Block: ‘Governing’ with data / Week 9
This week I inventoried the physical objects I use for the Critical Data and Education course (MSCCDE). I wanted to answer the questions:
- How many physical objects do I use for this course vs how many things do I need?
- How many resources do I consume through the act of online study?
Originally I wanted to document a physicalisation of these objects instead. I was inspired by Song Dong’s Waste Not installation, and the ways in which materialising data might underline the more-than-human and more-than-digital aspects of human-data-technology assemblages (Lupton 2019). But for space-related reasons I had to stick to a 2D version of this (see Figures 1 & 2).
Then, after reading how imbricated processes of quantification, standardisation and classification transform local knowledge into metrics (Anangnostopoulos et al. 2013), I decided to apply this process to my visualisation just for fun (Context for claim of ‘fun’: I was a library cataloguer before moving to education roles).
I created a 5-minute schema based on my questions and similar specifications I’ve seen used in education e.g. ACARA, CEDS (see Figure 3). I then attempted to standardise and classify my cartoon objects in a way that could be read and analysed by a machine (see Figures 4 & 5).
Figures 1 & 2 and 4 & 5 represent the same objects, and are both mirrors into my way of learning. While the latter figures might prove more useful for answering my questions, thanks to quick classification, the former figures reveal less quantifiable ways in which I relate to and use these objects.
There is a bit of play in each set of figures, represented by the mirror in Figures 2 & 5: I don’t actually use a mirror for study, but for MSCCDE I create ‘data mirrors’ each week through these activities.
It turns out I use a lot of things, many of which I don’t really need but happen to have on hand, and many of which consume power. Next I would try to calculate how much power I actually use. Governing bodies might be interested in data like this for calculating the cost of online learning borne by students. Particularly where carbon consumption shifts away from the campus to households (Filimonau et al. 2021), this data could contribute to more accurate calculations of the carbon costs associated with online education.
Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R., 2013. Introduction: Mapping the information infrastructure of accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.
Filimonau, V., Archer, D., Bellamy, L., Smith, N. and Wintrip, R., 2020. The carbon footprint of a UK University during the COVID-19 lockdown. Science of The Total Environment, 756, p.143964.
Lupton, D., 2019. Data selves: More-than-human perspectives. John Wiley .