Proposing big data as the means to a technical fix is “…Big Data solutionism… the idea that Big Data sets can control, solve and overcome economic and political crises…” [Fuchs, 2019]. Big data, such as that from social media, is suggested as a means to develop policy [Williamson, 2017b], primarily on neoliberal principles, such as economy [Bartlett et al, 2014] rather than suitability, ignoring the underlying inequalities and social injustice [Fontaine, 2016] behind the unrepresentative nature of big data sets [O’Neil, 2016; Schradie, 2017].
Those involved in governance determine the ‘problem’ this data will solve and the means by which this will be done [Williamson, 2017a]. This may also involve other actors, who may make use of this data for their own purposes [Robertson, 2019]; this gives them considerable power and influence, as well as the means to profit by it. Even where students or staff have any control, all important decisions have already been taken; that practices are data-led say, to increase efficiency [Williamson, 2019], may not be up for discussion [Ozga, 2016].
These data are “...products of complex assemblages…” that “…construct the infrastructure of accountability…” which “...shapes what and who count…” [Anagnostopoulos et al, 2013]. This infrastructure can end up ‘…sunken into objects...’ where it can “…recede into the background…” [Anagnostopoulos et al, 2013]; making it less likely to be noticed and therefore questioned. Commercial platforms are so pervasive, they interfere with every aspect of education, shifting it away from being a common good to a business venture [Van Dijck et al., 2018].
Acquisition of performance data, for staff and students, along with judgements and ranking [Espeland and Sauder, 2016] to contribute to decision-making [Esposito & Stark, 2019], are common practice in higher education. However, using data gathered “…on terms that are partly or wholly beyond the control of the person to whom the data relates…” has been called, not metaphorical but actual, colonialism [Couldry and Mejias, 2019].
To be used, this data is: (a) reduced: made simple for easy evaluation, meaning that aspects will be lost if their value is not appreciated; (b) standardised: made context-free for easy comparison [Ozga, 2016] denying the bias in their production [Boring, 2017], made to ‘…appear as objective facts…’ [Anagnostopoulos et al, 2013], so less likely to be questioned. This is not a neutral act [Williamson, 2017b].
The processed data represents people as ‘thin descriptions’ [Ozga et al, 2011], depersonalised, affecting how this proxy [Williamson et al, 2020] of what it claims it represents is seen and used. That such descriptions can be used to rate the ‘value’ [Burrows, 2012] of individuals and their achievements, can have serious consequences. Whether staff or students know what is done with and on the basis of their performance data, this is “…structured and structuring…” such that they could be “…driven by analysis of performance data…” [Ozga, 2016]. Instead of encouraging improved metrics through ‘improved’ performance, the technology can act as an ‘engine of anxiety’ [Espeland and Sauder, 2016], causing reactions that counteract institutional aims, or seek to resist surveillance [Fontaine, 2016].
Word count: 507
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.
Bartlett, J., Miller, C., Reffin, J., Weir, D. and Wibberley, S., 2014. Vox digitas. Demos.
Boring, A., 2017. Gender biases in student evaluations of teaching. Journal of public economics, 145, pp.27-41.
Burrows, R., 2012. Living with the h-index? Metric assemblages in the contemporary academy. The Sociological Review, 60 (2), pp. 355–372.
Couldry, N., and U. A. Mejias. 2019. Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject. Television & New Media. 20 (4): pp. 336–349.
Espeland, W. N., and Sauder, M., 2016. Engines of anxiety: Academic rankings, reputation, and accountability. New York, NY: Russell Sage Foundation.
Esposito, E., and Stark, D., 2019. What’s observed in a rating? Rankings as orientation in the face of uncertainty. Theory, Culture and Society, 36 (4), pp. 3–26. https://doi.org/10.1177/0263276419826276
Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.
Fuchs, C. 2019. Beyond Big Data Capitalism, Towards Dialectical Digital Modernity: Reflections on David Chandler’s Chapter. In: Chandler, D. and Fuchs, C. (eds.) Digital Objects, Digital Subjects: Interdisciplinary Perspectives on Capitalism, Labour and Politics in the Age of Big Data. pp. 43–51. London: University of Westminster Press. DOI: https://doi.org/10.16997/book29.c. License: CC‐BY‐NC‐ND 4.0.
O’Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. USA: Penguin Random House.
Ozga, J., 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15 (1), pp. 69-81.
Ozga J., Dahler-Larsen P., Segerholm C., et al. (eds), 2011. Fabricating Quality in Education: Data and Governance in Europe. London: Routledge, pp.127–150.
Robertson, S., 2019. Comparing platforms and the new value economy in the academy. In R. Gorur, S. Sellar, & G. Steiner-Khamsi (Eds.), Comparative methodology in the era of big data and global networks, pp. 169–86. London, UK: Routledge.
Schradie, J., 2017. Big Data is Too Small: research implications of class inequality for online data collection. Media and class: TV, film and digital culture. Edited by June Deery and Andrea Press. Abingdon, UK: Taylor & Francis.
Van Dijck, J., Poell, T. and De Waal, M., 2018. The platform society: Public values in a connective world. Oxford University Press.
Williamson, B., 2017a. Digital Education Governance: political analytics, performativity and accountability, in Big Data in Education: The digital future of learning, policy and practice. Sage.
Williamson, B., 2017b. Conceptualising Digital Data in Big Data in Education: The digital future of learning, policy and practice. Sage.
Williamson B., 2019. Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50 (6), pp. 2794–2809. doi:10.1111/bjet.12849.
Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25 (4), pp. 351-365.