Table Of ContentALGORITHMS OF EDUCATION
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Algorithms
of Education
HOW DATAFICATION AND ARTIFICIAL
INTELLIGENCE SHAPE POLICY
Kalervo N. Gulson, Sam Sellar,
and P. Taylor Webb
University of Minnesota Press
Minneapolis
London
The University of Minnesota Press gratefully acknowledges support for the
open- access edition of this book from the University of Sydney, the
Australian Research Council, and the Social Sciences and Humanities
Research Council (SSHRC) of Canada.
A different version of chapter 2 was previously published as Sam Sellar,
“Acceleration, Automation, and Pedagogy: How the Prospect of
Technological Unemployment Creates New Conditions for Educational
Thought,” in Education and Technological Unemployment, ed. M. A. Peters,
P. Jandric, and A. J. Means, 131–4 4 (Dordrecht: Springer, 2019). A different
version of chapter 4 was previously published as Kalervo N. Gulson and
Sam Sellar, “Emerging Data Infrastructures and the New Topologies of
Education Policy,” Environment and Planning D: Society and Space 37,
no. 2 (2019): 350–6 6; and as Sam Sellar and Kalervo N. Gulson,
“Dispositions and Situations of Education Governance: The Example of
Data Infrastructure in Australian Schooling,” in Education Governance and
Social Theory: Interdisciplinary Approaches to Research, ed. A. Wilkins and
A. Olmedo, 63–7 9 (London: Bloomsbury Academic, 2018); Bloomsbury
Academic is an imprint of Bloomsbury Publishing PLC. A different version
of chapter 6 was published as Sam Sellar and Kalervo N. Gulson, “Becoming
Information Centric: The Emergence of New Cognitive Infrastructures in
Education Policy,” Journal of Education Policy 36, no. 3 (2021): 309–2 6,
available at https://www.tandfonline.com.
Copyright 2022 by the Regents of the University of Minnesota
All rights reserved. No part of this publication may be reproduced, stored in
a retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior
written permission of the publisher.
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Available as a Manifold edition at manifold.umn.edu
ISBN 978-1 - 5179- 1024- 2 (hc)
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Printed in the United States of America on acid-f ree paper
The University of Minnesota is an equal-o pportunity educator and employer.
UMP BmB 2022
CONTENTS
Introduction. Synthetic Governance: Algorithms
of Education 1
1 Governing: Networks, Artificial Intelligence,
and Anticipation 17
2 Thought: Acceleration, Automated Thinking,
and Uncertainty 35
3 Problems: Concept Work, Ethnography,
and Policy Mobility 55
4 Infrastructure: Interoperability, Datafication,
and Extrastatecraft 71
5 Patterns: Facial Recognition and the Human in the Loop 95
6 Automation: Data Science, Optimization, and New Values 111
7 Synthetic Politics: Responding to Algorithms of Education 131
Acknowledgments 145
Notes 147
Index 179
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INTRODUCTION
Synthetic Governance
Algorithms of Education
Humans were special and important because up until now they
were the most sophisticated data processing system in the
universe, but this is no longer the case.
— YUVAL NOAH HARARI, “SORRY, Y’ALL — HUMANITY’S
NEARING AN UPGRADE TO IRRELEVANCE,” WIRED.COM
The most profound technologies are those that disappear. They
weave themselves into the fabric of everyday life until they are
indistinguishable from it.
— MARK WEISER, “THE COMPUTER FOR THE
21ST CENTURY,” SCIENTIFIC AMERICAN
Ava stands on a city corner. It is the final scene of Ex Machina, Alex
Garland’s science fiction film exploring artificial general intelligence. Ava
is a sentient android with female features that escapes a remote laboratory
after “seducing” Caleb, a software engineer who was invited to submit
Ava to the Turing test.1 The impact of this scene comes from the unre-
markable nature of the streetscape through which Ava moves, surveying
a new environment to collect and analyze data that will further increase
“her” intelligence. Ava’s shadow is shown to be indistinguishable from
those around “her” as “she” passes effortlessly among the humans in the
city. In observations about the film, Ireland notes that this feminized sci-
ence fiction image of artificial intelligence (AI) appears benign, and yet
AI is already becoming “absolutely ubiquitous and totally invisible.”2
While Ava serves as an object of both fear and longing for the (het-
ero) male characters in Ex Machina, our interest is in Ava’s ubiquity and
2 IntroductIon
invisibility as emblematic of the potential for machine intelligence to
radically change society. In this book, we explore how algorithms of edu-
cation move among us in the everyday workflows, values, and rationali-
ties of education governance. Like the humans who share the street with
Ava, we are generally not aware of the presence of algorithms and AI.
While “robots in the classroom” have become a common trope when
discussing and critiquing AI in education, in this book we consider how
machines are complementing and extending contemporary education
governance, and we explore whether there are other possible governance
rationalities that may emerge from the introduction of AI.3 This synthetic
development does not involve direct replacement of human minds and
bodies, but rather it produces new ways of thinking through the conjunc-
tion of human and nonhuman cognition. These emergent systems of
thought and their possible effects, like Ava, are not immediately recog-
nizable as a presence shaping human life-w orlds.
The machines that populate this book are not examples of artificial
general intelligence, such as Ava, but more limited and specific forms of
AI that encompass a wide range of techniques and tasks that aim “to
make computers do the sorts of things that minds can do.”4 This involves
efforts to produce “systems that think like humans, systems that act like
humans, systems that think rationally, systems that act rationally.”5 There
have been two main aims of AI research: (1) modeling intelligence in liv-
ing minds and (2) using computers to act on the world in intelligent
ways.6 Many of the primary techniques of AI use existing data sets that
are historical and spatial. For example, machine learning uses algorithms
that constantly learn and adapt from training data in order to identify
patterns in new data sets. Predictions are made based on this nonhuman
learning.7 In this book, we examine and discuss how these types of task-
specific AI act on the world, particularly in relation to education gover-
nance. We adopt the view that Deleuze and Guattari celebrate in Gabriel
Tarde’s microsociology — that is, that we can understand the introduction
of AI in governance by paying attention to “miniscule bureaucratic
innovation.”8
Narrow forms of AI are already part of education systems. Many pro-
ponents are applying machine learning in data science approaches and
in student information systems, from small-s cale education technology
companies that support the administrative workflows of everyday school
life, to ambitious Silicon Valley giants that aim to disrupt education sys-
tems and sectors. Start- ups have developed computer vision for facial
recognition systems that claim to serve as time- saving tools for teachers
taking daily attendance. Microsoft, Google, and Amazon Web Systems
IntroductIon 3
also provide off-t he-s helf business intelligence products for education sys-
tems with embedded AI services. Systems of this kind became part of
many people’s everyday lives during the Covid- 19 pandemic when
schools across the world were closed and students were forced to under-
take education remotely at home, using common platforms like Google
Classroom.9 The introduction of these forms of AI in education provoke
responses that range from outrage to ambivalence, depending on how
machines are seen to interact with the prevailing purposes and practices
of education.
The emergence of AI in education is the latest chapter in a longer proj-
ect of datafication that has both changed and intensified some aspects
of education. Datafication describes the process of translating things and
events into quantitative data that can be added to massive databases that
are growing daily. Modern education systems have been predicated on
datafication — that is, on acquiring information about the performance
of students across a range of fields and then issuing with them creden-
tials underwriting the authenticity of that information.10 What is differ-
ent today is the volume and variety of digital data that are captured and
analyzed more quickly than ever before. Big data purports to provide a
basis for technological innovation that promises progress and disruption,
and analyses of these data influence both the smallest and the most con-
sequential decisions made by individuals and organizations.
Datafication, and the new modes of educational accountability and
associated performativities that accompany it, have changed education
governance. What has been variously called algorithmic or digital edu-
cation governance describes the overlap of datafication and machines in
governance processes, resulting in “the monitoring and management of
educational systems, institutions and individuals . . . taking place through
digital systems that are normally considered part of the backdrop to con-
ventional policy instruments and techniques of government.”11 The
growing use of these technical systems introduces new actors and orga-
nizations into education, while the combination of machines and humans
in the process of decision- making is accompanied by the emergence of
new political rationalities. We use the term political rationality in a simi-
lar manner to Foucault, who aimed to
identify specific political rationalizations emerging in precise
sites and at specific historical moments . . . underpinned by
coherent systems of thought, and . . . [to] show how different
kinds of calculations, strategies and tactics were linked to each
other.12
