Despite seeming like an ephemeral topic, there are various tangible aspects of ‘school data’ that we can examine. First is the usually overlooked issue of infrastructure – i.e. the ‘backstage’ mess of wires, routers, devices and code that are integral to the production and circulation of data within every school. These aspects of data are often hidden away behind locked doors, ceilings and underground trenches. Yet, as researchers in the area of infrastructure studies have shown, there is much that can be gained from analysing these ‘submerged’ elements of the datafied school.
Thus, following the lead of Geoffrey Bowker, Susan Leigh Star and others, our investigations start from the premise that schools’ data infrastructures can be productively analysed “as sites of contestation, of practice, and of power” (Vertisi 2019a). Bowker (1994) terms this as approach as “infrastructural inversion” – i.e. focussing on the inner workings of a school’s data infrastructure – with a particular focus on the relational aspects of what people are doing around and through these components.
Points of interest here include the invisible labour that is required to keep data infrastructures working – from the routine maintenance work of technicians to instances of situational repair and problem solving when infrastructures breakdown, go wrong or malfunction. Of course, we are not looking to comprehensively map a school’s complete infrastructure, but rather attend to particular points where people encounter their school’s data infrastructures – “wherein individuals confront, contest, or otherwise deploy networked systems” (Vertisi 2019a).
Although perhaps more visible, we should also not overlook the important underpinning role of software in supporting the data-work that takes place across every school. Mundane data-analysis tools such as Microsoft Excel play an integral part in how school data is reconfigured and circulated. Excel shapes schools’ data practices in the demands that it places on data entry, its default analytic functions and visualisations, and the (lack of) ease with which .XLS files can be shared and collaboratively worked on.
At the same time, it is important to explore how schools and the practices of school staff shape the nature of what ‘Excel’ is. For example, the capacity of Excel to colour-code various cells “does not become meaningful, perspicuous, or identifiable as an important attribute of the software until actors locate and engage that element of the software tool and narrate it as such” (Vertesi 2019b, pp.381-382). As such, we need to play close attention to “the practices that compose software: that is, how software works through associations, interactions, and performances between and among individuals, groups, organizations, and code” (DiSalvo 2019).
We also need to pay attention to the other ways that data is being re-materialised into new forms within each of our research schools. As soon as something is captured into digital data then it does not stay immaterial for long. Instead, digital data will soon takes the form of online data visualisations, dashboards and other indicators. As Laura Forlano (2019) observes, these new online material forms “are clickable, taggable, searchable, and indexable; as such, they have new associations with one another as well as with networks”. At the same time are all manner of physical materialisations – from print-outs and reports to corridor noticeboards and display-screens. Data is performed in a number of unremarkable – but highly revealing – ways. These are all aspects of school data that are worthy of our attention.
REFERENCES
Bowker, G. (1994). Science on the run: Information management and industrial geophysics at schlumberger, 1920–1940. Cambridge, MA: MIT Press.
Forlano, L. (2019). Materiality. in Vertesi, J. and Ribes, D. (eds). Digital STS: a field guide for science and technology studies. Princeton NJ, Princeton University Press (pp.11-17)
Vertesi, J. (2019a). Infrastructure. in Vertesi, J. and Ribes, D. (eds). Digital STS: a field guide for science and technology studies. Princeton NJ, Princeton University Press (pp263-266)
DiSalvo, C. (2019). Software. in Vertesi, J. and Ribes, D. (eds). Digital STS: a field guide for science and technology studies. Princeton NJ, Princeton University Press (pp.365-368)
Vertesi, J. (2019b) From Affordances to Accomplishments: PowerPoint and Excel at NASA. in Vertesi, J. and Ribes, D. (eds). Digital STS: a field guide for science and technology studies. Princeton NJ, Princeton University Press (pp.369-392)