Both in my job and in my personal life I use a lot of technology and there are several devices that are at the core of this, including a laptop, smartphone, tablet, smartwatch and headphones which I’ve acquired over several years. Initially, decisions over which items to buy were heavily influenced by metric data in the form of price, features, pros and cons, and review scores, as well as personal preference. More recently however, as devices need updating or a phone contract comes to an end, the decision over which brand to invest in is getting more complex due to the investments I’ve already made.
Depending on which device I’m using often controls where that data is being stored. This can be anything from bookmarks and saved passwords, to backups of photos and cloud hosted documents. As I’ve started to invest more into the Google ecosystem over the past few years, decisions over buying a new piece of technology started to become governed my somewhat unconscious decisions I made years prior.
Owning devices by the same manufacturer typically gives me enhanced functionality and ease of use through a seamless integration as they’re part of the same technological ecosystem. While the choice of ecosystem I invest in for personal use largely only affects me, this is a very different when you consider this scaled up to education. When schools and universities procure technology, the implications for its users can have far more profound effects.
Within education, decisions over which technologies to invest in are not solely based upon institutions’ own requirements, as it would be for an individual using personal devices. Instead, this is often heavily influenced by technology providers through the essentialist view that assumes the technology being procured embodies their own pedagogical principles (Hamilton and Friesen, 2013). This places BigTech companies and EdTech providers in a very powerful position within education; a sector that has previously been resistant to attempts at datafication. However in recent years, perhaps with the Covid-19 pandemic serving as a catalyst, there appears to have been a step-change in the volume of data being shared with technology providers.
With VLEs and Student Information Systems capturing data on a large scale, this is giving rise to what Anagnostopoulos (2013: 11) refers to as ‘Informative Power’. In this scenario, the more schools and universities invest their data, the more this data is relied upon to make strategic decisions. This has led to the decision-making in education being decentralised from governments, schools and universities, to complex networks of data in what (Ozga et al, as cited in Williamson, 2017) as the ‘governance turn’. This in itself may not seem immediately concerning, but the software which collects this data is often designed without direct involvement with education.
Technology providers typically decide how their platforms should be used within education, without direct involvement from schools and universities. This may be in part due to education’s democratic approach to decision making, that is likely to be seen as incredibly slow and inefficient to technology companies that are used to working at speed and seeing all problems as solvable through algorithms. With technology providers increasingly working with governments and viewing themselves as thought leaders for schools and universities we’re also starting to see the development ‘fast-policies’ that are less informed by evidence-based practices and more by ‘what works’ or ‘best practice’ (Williamson, 2017: 68).
While there is now an abundance of technology available for education, the level of control given to educators has arguably been compromised. Software is now typically licensed on a SaaS (Software as a Service) basis, where the technology companies provide the software, the servers that store the data, and regular updates that institutions have very little (if any) control over. This results in schools, universities, and teachers having to change their practices to justify their worth through performance measures in what Williamson (2017: 75) refers to as ‘performativity’. Coupled with this is the concept of ‘accountability’, where the evidence of this apparent effectiveness can also be evidenced through large data sets.
With a focus on performance metrics at seemingly every level from student, to teacher, to school or university, EdTech platforms are positioning themselves as being able to help define both the problem and also provide the solution. The modern day ‘centres of calculation’ arguably happening now in schools, made possible through the software tools that are provided to education to facilitate the large scale harvesting of personal data (Latour, 1986, as cited in Williamson, 2017).
Of course not all educational institutions buy into off-the-shelf digital dashboard solutions. It’s common for wealthier institutions to resource in-house teams who can scrutinise how their data is being used and then build data-layers and reports from the ground-up based upon their needs. This is however typical of the more privileged western institutions such as Russell Group and Ivy League universities. On the other side of the spectrum, there underfunded schools who largely buy all-in-one solutions from the likes of Google and Microsoft. At the extreme end of the scale are ‘Global South’ institutions which is a term coined by Toshkov (2018) to describe anything from the poor and less-developed to oppressed and powerless.
Global South institutions as argued by Prinsloo (2020) don’t have the political infrastructures or financial resources in place to challenge datafication. Due to their oppressed situation, which has in no means been helped by their Global North counterparts, South African politicians and policymakers to are highly susceptible to the west’s ‘data gaze’ (Beer 2019, as cited in Prinsloo, 2020) with its promise of technology that is speedy, accessible, revealing, and gives a 360 degree view.
In this final visualisation I’ve attempted to illustrate how technology providers are deliberately designing solutions that rely on the large scale use of data. Whether it’s a personal device such as a smartphone, or a VLE, the premise is the same – to provide a very low and frictionless point of entry, but through accelerated datafication, it’s very impractical to get out of. Individuals and educational institutions alike are increasing their reliance on data, and rather worryingly local pedagogical practices that have previously been a hurdle are being abandoned to accommodate the functionality embedded in technology and representable through data.
Bibliography
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.
Hamilton, E. and Friesen, N., 2013. “Online Education: A Science And Technology Studies Perspective / Éducation En Ligne: Perspective Des Études En Science Et Technologie” By Edward C. Hamilton.
Prinsloo, P. 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in Higher Education, 25(4) pp.366-383
Williamson, B. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.