Block: ‘Learning’ with Data / Week 3
This week, I decided to start recording some very simple data. I recorded the general position of my body while I read articles or books (for leisure, work and study) throughout the week, using stick figures as my data points.
Below is my data collection between January 25-29 (including some test runs), a legend, and my totals at the end of the week.
As the week went on, I was tempted to record more data to explain the stick figures I was recording. Very hot weather and busy work days shaped this data, but this isn’t always explained in the data. The temptation to record more data to explain the data on hand is perhaps not an uncommon temptation.
Another frustration I found was that, by adhering to plain stick figures and recording 30-minute blocks of time, my reading body appears to be more static than it is. My data couldn’t show the all the fidgeting and readjusting my body made. I felt like I had chosen a very reductive way of recording the human body – one that doesn’t show my weight, ability, gender or skin. This reduction feels both liberatory and restrictive to me, tensions which are similarly explored in John Phillip Sage’s Data Drag project. While this exercise allows me to choose how I track my body through data, I was still unhappy with the representation I chose. Nevertheless I tried to stick to the original parameters I had set out, of simple stick figures, to sit in that ambivalent feeling.
When it came to visualising the data, I didn’t want to create a static image. So I made a quick flip book animation (below). If I kept recording these stick figures I could create a longer and more engaging animation/visualisation.
Knox et al. suggest that “a substantial interest in new education technology development appears to be towards ‘bodily’ and ’emotional’ data” (2019, p. 42), and in light of this I want to spend the rest of this block continuing to visualise data around the embodied experience of learning. When I think about machine tracking of bodily data in education, I think immediately of proctoring software that tracks eye and body movements for surveillance purposes. Can I record and visualise data about my body that doesn’t replicate this kind of surveillance, but is more in the spirit of Data Drag and a (queer) quantified self? That might require some unlearning for me.
Knox, J, Williamson, B & Bayne, S 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45(1), pp. 1-15.
Sage, J.P. 2018, Data drag, viewed January 30 2021, <https://www.johnphilipsage.com/datadrag.html>.