Rationale.
This visualisation follows up on the question that emerged last week: if data we use in education policy making is unreliable, what should be done about it? The quickest answer is to get better data (O’Neil 2016). As Williamson (2017) shows, this approach has been undertaken by a number of agents, from OECD to National Pupil Database in the UK. They gather all sorts of metrics on students and teachers, in hopes that the more varied and detailed the data, the better it is. That, however, might be erroneous. As Kearns & Roth (2020) show, the data-based solutions, such as algorithmic tools, generate unpredictable results failing to preserve certain core values. Furthermore, the data collection itself introduces new practices to education, thereby changing its nature (Williamson 2017). This nulls the neutral observer role of data metrics. Finally, data gathering isn’t objective in itself (O’Neil 2016). It is also superficial (Ozga 2016).
My data visualisation this week shows that unquantifiable value of teachers. I juxtaposed the numerical information that usually represents teachers’ performance (Williamson 2017) for governing purposes, namely the students’ grades, with everything else I learned (or not) from these teachers that data are blind to.
Design.
For this final visualisation, I returned to Dear Data design (below). I used the mini blackboard in my room this time as I felt this, together with the theme, would be a nice closure to the visualisation blog. Number of arrows based on my subjective evaluation. All these teachers taught at the same time & institution.
Sources.
Kearns, M. Roth K. (2020) The Ethical Algorithm; Audible
Lupi, G. Posavec, S. (2016) Dear Data. Princeton Architectural Press
O’Neil, C. (2016) Weapons of Math Destruction. Random House Audio (Audible release date 09-06-2016)
Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
Williamson, B. (2017) Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.
Really excellent visualisation here Magda. I particularly liked the chalk board, which definitely emphasised the ‘hand drawn’ aspects of the task. I liked how you also provided zoomed in details.
Useful reflection here too. The ‘superficiality’ noted by Ozga is an important critical dimension, I think, and as you describe, it is a useful counter to the idea that simply collecting more data will make things better.