Influentials

Some time ago I was thinking about how Twitter’s character limitations for tweets imposed brevity and concision on tweet authors. Nonetheless, in spite of those restrictions, when you begin to disassemble a tweet, a rich and complex ensemble is revealed. More recently I’ve been revisiting those actors and trying to think about them in terms of what they ‘do’ when enacting different practices teachers associate with professional development. The thing which most struck me was that different actors play a more or less significant role depending on the practice in which they’re involved. For example, the ‘Follow’ button is clearly significant in the activity teachers call ‘connecting with others,’ but is (arguably) less important when ‘discussing issues.’ I then began to consider how it might be possible to map the significance of different tweet actors as participants in different professional development activities. These ponderings produced the following vis:

https://www.instagram.com/p/BvxDufSlPof/?utm_source=ig_web_copy_link

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Thesis Abstract

“Visual ToC” flickr photo by IaninSheffield https://flickr.com/photos/ianinsheffield/45061354834 shared under a Creative Commons (BY-NC-SA) license [click image for a larger version]

Before I write further posts as part of my viva preparation, I thought it might be helpful to provide a quick overview of what my thesis involves. The sensible way to do that – and the one requiring least effort – is by sharing the …

Abstract

“BEST. PD. EVER!” Some teachers make bold claims for the way that Twitter supports their professional development, yet research into this area is rather limited. This study sought to gain a better understanding of the practices involved and the part that Twitter plays. It uses a sociomaterial sensibility informed by actor-network theory (ANT) to unravel the complex webs of relations which form, break apart and reform when knowledge practices are enacted in the mediated arena of Twitter.

Visualising ‘what I do’ … again!

One of the questions I’ve found the toughest to answer throughout my study is ‘what do you do when you do Twitter?’ Or, more specifically, ‘what does participant observation look like in the context of your research?’ I’ve previously responded at length, and have been trying to capture  a sense of what I do within a single visualisation. I’m still not convinced I’ve quite managed it, but here’s the final version which made it into the thesis:

“What I do when I do Twitter” flickr photo by IaninSheffield https://flickr.com/photos/ianinsheffield/44602792592 shared under a Creative Commons (BY-NC-SA) license

 

Analytical moves

In an earlier post I discussed some of the thinking behind the analytical process I was leaning towards; much of that made it into an earlier draft of the thesis. Feedback from my supervisors pointed out that detail on how I actually proceeded through the analysis was rather thin. Any reader (examiner!) would therefore not be clear about the steps I took … and therefore my thesis fails one of the characteristics I set for its integrity – that of transparency.  I’ll now attempt to set out the analytical moves I made in a little more detail.

Analysis was a multi-stage process, although not one which proceeded linearly from start to finish. Instead it involved a series of back and forth iterations moving between and across the different data sources. As data offer themselves either as words participants deliver during interviews, tweets that appear through observation, or blog posts at the end of hyperlinks, analytical seeds begin to germinate during this period of familiarisation.

Further Flânerie on Twitter

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

“Animated composite” flickr photo by IaninSheffield https://flickr.com/photos/ianinsheffield/41631862981 shared under a Creative Commons (BY-NC-SA) license

Visualising Flânerie on Twitter

“Doctoral Research Image” flickr photo by IaninSheffield https://flickr.com/photos/ianinsheffield/40631136105 shared under a Creative Commons (BY-NC-SA) license

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.

The path to Twitter is paved with …

Writing my first tweet!

— Ian Guest (@IaninSheffield) February 19, 2009

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?

Visualising assemblage

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?

“Marble loop” flickr photo by IaninSheffield https://flickr.com/photos/ianinsheffield/24447353008 shared under a Creative Commons (BY-NC-SA) license

Treeverse: displaying a Twitter conversation … clearly!

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.

More than pretty?

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:

“NodeXL Twitter Network Graphs: CHI2010” flickr photo by Marc_Smith https://flickr.com/photos/marc_smith/4511844243 shared under a Creative Commons (BY) license