Block: ‘Learning’ with Data / Summary
This block, I tracked different bodily data while learning and working – my posture, movement, and reflected gaze. I wanted to experiment with the encoding of the body through data as a way to explore the turn towards bodily and emotional data in edtech (Knox et al. 2020). If the edtech imaginary treats the failings of educational systems as “engineering problems to be solved at scale” (Friesen 2019, p. 144), then the turn towards bodily data posits our bodies as part of the ‘problem’ to be ‘solved’ by edtech and learning analytics. This is an obvious and problematic endpoint of data practices based on behavioural approaches – or, at least obvious to anyone who has experienced similar exertions of control over their bodies based on their gender, disability, sex or race.
I didn’t set any clear questions to be answered in this block, choosing to explore and think about the construction and curation of bodily data instead. But my experimenting so far feels only surface level and, despite inspiration from the ‘Dear Data’ project, I struggled to track and visualise data in ways that I felt reflected the complexities of intra-action between the mind-body and data while learning or working (Rogowska-Stangret 2017). Still, I posited that a ‘knowing’ awareness of the methods of data collection and visualisation could provide a site of performative resistance to surveillance technologies and learning analytics, in a similar way to how a knowing awareness of gender performance informs drag acts.
The act of self-tracking made me think about how I ‘pay attention’ to learning activity and data production and when I’m happy to let a machine ‘pay attention’ on my behalf. Can an awareness of the rules of the game, so to speak, help develop learners’ empowerment over their data? Although increased transparency and personalisation of data (for example, through personalised data dashboards) might help to develop a ‘knowingness’ of data practices, we cannot automatically assume that this will lead to student or teacher empowerment over their learning (Bulger 2016, Tsai et al. 2020). Assuming also that teachers should explain to students how their data is being collected ignores the ways in which power dynamics in educational systems and complex data processes obscure teachers’ comprehension of data practices as well.
It’s these tensions and complexities I’ll carry in the back of my mind as we move into our next block on ‘teaching’ with data.
Bulger, M, 2016, Personalized learning: The conversations we’re not having. Data and Society, 22(1), pp. 1-29.
Friesen, N 2019, “The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.” In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press, pp. 141-149.
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.
Rogowska-Stangret, M 2017, Body, viewed 13 February 2020, <https://newmaterialism.eu/almanac/body/body.html>.
Tsai, Y-S, Perrotta, C & Gašević, D 2020, Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45(4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396