Block: ‘Learning’ with Data / Week 4

This week, I continued my exploration of “bodily” learning data by recording the times I took a break from learning, working, leisure or caring. These needed to be breaks that I would pay attention to and record (I might not notice that I look out of a window, or open a new tab in my browser). So I recorded each time I stopped an activity to stretch.

This week was another sedentary week. Before recording, I sorted the stretches I expected to perform into three categories: hands/wrists, back/neck/shoulders, and hips/legs/feet. These are represented by shapes. I also sorted the expected activities I would perform this week into 4 categories: work/study, leisure, care (for self and others), and rest. These are represented by colours. The time of the stretches and activities were recorded throughout the week.

Figure 1. Tracking my stretches over a week.

On Tuesday, I downloaded a Stretch Reminder app to see if an app could “nudge” me to stretch more often. Shapes with a line represent the times I was “nudged” to stretch. Reminders were sent via push notifications every hour. You can see I often ignored the reminders.

At the end of the week, I drew a gender-neutral body to plot my stretches on. While I considered using an outline of my own body, to avoid an assumption of a ‘typical’ human body, or perhaps an “ideal learner” Eynon describes (2015, p. 408), I’m not quite ready to share my own silhouette here. But by drawing over the body with colourful representations of my stretches I think I was still able to reflect the messiness and individuality of embodied activity.

Like last week, I like how the act of animating data visualisations can help draw attention to the construction of the visualisation.

Video 1: I hand draw stretch tracking data on an outline of a body.

As I worked on this I reflected upon my activity. Firstly, I learnt that “nudging” via app doesn’t have any impact on the number of times I stretch per day. Would “nudging” from learning analytics algorithms impact my learning activity? I’m not sure it would after this. Secondly, I felt the need to collect more comparative data – I would like to compare this sedentary week to a more active week. Lastly, I heed Enyon’s warning about interpreting this data with an understanding of what it is actually capturing and not to invest too much into it (2015, p. 409). This data counts the stretches I performed, but not the quality of the stretching.

This data could be interpreted in different ways by humans and machines. To me, it’s perhaps a subjective count of the times I stopped to care for myself, but also the pains I felt while performing different activities.

To different algorithms, the data could look different again. If I were being monitored by proctoring software, it could be the times I potentially cheated; MS Teams might interpret this as the times I stopped learning or being productive; to the Stretch Reminder app, it’s the times I did or didn’t complete desired user behaviour.

Data is never neutral, in its production or construction, and the quantifying of self through data can feel empowering and restrictive at the same time.

References

Eynon, R., 2015, The quantified self for learning: critical questions for education, Learning, Media and Technology, 40(4), pp. 407-411.