pasting its url into a cell.
Earlier this week I was engaged in a spatially interesting exchange, in which I was discussing the contents of a blog post with Chris, next to whom I sit here at SHU. The post, authored by Naomi Barnes, had been brought to my attention in a tweet by Aaron, made accessible by the url he had included. Knowing Chris’ interests and current writing, I thought the post might interest him, so I mentioned it … old school … the spoken word. He asked if I would forward the details, which I did by sending an email which contained only the url. Having read the post, he then tweeted his interest and thanked both me and Naomi … which then set in train a flurry of replies, retweets and likes. And one hat-tip.
Following the preceding post, I’ve dug a little deeper into sentiment viz to explore more carefully what it might offer in terms of revealing the emotional components within Twitter and tweets. Like before, I used a chat hashtag as the search term and perhaps unsurprisingly got a similar shaped visualisation which expressed sentiment as generally positive and somewhat relaxed. Probing a little further and clicking on a few individual circles provides the data which located the tweet at that point on the chart. Here we see the overall sentiment rating expressed as ‘v’ for valence (how pleasant) and ‘a’ for arousal (how activated). Then there’s a breakdown of those words which contributed to that sentiment rating, with their individual scores. We therefore have multiple ways we can compare the emotional content of one tweet with another, but can make a judgement whether those ratings make sense – more of that later.
During my pilot studies, a couple of findings suggested areas for further exploration I’d not previously considered. One of these was the degree to which people talking or writing about Twitter seemed to be ‘affected.’ Although it was not a topic I had gone looking for, nor had asked questions about, and although people rarely mentioned it explicitly, the language and terms they used implied some element of emotional response. Before I could take this much further, I needed to return to the literature and see how people have discussed and/or researched the affective side of teacher learning.