The data imaginary, as termed by Beer (2018), refers to the marketed promises and potential of datafication and data analytics while raising fears of “missing out” or “acting too slowly”. The data imaginary is used to expand the boundaries of or intensify what is acceptable for datafying, while simultaneously reducing resistance and increasing adoption. These new, uncharted data territories are thought of as data frontiers (Beer, 2019; Prinsloo, 2020) and the expansion into these territories lead to the idea of data colonialism (Couldry and Mejias, 2020; Prinsloo, 2020).
In data colonialism, social data (much like land, people, and resources in historical colonialism) are viewed as “just there” or “raw material” that can be extracted, appropriated, and commodified (Couldry and Mejias, 2020; Couldry and Mejias, 2019; Prinsloo, 2020). Making this colonization possible, through data imaginary, is the promise that data are: speedy, accessible, revealing, panoramic (data can see everything), prophetic (data can give insight and foresight), and smart (Beer, 2019; Prinsloo, 2020).
Education is one frontier that has been intensified over the last decade or two. New, large-scale data systems and infrastructures have been developed and installed by state governments, education corporations, and education institutions themselves to monitor, collect, analyze, forecast, and report data about schools, teachers, and students (Anagnostopoulous 2013; Fontaine, 2016; Williamson, 2017). These data have become a key component in developing educational policy (Williamson, 2017).
At the heart of educational data are metrics of performance and productivity from education institutions and their staff, faculty, and students (Williamson, 2017). Closely linked to performativity is that of accountability: measures of effectiveness and efficiency and focused on the quantifiable (Williamson, 2017; Fontaine, 2016). Accountability takes an instrumental view of learning and positions it as an output, allowing meaningful relationships between inputs (e.g., funding, pedagogy, and curriculum) to be made (Fontaine, 2016).
Educational policy that emphasises performance and productivity reorients educators to focus on things that can be quantified and quantified positively (Williamson, 2017). Standardized tests, for example, are a common source of data that promises to hold institutions accountable for student learning. Consequently, schools and educators are focused on raising or maintaining high test scores which can lead to “teaching to the test” by altering curriculum (Fontaine, 2016).
Intertwined within the notions of datafication and data colonialism is neoliberalism which further concentrates focus on measures of productivity, engagement, and inputs and outputs (Fawns, 2020; Ozga, 2015). Ozga (2015 and 2016) notes that neoliberal policy emphasizes the transparency of performance data to the public and data that is also comparable to other local, national, and international schools. These (inter)national data reports can result in competition and school ranking systems. Consequently, education systems can be pressured to improve their data (and rankings) which further prioritizes staff, faculty, and student data collection and policy intervention (Williamson, 2017) .
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Beer, D., 2018. The data gaze: Capitalism, power and perception. Sage.
Couldry, N. and Mejias, U.A., 2019. Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), pp.336-349.
Couldry, N. and Mejias, U.A., 2020. The Costs of Connection: How Data Are Colonizing Human Life and Appropriating It for Capitalism.
Fawns, T., Aitken, G. and Jones, D., 2020. Ecological teaching evaluation vs the datafication of quality: Understanding education with, and around, data. Postdigital Science and Education, pp.1-18.
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.
Ozga, J. and Segerholm, C., 2015. Neo-liberal agenda (s) in education. Governing by inspection, pp.27-37.
Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
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.