Although it’s unlikely to be phrased in precisely this way, in this third post on potential viva questions, I’m going to pick up the baton offered in my last post and discuss ‘making the cut.’ This references the work of Karen Barad amongst others, and the need to make choices about what should and shouldn’t be included in our research studies. That might be during field work when deciding which actors to follow and which to leave behind. It could be during data analysis where some data are attended to more closely than others, or in presenting some findings whilst discarding others. In ‘After Method,’ Law (2004) describes method assemblage as
the process of crafting and enacting the necessary boundaries between presence, manifest absence and Otherness.
Within my study then there will be that which is present (whether through the choices I made during observation, analysis or in the thesis), that which is manifestly absent (which I was aware of, chose not to include, but didn’t hide), and that which I Othered by being unaware of the part it played.
In the field
Whilst conducting observations, there were some things which drew my flânographic gaze and some which didn’t, so the first cut I made was at that point. There were also some people I interviewed and thousands of others I didn’t, some blog posts I interacted with and many I didn’t. How did I make those choices?
The first thing to say was that I had some overarching principles I could apply. Firstly I always tried to keep my research questions in mind and remember that I was looking for how teachers’ professional learning practices were manifest and how Twitter participated in that. Of course it becomes necessary to remain aware that I have a preconceived idea of what professional learning practices might constitute, based on the hinterlands on which I draw. What I consider to be PD might not necessarily be the same as someone else, so I would often take cues from what participants mentioned. For example, a tweet or blog post might mention PD and Twitter, so I’d follow that trail for a while, perhaps join a #chat, open a dialogue on a post or invite someone to be interviewed. Sometimes participants who were aware of my research would point me to tweets or posts or other people they thought might inform my study. Occasionally, interviewees, came forward and offered to participate.
Through the data
There was a lot of data and I mean a lot! I must confess to gathering more data than I needed, probably due to inexperience and lacking the confidence to making the cut too early. This meant that in some cases there was repetition. In a previous post I provided an outline of the analytical process I undertook, but didn’t mention why my attention was drawn to certain data and not others. Once again my research questions provided a touchstone, albeit with the same proviso as before and not being blinkered to emergent phenomena. Accompanied by my conceptual travelling companions and ‘thinking with theory’ as I wove my way through the data, I became particularly attuned to instances which illustrated or were informed by assemblage, multiplicity and fluidity. Rather than trying to reduce the volume of information by seeking commonality through themes, I was looking for what was novel, interesting, unique and rich – ‘gathering anecdotes.’ The task then became one of trying to somehow draw those emerging threads together to provide a coherent narrative.
In the writing
The first draft of my thesis contained around 95k words, some 15k over the advised limit. Given how much of that came through the ‘Gatherings,’ it was inevitable that some of that got cut. Once again it became a matter whether the words enlivened or enriched the emerging stories in a coherent fashion. I dropped almost half a chapter where I was discussing the characteristics that might predispose teachers to using Twitter for professional learning. I chose to drop it, not because it wasn’t interesting, but pragmatically, something had to go and this lacked coherence with the sociomaterial sensibility upon which I drew. A ‘disposition’ suggests anteriority, which simply doesn’t fit within a worldview in which ‘things’ are enacted into existence. There were other shorter sections and paragraphs which were dropped because they rubbed against, rather than contributing towards, the arc of the Gathering. I’m not entirely comfortable with that. Perhaps they belonged and I simply lacked the skill or experience to build them in coherently.
In all cases I have to remain conscious of the fact that it’s not only me who’s exercising choice over what data are made present or absent, but so too the applications with which I worked. Twitter decides what data I see either in my main timeline, through Tweetdeck or Twitter searches. Since most blog posts I settled on came through these routes, Twitter influenced them too. When gathering corpora of multiple tweets either through Treeverse or TAGS, I rely on their algorithms for both the tweets which are collected (or rejected). Similarly when producing automated visualisations; I have little say in the way the data are displayed and my analysis will undoubtedly be influenced by that.
In the end, this was one set of stories among many that I could have told. There were no right and wrong choices to be made when deciding which actors to follow, which data to analyse or which stories to tell. I simply tried ask whether this helps to answer how teachers’ learning practices are manifest and how Twitter contributes to that.
Law, J. (2004). After method: Mess in social science research. Routledge.