The Data-doppelganger and The Cyborg-self: Theorising The Datafication of Education – Pierlejewski – 2020 – longer notes here
This article discusses how we (as users of technology) are now rendered as our data. This article separates the data and the person, with the two coming together to make the cyborg-self or data-doppelganger. This analysis of the cyborg-self is done through the use of literary and psychoanalytic methods. A report by the British Office for Standards in Education (Ofsted) called Bold Beginnings on ‘good practice’ in digital education is then analysed using this data-doppelganger framework. This article uses Cheney-Lippold’s (2017) definition of datafication, ‘the transformation of part, if not most of our lives into computable data’. The data collected on users is built up in layers, creating algorithmic identities for people. These algorithmic identities are often treated as more accurate than our physical selves, as they are seen as ‘rational’ and ‘objective’. This applies to education, with data now being used to judge students, teachers, and schools; making education data-centred, rather than child-centred.
This article develops an approach they call ‘doppelganger as method’. This takes from Burman’s (2018) ‘child as method’, which examines how childhood functions to reflect and constitute socio-political dynamics. While the ‘child as method’ approach brings together disciplines like feminism and postcolonialism, ‘doppelganger as method’ uses literary and film critique approaches to understand data in education. The literary trope of the doppelganger story is the centre of this, having five key features: an exploration of the relationship between the self and the self; the original and double are inextricably linked; the double’s existence causes anxiety for the original’; the double reveals hidden desires of the original; the original will kill the double in what is really a suicide.
Psychoanalytic concepts are used here too. Freud’s (1919) unheimliche, for example, is an examination of discomfort rooted in recognition of the self. The unheimliche is what exists between the internal and external, causing anxiety about reality. Data is seen in this way here. Freud’s model of the psyche is used here too: The ego is rational; the id is instinctual; and the superego is regulatory. The doppelganger allows for repressed desires of the id to happen (making it also part of the ego) while regulating the subject as the superego. These methods are used to analyse Ofsted’s (2017) Bold Beginnings, asking three questions about the data: How does the data-doppelganger complete the ego?; How does the data-doppelganger reveal the repressed desires of the id?; How does the data-doppelganger regulate the subjects?
The doppelganger as ego: the data doppelganger of a child is a ‘safe’ and ‘predictable’ version of a student. Teachers are tasked with creating this through measuring students over time (with this data then being used to measure the ‘success’ of the teachers themselves as well). Here, teachers construct the ego, allowing teachers to ‘know’ who they are based on this measurable ‘success’. Bold Beginnings pushes for measuring children from the very beginning of schooling, forcing children to be measured and see themselves measured. Children thus enter the mirror phase, discovering where they are ‘deficient’ and responsibilising the child for their own improvement.
The doppelganger as id: organic child can emerge from the id and is not easily measured. The id is therefore reassembled to be controlled: the body is controlled through measuring where/how they learn, while the mind is regulated through the testing. Bold Beginnings eschews child-led play as it is unpredictable and does not produce the desired data.
The doppelganger as superego: The point of education here becomes the production of data. The id-driven child is to be replaced with a measurable and ordered dataset. Data is used as the superego, to regulate within pedagogy. Bold Beginnings promotes a ‘direct teaching’ model, regulating behaviours of teachers and students to promote the creation of clean/neat data. This limits teacher autonomy, disallowing any teaching which may not produce the ‘correct data’. Student autonomy is limited by unending testing and measuring, intended to stoke competition but often causing alienation.
Barassi begins by saying that data is not new in education, rather she quotes Lawn (2013) in saying, ‘the history of the education sector cannot really be understood without considering the history of measurement’. This chapter examines Big Data in education, positioning governments and private bodies creating educational data infrastructures due to the value of educational data. This has a huge impact of childrens’ lives but data collection and brokering remains deregulated and opaque.
Measurement in education goes back (in this chapter as far as 1904, with Thorndike suggesting a standardised unit of measurement for education. In the UK, Valentine, Burt, Isaacs, etc argued that children had innate intellectual capacity and that this could be measured (Wooldridge, 2006). This is aligned with early industrial capitalism here but the measurement movement in education became more prominent in Europe after WWII as well. This belief in measurement and innate ability persists in the UK today, while the measurement of students is also used to govern teachers and institutions (Oza, 2009). Both the UK and US governments have put self-assessments into place for educational institutions. This makes institutions measurable and comparable. Both governments also have longitudinal student databases, measuring students across their educational life. Williams (2017) states that children have become ‘proxies for school performance, while schools became ‘data centres’.
There has been (and still is) a belief that EdTech will lead to personalised learning strategies in schooling. While technology can make schooling more personalised and more inclusive, Ito (2012) raises critical questions around how digital technology is implemented in reality and how this can exacerbate exciting educational inequities. Many (such as Mayer-Schönberger and Cuckier (2013)) have nonetheless remained optimistic about Edtech, while others (such as Selwyn (2013)) highlight how the digitalisation of EdTech is often founded on neoliberal models which erode the public nature of education. There is a preference for data production over all else, quantifying and classifying students for the benefit of private bodies. The classifying of students is often tantamount to profiling, locking in stereotypes and pigeonholing people within these personalised learning systems.
Personalised learning systems are often run by private, for-profit companies with black-boxed algorithms. We thus cannot know how these systems deal with the issues outlined above. The baseline assessments in the English school system are given as an example here. Bradbury and Roberts-Holmes (2016) studied the pilot launch of the assessment, finding it to have negative impacts on the introductory days of schooling for students, while teachers doubted the efficacy of the system. This doubt was supported by the wide ranging outcomes found by the different companies involved with the pilot launch. The point being made is that these systems are not unbiased or objective, and that children cannot be reduced to a single datapoint.
Despite this, between 2011 and 2015, both the UK and US have relaxed laws regulating educational data to facilitate data disclosure to third parties. This raises questions around who constitutes as a third party, how the information is shared, for what purposes, etc. The data exchanges enabled here happen without much oversight as there is a largely deregulated market of educational data brokers. The Center for Law and Information Policy at Fordham Law School published a report in 2018, showing 14 data brokers selling personally identifying information on a range of categories (such as ethnicity, class, and religion) (Russell et al, 2018). Alongside the rise of systems like biometric data, LMSs, etc, educational technology now seems inevitable and inescapable to many. Neither parents nor students are empowered to give informed consent to this data collection, or know what is being done with the data once it is collected. When parents do raise concerns, they are often dismissed by the school (or the principal, in the example given here. Schools are seen as enabling Big Tech companies to gather and store large amounts of data on their students, without addressing any potential harms