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.
References
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>.
tmadden says:
Hi Elizabeth,
Like you I also found myself wanting to record more variables, not sure I had enough (and data was getting away from me) or I was missing something interesting. I guess you can see why there is that ambition to capture it all.
Love the flip book movie; can’t wait to see what you come up with next!
January 31, 2021 — 7:31 pm
esmith says:
You’re so right, Tracey. Feeling like I didn’t have enough variables, I subsequently felt a need to overexplain in the text of the blog! It will be interesting to see how we go with this over the semester.
February 2, 2021 — 7:00 am
Jeremy Knox says:
This is a great start to your data visualisation blog Elizabeth. I really like the idea of tracking your body position, which gives such a fresh insight into your week. I think the potential patterns here are really interesting, showing not only your reading time declining across the week (perhaps as one might expect), but also more sitting up, as if you are becoming more engaged! Stick figures were also a great choice, as they are, I suppose, a fairly universal symbol and quickly convey quite a lot of detail.
Your reflections here are excellent too, particularly around how representative these figures are of your actual movement. One might see Monday as a rather horizontal day! And this is also quite interesting in terms of tracking – would this be interpreted as not being very engaged? An old colleague of mine used to say how ridiculous it was to feel guilty, as an academic, about reading a book in your office, as if others might walk past and think that you were not very busy, and your visualisation kind of reminded me of this. Of course, reading lying down is, as far as I’m concerned, a perfectly authentic scholarly activity no matter the weather! However, some of the research and development in the area of facial and body tracking would seem to suggest that there are ‘correct’ ways to engage, for example this paper: https://dl.acm.org/doi/abs/10.1145/3242969.3264986 – this seems very much like the surveillance you also describe.
I also really liked the idea of fidgeting and readjusting being important, but absent here – perhaps an idea for a further visualisation? I also think this idea of a ‘temptation’ to record more, and resisting it, is useful to explore further. I think you’re right that visualisations should be a reduction of complexity, a distillation of activity, so we are always intentionally leaving something out. But we can also experiment with some quite complex visualisations too.
The flip book animation is brilliant too – I wonder if this might be a theme to continue to explore in your blog?
February 2, 2021 — 6:19 am
esmith says:
Thanks for that link, Jeremy! Looks really interesting. Depending on the context, an “engagement detection task” could sound quite threatening!
Thank you also for your thoughts on reduction and complexity. It gives me something to ponder as I plan my next visualisations.
February 2, 2021 — 7:07 am
ailtukhova says:
This is a very fresh topic, Elizabeth! And very well-visualized.
Frankly, I was a bit surprised to see that you did a lot of reading in a lying position. I personally get very sleepy when doing it. Moreover, I’ve never tried reading in a cross-legged way either. I found it very useful that you described the circumstances that shaped your data. As a rule, we can only see a graph/dashboard that is open to subjective interpretation…
February 2, 2021 — 12:48 pm
esmith says:
Thanks for reading! I don’t usually read so much when I’m lying down either, but it was very hot on those days. I really like reading cross legged though, as I spend so much time on a desk chair for work.
You are so right about subjective interpretation of data. Your comment makes me think of how this data was produced very subjectively, in ways I hadn’t noticed.
February 4, 2021 — 5:15 am
ailtukhova says:
Great to hear that you found it useful! Looking forward to your next piece of work!
February 5, 2021 — 10:16 am
Enoch Chan says:
Hi Elizabeth,
Great effort recording your posture during your reading!
When you recorded your posture data, did you already have pre-defined categories? or did you descriptively record your posture, and then fit them into categories at the visualisation stage?
February 2, 2021 — 4:25 pm
esmith says:
Hi Enoch! I used two “test runs” on days I was doing a lot of reading to pay attention to what positions I used. I then generalised those into 3 categories: sitting in a chair, sitting crosslegged/on a flat surface, and lying down. And the stick figures were my notation, so I was visualising as I went. I could have broken these categories down further if I had used a different notation style, or descriptively recorded my posture. I hadn’t thought of using descriptive text as data. Thanks for that idea!
February 4, 2021 — 5:11 am