28 SES 12, The Promises and Dangers of the New Biological Turn in Education
This paper explores connections between postgenomics and prediction in education policy. Increasingly, education policy is informed by decisions made by algorithms, managed through data infrastructures, and premised on combining data analytics and new computational capacities, such as machine learning, that are able to ‘detect patterns in existing data and calculate predictions of probable future actions and outcomes’ (Williamson, 2016: 5). The possibility of ‘anticipatory governance, whereby people’s actions and possibilities may be calculated and pre-empted’ (Williamson, 2016: 5) is allowing for the quest for prediction in policy sciences to be reinvigorated (Webb & Gulson, 2015). This paper examines how the algorithmic modelling of learning and life is underpinned by changes to biology. As Harari (2015) notes, the move to algorithms ‘is fuelled by biological insights more than by computer science. It is the life sciences that have concluded that organisms are algorithms’ (p.345). Life sciences are now postgenomic fields, that ‘use genomic information…as…standard elements of their research practices’ (Stevens & Richardson, 2015: 3). Bioinformatics is central, understood as ‘the increasing entanglement of biology with computers’ (Stevens, 2013: 6), and entails data management, statistics and sequencing. There have been advances in fields such as epigenetics, that are adding new knowledge to the field of education and social policy about how people learn and the effects of the environment (Youdell, 2016a). This new knowledge is encapsulated as ‘[g]enomic infrastructures – the ensemble of software, hardware, algorithms, networks, and repositories that handle sequence data and other biological data – [that] tell us something about how genomes come to be what they are’ (MacKenzie, 2015: 74-5). In this paper, we identify a confluence between the conditions being created in education around data management and algorithmic decision making that allows for the seamless integration of new forms of postgenomic data. It is plausible that the introduction of new computationally based practices into education policy, as the ‘evidence’ base and one source of truth, will lead to the reshaping of education, as for in biology. That is, the success of computationally based approaches such as bioinformatics ‘depended not on adapting the computer to biological problems, but on adapting biology to problems computers can readily solve’ (Stevens, 2013: 21). We suggest that the data infrastructures of contemporary education are well on the way to this adaptation, and it is conceivable that genomics, as new data sets, will become standard elements for education policy (e.g., Williamson, 2016b).
Harari, Y.N. (2015). Homo Deus: A brief history of tomorrow. London: Harvill Secker. MacKenzie, A. (2015). Machinic learning and genomic dimensionality: From features to landscape. In H. Stevens & S.S. Richardson (Eds.), Postgenomics: Perspectives on biology after the genome (pp.73-102). Durham and London: Duke University Press. Stevens, H. (2013). Life out of sequence: A data-driven history of bioinformatics. Chicago: The University of Chicago Press. Stevens, H. & Richardson, S.S. (2015). Beyond the genome. In H. Stevens & S.S. Richardson (Eds.), Postgenomics: Perspectives on biology after the genome (pp.1-8). Durham and London: Duke University Press. Williamson, B. (2016a). Silicon startup schools: technocracy, algorithmic imaginaries and venture philanthropy in corporate education reform. Critical Studies in Education, 1-19. doi:10.1080/17508487.2016.1186710 Williamson, B. (2017). Decoding ClassDojo: psycho-policy, social-emotional learning and persuasive educational technologies. Learning, Media and Technology, 1-14. doi:10.1080/17439884.2017.1278020 Youdell, D. (2016a). New biological sciences, sociology and education. British Journal of Sociology of Education, 37(5), 788-800. doi:10.1080/01425692.2016.1184406
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