Why EdTech is always right: students, data and machines in pre-emptive configurations

ABSTRACT

Pre-emption describes a system of automated knowledge creation and intervention that steers the present towards a desirable future, by building on knowledge derived from the past. Folding together temporalities makes it impossible to disprove pre-emption. It is increasingly featured within EdTech, introducing new forms of automated governance into education. This paper examines how students and EdTech come together to make pre-emption possible, not as a single event but as a normalised governance instrument. For this, we introduce Lucy Suchman’s idea of configuration to examine pre-emptive EdTech. The paper presents three openings into the configuration of students and pre-emptive EdTech. These include observations from an EdTech trade show; interviews with insiders of technology companies; and analysis of accepted papers to a learning analytics conference. We conclude the data used at the heart of pre-emptive EdTech seeks to exclude students and configures them as absent. Yet, its interventions have material consequences.

KEYWORDS:

• Education technology
• pre-emption
• configuration
• automation
• governance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 A desired status can be as simple as ‘engaged student’. Throughout this paper, ‘desired future’ or ‘desired goals’ refers to a pre-defined status that a pre-emptive configuration aims to achieve. This is similar to the notion of regulation in cybernetic governance, where a status is defined, agreed upon and then ‘regulated’ through control mechanisms. The only difference between pre-emption and cybernetic governance is then that the regulated status is either a point of reference in the future (pre-emption) or the present (cybernetic governance).

2 While we use anticipatory governance here as an event, we can see that pre-emption also has an affinity with ‘anticipatory governance’ as a research field about building capacity in foresight, and ‘managing emerging knowledge-based technologies while such management is still possible’ (Guston 2014, 218).

3 For an overview of other methods such as deterrence and prediction see Massumi (2015, 5–9).

4 Ethics approval for observations at BETT have been reviewed and granted by the Ethics Panel UNSW HREAP B: Arts, Humanities & Law with the approval number HC190430.

5 Ethics approval for interviews at EduTech have been reviewed and granted by the Ethics Panel UNSW HREAP B: Arts, Humanities & Law with the approval number HC190019.

6 The company also has been mentioned in the Automating Society report about Sweden by Algorithmwatch (Kaun and Velkova 2019).

7 Something that we did not anticipate during BETT in January 2020 was that only within a few months most universities would suspend on campus education due to the Covid-19 pandemic. The speed through which proctoring – and thereby pre-emptive – technologies became normalized within higher education exceeded our expectations.

Additional information

Funding

This work was supported by Australian Research Council [FT180100280]; UNSW Faculty of Arts and Social Sciences, and Faculty Funding for Higher Degree Researchers.

Notes on contributors

Kevin Witzenberger

Kevin Witzenberger is a Scientia PhD candidate at the University of New South Wales, Sydney, Australia. His dissertation investigates forms of automated governance within education. Kevin is interested to understand the shifting power relations as tools of automated governance transform into fully automated technical systems.

Kalervo N. Gulson

Kalervo N. Gulson is a Professor in the School of Education and Social Work, University of Sydney, Australia. His research investigates whether new knowledge, methods and technologies from the life and computing sciences, including Artificial Intelligence, will substantively alter the processes and practices of education policy.