Contemporary education policy involves the integration of novel forms of data and the creation of new data platforms, in addition to the infusion of business principles into school governance networks, and intensification of socio-technical relations. In this paper, we examine how ‘computational rationality’ may be understood as intensifying of an instrumental set of logics in educational governance and decision making, and/or as opening up new explorations around the uncertainty and incompleteness of policy. We suggest that policy rationalities focused on prediction, transparency and data provide the conditions of possibility for Artificial Intelligence to be integrated into, and intensify aspects of, what we term ‘computational education policy’.
AdamsP (2016) Education policy: Explaining, framing and forming. Journal of Education Policy31(3): 290–307.
2.
ArtsBTatenhoveJV (2004) Policy and power: A conceptual framework between the ‘old’ and ‘new’ policy idioms. Policy Sciences37(3): 339–356.
3.
BacchiC (2000) Policy as discourse: What does it mean? Where does it get us?Discourse: Studies in the Cultural Politics of Education21(1): 45–57.
4.
BacchiC (2009) Analysing Policy: What’s the Problem Represented to Be?, Frenchs Forest: Pearson Education.
5.
BacchiC (2015) The turn to problematization: Political implications of contrasting interpretive and poststructural adaptations. Open Journal of Political Science5: 1–12.
6.
Baker RSJD and Inventado PS (2014) Educational data mining and learning analytics. In: Larusson JA and White B (eds) Learning Analytics: From Research to Practice. New York, NY: Springer, pp. 61–75.
7.
BallSJ (1994) Education Reform: A Critical and Post-structural Approach, Philadelphia, PA: Open University Press.
8.
BallSJ (1995) Intellectuals or technicians? The urgent role of theory in education studies. British Journal of Educational Studies43: 255–271.
9.
BallSJJunemannC (2012) Networks, New Governance and Education, Bristol: The Policy Press.
10.
BarryAOsborneTRoseN (1996) Introduction. In: BarryAOsborneTRoseN (eds) Foucault and Political Reason: Liberalism, Neoliberalism, and Rationalities of Government, Chicago, IL: University of Chicago Press, pp. 1–18.
BoweRBallSJGoldA (1992) Reforming Education and Changing Schools: Case Studies in Policy Sociology, London: Routledge.
13.
CaliskanABrysonJJNarayananA (2017) Semantics derived automatically from language corpora contain human-like biases. Science356(6334): 183–186.
14.
de FreitasEDixon-RománELatherP (2016) Alternative ontologies of number. Cultural Studies ↔ Critical Methodologies16(5): 431–434.
15.
DeLeonP (1998) Models of policy discourse: Insights versus prediction. Policy Studies Journal26(1): 147–161.
16.
Easton D (1953) The Political System. New York: Knopf.
17.
FayB (1975) Social Theory and Political Practice, London: Allen and Unwin.
18.
GershmanSJHorvitzEJTenenbaumJB (2015) Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science349(6245): 273–278.
19.
HindessB (2005) Politics as government: Michel Foucault’s analysis of political reason. Alternatives: Global, Local, Political30(4): 389–413.
20.
JordanMIMitchellTM (2015) Machine learning: Trends, perspectives, and prospects. Science349(6245): 255–260.
21.
LasswellHD (1970) The emerging conception of the policy sciences. Policy Sciences1(1): 3–14.
22.
LernerDLaswellHD (1951) The Policy Sciences: Recent Developments in Scope and Method, Stanford, CA: Stanford University Press.
23.
LingardB (2011) Policy as numbers: Ac/counting for educational research. Australian Educational Researcher38(4): 355–382.
MackenzieA (2015) The production of prediction: What does machine learning want?European Journal of Cultural Studies18(4–5): 429–445.
26.
MarstallerLHintzeAAdamiC (2013) The evolution of representation in simple cognitive networks. Neural Computation25(8): 2079–2107.
27.
Mayer-SchonbergerVCukierKN (2013) Big Data: A Revolution That Will Transform How We Live, Work, and Think, Boston, MA: Houghton Mifflin Harcourt.
28.
MittelstadtBDAlloPTaddeoMet al. (2016) The ethics of algorithms: Mapping the debate. Big Data and Society3(2): 1–21.
29.
ParisiL (2013) Contagious Architecture: Computation, Aesthetics, and Space, Cambridge: MIT Press.
30.
PopkewitzTSLindbladS (2004) Historicizing the future: Educational reform, systems of reason, and the making of children who are the future citizens. Journal of Educational Change5(3): 229–247.
31.
Rizvi F and Lingard B (2010) Globalizing Educational Policy. London: Routledge.
32.
RoseN (1999) Powers of Freedom: Reframing Political Thought, Cambridge: Cambridge University Press.
33.
RosenbergD (2013) Data before the fact. In: GitelmanL (ed.) ‘Raw Data’ is an Oxymoron, Cambridge: MIT Press, pp. 15–40.
34.
RussellSNorvigP (2016) Artificial Intelligence: A Modern Approach, 3rd ed. Harlow: Pearson Education Limited.
35.
SellarS (2015) Data infrastructure: A review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education36(5): 765–777.
36.
Solo AM (2011) The new fields of public policy engineering, political engineering, computational public policy, and computational politics. In: Paper presented at the proceedings of the 2011 international conference on e-learning, e-business, enterprise information systems, and e-government (EEE’11), Las Vegas, Nevada, 18–21 July.
37.
WebbPT (2014) Policy problematization. International Journal of Qualitative Studies in Education27(3): 364–376.
38.
WebbPTGulsonKN (2012) Policy prolepsis in education: Encounters, becomings and phantasms. Discourse: Studies in the Cultural Politics of Education33(1): 87–99.
39.
WilliamsonB (2015) Digital education governance: Data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy. 1–19. DOI: 10.1080/02680939.2015.1035758.
40.
WilliamsonB (2016) Coding the biodigital child: The biopolitics and pedagogic strategies of educational data science. Pedagogy, Culture and Society24(3): 401–416.
41.
WoolfBPLaneHCChaudhriVKet al. (2013) AI grand challenges for education. AI Magazine34(4): 66–83.
