Sensing smart schools: fabricating educational institutions as sentient spaces in the...

Williamson, B. 2014. Sensing smart schools: fabricating educational institutions as sentient spaces in

the software-supported city. University of Stirling

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Sensing smart schools

fabricating educational institutions as sentient

spaces in the software-supported city

Ben Williamson, University of Stirling

Paper prepared for School of Education seminar series, University of Stirling, 29

September 2014

Abstract Along with imaginings of the future of the ‘smart city,’ an urban environment augmented

by information and communication technologies, the idea of the ‘smart school’ is emerging in

re-imaginings of the future of education. This paper argues that smart schools are emerging

‘fabricated spaces’ formed from a mixture of discursive imaginings and related technical

developments, and suggests that such spaces are to be managed and governed through

processes written in computer code. In particular, it highlights the dependence of smart

schools upon: a constant flow of digital data; the positioning of students as nodes in networks

whose behaviours can be nudged and tweaked through social network effects; the

deployment of networks of surveillant sensor devices; the ways they are visualized through

graphical displays; the positioning of students as ‘operatives’ who must ‘learn to code’ in

order to become ‘smart citizens’ in the digital governance of the smart city; and the

mobilization of ‘machine learning’-based techniques of predictive and prescriptive analytics

that enable student data to be used to anticipate their actions and pre-empt their futures.

These features of smart schools are characteristic of a new technocratic way of

conceptualizing educational practices and spaces, and of emerging modes of both ‘real-time’

and ‘future-tense’ digitized education governance.

Computer code and digital data have become powerful influences in the social

organization and governance of education. This paper surveys and maps some of the

key ways in which code and data have combined as a ‘sentient infrastructure’ that



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permits increasingly real-time and automated governance processes to take place

within educational settings. Its particular focus is on how education is being

positioned in relation to emerging developments and debates concerning ‘smart

cities’—urban settings monitored and managed using ICT infrastructure and

ubiquitous computing. My argument is that by tracing key technical developments

and related discourses we can begin to discern some of the contours of a new way of

conceptualizing the organization, management and governance of education.

Specifically, I argue, we can begin to sense something of the outline of ‘smart

schools’ that are currently in-the-making—fabricated educational spaces that are

thoroughly augmented, mediated and even constituted by assemblages of coded

technologies and data infrastructures, and that are supported by the production of

discursive imaginings. By surveying related discursive and technical developments,

my aim is to get some sense of what schools might become (but not to claim that it is

possible to document the emergence of new kinds of fully-formed ‘sentient’

educational institutions fully facilitated by digital technologies), and to highlight

how software code and digital data might contribute to the fabrication of these

educational settings in the future. It is through code and data that smart schools will

become programmable, and through which the practices of smart schooling will be

enacted.

In the fields of geography and sociology, increasing attention has turned in recent

years to the emergence of ‘smart cities,’ ‘programmable cities,’ ‘computational

urbanisms’ and ‘sentient spaces’ that are augmented with ‘big data,’ ‘sensor

networks,’ ‘ubiquitous computing,’ ‘coded infrastructures’ and other

computationally programmable processes and software-supported practices (e.g.

Crang & Graham 2007; Kitchin 2011, 2014; Kinsley 2011; Gabrys 2014; Rose et al.

2014; Thrift 2014a, b). Many major commercial computing firms have launched

projects promoting their products for ‘smart cities,’ ‘future cities’ and ‘city sensing’

programmes, including IBM, Cisco, Intel and Microsoft. These are linked to huge

urban projects building new smart cities from the ground up, such as Songdo in

South Korea and Masdar in Abu Dhabi. Large funding grants have been awarded to

research on new digital urban infrastructures, many based at new research centres at

universities, while political initiatives have made questions about the future of cities

into a subject of governmental attention. The UK Technology Strategy Board, for

example, awarded £24million to the city of Glasgow in 2014 to create a ‘Future Cities

Demonstrator,’ including building ‘a technology infrastructure to integrate city



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systems and data and deliver improved and responsive city services,’ which would

be ‘replicable by other urban areas.’ At the centre of all these activities is a re-

imagining of the digitized future of urban environments, with cities positioned as

vast ‘sensing environments’ in which key aspects of urban governance, such as

managing traffic flow and surveillance systems, are delegated to a dense

infrastructural mosaic of sensor devices and networks, data collection technologies,

and forms of analysis enabled by algorithm-driven analytics packages. The future

smart city is emerging as a ‘code/space’ (Kitchin & Dodge 2011) which is augmented

and even co-produced through software packages, computational practices, and

coded infrastructures.

Within many smart city programmes, themes such as ‘smart education’ and ‘smart

learning’ are emerging as important points of focus for various kinds of imaginings

and product developments. Educational research has, to date, not responded to the

challenges of such data-intensive, spatially sentient and programmable cities, or to

the various related commercial and governmental programmes sponsoring their

development. Yet various organizations and actors have begun to produce materials

envisaging education as a smart, software-mediated, and computationally-

programmable social institution situated in new digitally-mediated urban

infrastructures. For example, IBM has a ‘Smarter Education’ programme while

Microsoft’s CityNext initiative includes an ‘Educated Cities’ vision. In such

imaginings, schools and classrooms are positioned as highly software-mediated

spaces and as ‘data platforms’ webbed into distributed networks where the flow of

code and data through the environment can be utilized to remember, correlate,

anticipate and animate movement and activity. The imagined smart school is a

software-mediated spatial environment governed in real-time by coded and

increasingly automated processes of data collection, analysis, visualization and

feedback—an environment that appears more and more ‘sentient’ and ‘knowing’ as

data collection, analysis and feedback mechanisms are built-in to its operational

infrastructure. Many of the software products circulating in education, whether in

schools, universities, or lifelong learning activities, are hybrid products of both

computer code and of social codes of conduct (Mackenzie & Vurdubakis 2011) which

embody and materialize existing worldviews about social ordering, control and

governance. The code on which smart schools run are not just the technical ‘lines of

code’ known to computer science, but translations of ‘codes of conduct’ into the

functional and operational logics of software.



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The research

This article is an initial attempt to survey and critically consider some of the ways in

which education is being imagined as a smart social institution running on an

infrastructural urban substratum of sentient technologies. It addresses the question

of how discourses and practices of education governance are to be translated into

code in fabrications of the smart school. In the smart school, students, curricula,

facilities and administrative processes are to be managed and governed by code.

Through a fairly wide-ranging survey, my aim is to consider what schools might

become and to think about the role of code and data in the production of such

places—rather as Crang and Graham (2007) have surveyed the emerging

technological and discursive developments, as well as the surrounding imaginaries,

that constitute ‘sentient cities.’ Overarchingly, my interest is in how discourses and

imaginaries about the future of education, especially aspirations about its future

reform, might be translated into software—that is, how aspirations to the

governance of schools might be delegated to technical means—and how, in turn, this

software might alter the governance, management and organization of education.

Methodologically, I set out to survey some of the various connections between

social, material, discursive and technical elements that are beginning to constitute

the infrastructural nervous system of what I term ‘smart schools.’ Smart schools are

an emerging entity of smart cities, and in an important sense are not actually-

existing spaces but imaginary spaces being marked out and made intelligible

through particular discursive and material means. Smart schools are one example of

what Rose (1999) has termed ‘fabricated spaces’ that are delineated and made

intelligible by being ascribed particular characteristics and component parts through

discursive and material mechanisms, each produced and promoted within particular

social contexts. Fabricated spaces in this sense act as models or diagrams to which

certain actors hope to make reality conform. In the article, what I want to consider

are the various interacting technical, social and discursive elements of those spaces

now being fabricated as smart schools. Smart schools are being fabricated through

the technical interaction of devices, information, data, algorithms and code; the

social interaction of actors, groups and organizations such as software and hardware

producers, governmental agencies, commercial companies, and civil society

organizations; and the discursive layering of texts, documents, visualizations,



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materials and imaginaries, each produced and circulated by various promoters of

technologies, and further promoted through ‘futurist’ thinking in education. These

technical, social and discursive interactions all combine to produce the ‘fabricated

space’ of the smart school—that is, all the various ways in which smart schools have

been demarcated, delimited and delineated, ascribed characteristics of sentience,

awareness and ‘knowingness,’ and populated with code and data through the

promotion of particular software packages and hardware devices. The emerging

fabricated space of the smart school acts as a model for the future organization of

education as a social institution.

While it is certainly possible to locate specific documents and texts where smart

schools are discursively constructed (such as commercially produced ‘white papers’

and web resources), and to conduct forms of discourse analysis on those materials,

these are not my only source in the article. Instead, I cast a wider net over recent

documents, publications, websites from a variety of organizational sites, including

think tanks, academic institutions, the media, governmental departments and

commercial companies, where schools are being demarcated as the target for

reformatory ambitions which are informed by a particular imaginary of the potential

of new technology to afford administrative, pedagogic and pastoral benefits.

Ultimately, my argument is that the fabrication of the future of schools, and the

imaginary of smart schools in particular, is infused with aspirations to make them

more computationally amenable to monitoring and management.

In developing this argument, I draw on concepts of governance, and in particular

develop the notion that smart schools are those fabricated spaces that are to be

governed through technologies that run on an infrastructure of code and digital

data. I draw here on the work of Foucault (2007) on ‘governmentality,’

conceptualized as the interlocking rationalities, institutions, procedures, techniques

and organized practices by which particular ways of thinking about ‘how’ to govern

a society, or how to govern the behaviour and conduct of one’s self and others, are

articulated. As Miller & Rose (2008) have documented, distinctive forms of

governmentality are historically, discursively and materially constituted. The

governing of contemporary ‘advanced liberal societies’ is one that relies on complex

procedures of collecting, counting and classifying data about individuals in order to

regulate and manage society as a whole; a style of governing that Foucault (1998:

139) termed ‘a biopolitics of the population.’ It is based on a rationality of regulatory



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controls, constant surveillance, and ‘seeing’ and ‘knowing’ the activities of each

individual member of the population in order to govern them appropriately, and on

the belief that individuals and their behaviour and conduct are best governed

through inculcating capacities for self-management and self-fulfilment rather than

through commanding obedience to punitive disciplinary techniques (Dean 2010).

These recent theories of governing attempt to get past the discourse of domination

and control associated with accounts of ‘ideology.’ Instead, as Rose (1999: 4)

articulates it, ‘to govern is to act upon action. … To govern humans is not to crush

their capacity to act, but to acknowledge it and to utilize it for one’s own objectives.’

A key role in such practices of governing is ascribed to technology. Rose (1999) refers

to the idea of a ‘technology’ as a complex of forms of knowledge, practical

techniques, textual artefacts, discursive materials, objects and devices and so on that

are brought together and imbued with aspirations to shape conduct in some way.

Rose sometimes uses the term ‘human technology’ to make clear that his focus is on

‘human capacities that are to be understood and acted upon by technical means’

(Rose 1999: 52). Cast in the educational context, it becomes necessary to look at the

variety of techniques installed within schooling to achieve these ends. Thus the

‘technology of schooling’ in any specific context consists of particular pedagogic

knowledges, civilizing aspirations, techniques of discipline and organization,

professional standards and obligations, mental exercises, schoolrooms of a certain

design, material and technical infrastructures, textbooks and other discursive

products, all of them infused with the aim of shaping and inculcating particular

forms of conduct (Rose 1999: 54). In the current context, it is necessary to add that

digital data and software code are part of a technology of schooling aimed at

understanding and acting upon students’ capacities.

The paper, then, develops some initial understandings about the digital

infrastructures underlying emerging techniques of educational governance and the

devices mobilized pedagogically to intervene in the governing of learners’ conduct.

Smart schools are fabricated spaces in which aspirations to manage and govern

students are to be translated into software and its underlying code and algorithmic

routines. In this sense, we can modify the expression that ‘to govern is to act upon

action’ to say that to govern now means that code acts upon action.



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Conceptualizing smart cities

While ‘smart cities’ has become a relatively popular discourse among urban

developers, politicians and technology businesses, as already noted, a more critical

response to these urban developments has emerged among geographers and

sociologists. Drawing on a review of the commercial literature, Gabrys (2014)

characterizes ‘smart cities’ as those spaces enabled by automated infrastructures

equipped with networked digital sensors and ubiquitous computing; spaces that

provide augmented experiences through mobile devices; spaces that mobilize the

capture and analysis of ‘big data’ from urban processes in real-time; and spaces in

which citizens are positioned as data points and data-gathering nodes—all of which

are infused with the aspiration to orchestrate and manage particular urban processes

and ways of life through computational logics of programming. Smart cities are thus

‘programmable environments’:

[T]he programming of environments … is generative of political techniques for governing

everyday ways of life, where urban processes, citizen engagements, and governance unfold

through the spatial and temporal networks of sensors, algorithms, databases and mobile

platforms that constitute the environments of smart cities. (Gabrys 2014: 44)

In addition, Kitchin (2014) specifically identifies the increase in automated data in

the ‘real-time’ ‘instrumented city,’ including automated forms of surveillance data

enabled by sensors, trackable ‘smart cards,’ RFID tags, and automatic recognition

systems; voluntarily ‘gifted’ data such as that generated by social media interactions;

and objects and machines that conduct automatic work and are part of the ‘internet

of things’:

These forms of instrumentation provide abundant, systematic, dynamic, well-defined,

resolute, relatively cheap data about city activities and processes, enabling the possibility of

real-time analytics and adaptive forms of management and governance. (Kitchin 2014: 5)

The ‘real-time city’ described here is one in which urban data analytics provide

powerful means for making sense of and managing urban life, and for envisioning

and predicting future scenarios for which pre-emptive plans can be formulated and

enacted. Elsewhere, Kitchin (2011: 946) argues that the smart or ‘programmable city’

depends on two key processes: (1) translation: how cities are translated into code; and

(2) transduction: how code reshapes city life. In the process of translating the city into

code, for example, Kitchin lists processes such as the capturing and processing of

digital data about citizens; the transformation of discourses and practices of city



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governance into code; and the political economy of software production that shapes

coding practices. In terms of transduction, he asks how software is then developed to

drive public policy development and implementation; how software is used to

regulate and govern city life; and how software alters the nature of working and

daily living. Through this double process of translating the city into code, and the

resulting software then altering the city, the smart city becomes an example of what

Kitchin and Dodge (2011) have termed a ‘code/space,’ a space that is through and

through organized by a ‘coded assemblage’ of network infrastructures, computing

devices and software packages, and that ultimately relies on code for much of its

functioning. Such spaces then become ‘programmable’ to the degree that software

systems actively alter tasks, reshape behaviours, and regulate and govern city life—

they are spaces structured and supported by software code, without which they

cease to function as planned.

A key development in the emerging literature on coded urban environments, as

taken to the extreme by many smart city visions, is the notion that the space itself is

becoming embedded with a kind of ‘intelligence.’ Thrift (2014b: 1264-65), for

example, writes of an emerging urban space ‘with a life of its own,’ in which every

surface is overlaid with data, in which every surface can speak and independently

represent itself, and where ‘each and every situation will be haunted by its data

analogue.’ Such thinking goes beyond notions of the ICT-enabled smart city to a

notion of a ‘sentient city.’ Crang and Graham (2007) describe the sentient city as an

urban environment embedded with ‘ambient intelligence’ through the use of

ubiquitous computing, data collection platforms, sensors, and so on. These are cities

that ‘think of us, where the environment reflexively monitors our behaviour’:

Urban ubiquitous computing systems entwine people, place and software in complex ways.

Software and algorithms code people, places and their data in interrelated systems that are

then used to profile and drive decision-making systems. (Crang & Graham 2007: 792)

These sentient urban environments identify, profile, categorize and ‘code’ people

and places, and then layer and network them through software algorithms to larger

categorical renderings stored in computer memory. Thrift (2014a: 8-9) identifies five

coalescing tendencies in the notion of the sentient city:

(1) the prevalence and profusion of ‘big data,’ as enormous quantities of information about

urban processes and ways of living are collected, analysed, visualized and acted upon;



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(2) calculation is becoming more performative, as data is fused and associated to produce norms

inferring what a population might be, its proclivities, possibilities and potentialities;

(3) machine-to-machine interrelation and collective automation, as entirely non-human

conversations are conducted between billions of things in cities, functioning without direct

human perception;

(4) the expansion of sensors, as movement, touch, smell and sound can be loaded down into all

manner of everyday objects which are programmed to then ‘nudge’ people towards desired

choices and behaviours;

(5) the growth of ambient environments, as location-based and context-aware media and devices

(e.g. Google Glass) transform people’s behavioural patterns in urban environments.

Through the combination and interaction of these elements, the sentient city is

becoming an urban environment with a computational ‘nervous system’ (Townsend

2013).

Such augmented spaces thus appear to have some form of awareness, intelligence,

and capacities for recursive thought, along with some ability to learn and to

transform themselves. However, as Thrift (2014a: 10) cautions, ‘considerable care

needs to be taken in making claims to urban sentience’ or to assume ‘that the city is

aware of itself in any human way’:

As computational objects have developed, cities are able to take on new forms of vitality….

[Sentient cities have] only gradually arisen, line by line, algorithm by algorithm, program by

program. Cities are full of a whole new layer of emergent entities which, because they are

underpinned by code using data as fuel, might be thought of as akin to sentient beings….

[T]he degree and type of awareness of an environment that a city manifests can increase as its

environment is progressively augmented by more and more information and

communications technology.

This degree of sentience is not powered by some all-seeing artificial intelligence, but

much more mundanely through the linking and connection of multiple technologies

into vast networked infrastructures. Through the linking and layering of data in vast

datasets, urban infrastructures are equipped with the power to analyse urban

processes and population behaviours, often automatically, and increasingly to

mobilize that intelligence to make predictions about future processes and

behaviours. Such predictions can then be used to prescribe particular kinds of urban

modifications or to trigger specific interventions automatically. It is to this degree

that the smart city appear to be sentient, as having qualities of intelligence,

responsiveness and adaptivity, with ‘a life of its own.’



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Indeed, a powerful new form of governance is in evidence in smart cities, one that

Kitchin and Dodge (2011) term ‘automated management.’ They describe automated

management as a form of governmentality—a way of thinking about how to

manage, organise and regulate individuals and society as a whole. Automated

management as Kitchin & Dodge (2011: 85) describe it refers to practices of

governing and regulating people and objects through processes that are increasingly

automated (technologically enacted), automatic (the technology performs without

prompting or direction), and autonomous (enacted without human oversight).

Through automated, automatic and autonomous processes, software is being

mobilized both to transform regimes and practices of surveillance and to instil a

stronger regime of discipline and behaviour change. The nature of such technologies

is that it is software operated and automated, dynamic enough to respond and

regulate in real-time, and has the capacity to act predictively. The result is that

‘many aspects of social and economic life are now captured, processed, and

governed to a significant degree by software (on behalf of state agencies, companies,

and also individuals themselves)’ (Kitchin & Dodge 2011: 110).

In the rest of this article, I argue that a form of governance associated with the idea

of smart, sentient cities is now being embedded in imaginaries and discourses of

smart schooling. In what follows in the next sections, I provide a survey of some of

the key technical, social and discursive elements being assembled in imaginaries of

the smart school—their infrastructure, databases, networks, sensors, visualization,

operatives, and analytics—and consider how these combine as an emerging

assemblage of techniques of governance: a technology of schooling for the smart

school.

Infrastructure

Like smart cities in general, the imaginary of the smart school depends on technical

infrastructure. In simple terms, an infrastructure is the physical, material and

organizational structure that underlies and orchestrates social, political and

economic life. Infrastructures consist of massive technical systems such as the

telecommunication and informational networks of electronic communication, energy

and power networks, water and waste networks, the networks of transport and

travel, each of them underpinned by dense thickets of standards, protocols and



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classification systems, and which are increasingly coordinated by computer

programs, software, algorithms and code that define how they should function. As

Bowker and Star (1999: 35) have defined it, ‘infrastructure is sunk into, inside of,

other structures, social arrangements, and technologies’; it is a historically worked-

out set of technologies, routines, conventions of practice, and organizational

structures. An infrastructure is not merely a technical system, but constituted

through the relations between many interlocking technical, social and discursive

elements. This is akin to what Kitchin and Lauriault (2014: 6) have termed a highly

relational ‘data assemblage’:

a complex socio-technical system, composed of many apparatuses and elements that are

thoroughly entwined, whose central concern is the production of data. A data assemblage

consists of more than the data system/infrastructure itself, such as a big data system, an open

data repository, or a data archive, to include all of the technological, political, social and

economic apparatuses that frames their nature, operation and work.

Such relational assemblages are both technical infrastructures, built on data

technologies and associated software packages; and human infrastructures,

requiring new kinds of knowledge workers, designers, engineers and so on. The

emerging technological and electronic infrastructures of our present time therefore

consist of classification systems, standards and protocols which are produced by

computer and information scientists and materialized in the form of functioning of

the software and networks we use. But they also consist of built-in political and

social assumptions or aspirations that can inform the social and moral order, craft

people’s identities, order human interaction, and valorize or silence particular points

of view, so that ‘seemingly purely technical issues like how to name things and how

to store data in fact constitute much of human interaction and much of what we

come to know as natural’ (Bowker & Star 1999: 326).

A study of contemporary technical infrastructures by Beer (2013: 23) suggests that

they are the ‘material instantiation or embodiment of some wider social and political

movements.’ All of these infrastructural networks increasingly rely on vast software

systems for their functioning, so that as Beer (2013: 25) claims software is now

‘sinking’ into the ‘taken-for-granted background of our everyday lives.’ They

constitute what Thrift (2005) has termed the ‘technological unconscious’ of

contemporary life. These invisible infrastructures are the background networks to

which many of our everyday technical devices and the software programmes we use

are now connected. In turn, such devices as laptops, smartphones, iPads and so on,



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are now increasingly able to monitor and record their own use. Consequently, such

devices are becoming ‘active in capturing aspects of our everyday lives’ (Beer 2013:

20). The kind of environment we can see emerging here is one populated by

increasingly ‘smart’ devices connected to ‘lively’ background infrastructures, with a

constant back-and-forth flow of information and data between them facilitated by

computer code. New practices of governing are increasingly being mediated through

these dense interconnections of infrastructures and devices and the code that

instructs them. Software code, Thrift (2005: 172-73) argues, is becoming a ‘key

technology of government’ and integral to the practices of governing as ‘adaptive

standards of conduct’ which can ‘direct how citizens act.’ It is in the context of such

infrastructures that the notion of the ‘smart city’ has been developed. Without

‘lively’ underlying infrastructures, smart cities could not function as intended.

The fabrication of smart schools, too, relies on infrastructural architectures, such as

the vast circuits of wiring required to link up devices and networks, and all their

associated standards and operating systems; wifi networks that enable mobile

connectivity; closed-circuit surveillance camera networks; operating systems and

software packages that are distributed across classrooms and offices according to site

license agreements; the data management and storage systems that facilitate the flow

of data from schools to other sites, and so on. These infrastructures of the smart

school are, in turn, nested within and linked up to wider infrastructures, such as the

subterranean broadband cabling that connects it to internet, email and telephony

providers; the commercial social media networks that facilitate social interaction, the

sharing of digital artefacts, and the storage of digital data in the ‘cloud’; and the

global web services such as Google that facilitate the search and retrieval of

information.

The infrastructure of the smart school, then, partly consists of the massive technical

systems that enable the connection and communication of all the other technical

components described in the sections below. But if we take the relational view, then

we need to consider here the various social and human elements of which an

infrastructure consists too. Here we might list the commercial organizations like

IBM, Cisco and Microsoft that promote smart education visions through various

discursive materials, resources and websites. Governmental and semi-governmental

intermediaries that promote educational aspects of smart cities programmes would

also form part of the social and human infrastructure of smart schools. These include



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organizations like Nesta that describes its ‘digital education’ projects as contributing

to the smart cities agenda (Mulgan 2014). Others, such as consultants, media

pundits, academics, architects, software programmers, and educational data

scientists are also part of the infrastructure supporting the development of this new

space of the smart school.

As a sociotechnical assemblage of human and technical interactions, infrastructure

provides the discursive, material, and technical foundations, as well as the social,

economic and political framing, for emerging notions of smarter education, smart

schools, and smart learning.

Databases

A key feature of emerging models of smart schools, like the smart cities they belong

to, is their dependence on massive sources of digital data, or ‘big data.’ A good

example of such thinking is a report from the Chicago Council of Global Affairs

(2014) focusing on big data in the organization and governance of a future

‘megacity.’ The report offers a vision of a future urban landscape in which the data

generated by residents and city operations, twinned with the ability to collect,

analyze, and utilize data for decision making, is increasingly enabled by the

prevalence of personal devices, increased connectivity, accessibility to high-

performance computing and storage, and advanced analytics. An entire chapter of

the report focusing on big data and education in such a data-driven megacity

concludes that ‘harnessing data mining and applied analytics, big data in education

can greatly increase the quality of instruction, monitoring, evaluation, and

accountability.’ The report closely aligns the data-driven functions of the megacity

with the data-driven functions of the school; indeed, they are in a symbiotic

relationship, with schools as sites for the collection of data that can be fed into the

planning processes of urban managers and educational policymakers.

What might such a big data-driven school look like? The authors of Learning with Big

Data: The Future of Education (Mayer-Schönberger & Cukier 2014) suggest big data

will ‘reshape learning’ through ‘datafying the learning process’ in three significant

ways: through real-time feedback; individualization and personalization of the

educational experience; and probabilistic predictions to optimize what students

learn. These changes are being brought about, they argue, through a combination of:



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online courses that enable the constant logging and tracking of learners through their

clickstream data

e-textbooks that can ‘learn’ from how they are used and ‘talk back’ to the teacher

adaptive learning systems that enable materials to be tailored to each student’s individual

needs through automated real-time analysis

the generation of personalized ‘playlists’ determined by an algorithm

new forms of data analytics that are able to harvest data from students’ actions, learn from

them, and generate predictions of individual students’ probable future performances

The publication provides a seamless image of school as a ‘data platform,’ the

‘cornerstone of a big-data ecosystem,’ in which ‘educational materials will be

algorithmically customized’ and ‘constantly improved.’

These representations of schools as connected nodes in vast networked data

ecosystems reflect a longer trend identified by education researchers in the use of

data to enable new forms of governance and control (Lawn 2013). Already, through

the mobilization of digital data technologies and practices, education is being shaped

as a contemporary system of governing that utilizes the dynamic collection and

analysis of data to predict, target and reshape behaviour. As Grek and Ozga (2010)

argue, education is not governed so much as a national system, but through the

constant collection and calculation of data on individuals. Indeed, this constant

capturing of data makes it possible to monitor individuals’ performances and to

predict or forecast their future needs, as well as to measure and manage schools at

local and national scales of analysis. The requirement for ever-more data is closely

aligned with technical developments in database storage and analysis technologies.

The governing and managing of education is attached to the large scale

infrastructural capacities of data servers, database software developments, data

mining, and visual data presentation techniques, as well as to new forms of

technical, methodological and graphical design expertise. Embedded in a dense data

assemblage of infrastructures, technologies, agents, and practices, and rendered

from there as a vast surface of machine-readable data traces, education has been

made amenable to being effortlessly and endlessly crawled, scraped and mined for

insights.

The image of the smart school as a data platform in a massive urban big data

ecosystem resonates strongly with images of the smart city as a ‘real-time’

environment (Kitchin 2014) that appears more and more sentient and ‘knowing’ as

instantaneous processes of data collection, analysis and feedback mechanisms are



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built-in to its operational infrastructure. Big data practices have become powerful

sources of contemporary educational governance as the scale, diversity, and

relationality of educational datasets has grown. The data is now also being projected

from there out into the pedagogical apparatus of the classroom and into educational

policy programmes. Ultimately, the management of education today is being

accomplished by making children enumerable and machine-readable as data that

can be processed by database packages and understood through mediating

techniques of data presentation and visualization.

Networks

Smart schools are also envisioned as being highly socially networked, with learners

participating in a socially connected ecosystem of learning at home, at school and

online. Visions such as Microsoft’s Educated Cities program, for example, exemplify

a perceived symmetry between networked technical infrastructures with the

apparently natural sociality of people. Its Educated Cities white paper emphasizes

the social power of mobile devices and social media platforms to enable dialogues

and open new ‘social channels,’ as well as the use of ‘social tools’ to foster

collaboration (Microsoft CityNext 2014). The student of a Microsoft Educated City is

addressed as a social learner participating in new digitally networked social

configurations. It is based on contemporary preoccupations with open education,

learning in the cloud, smart mobs, collective intelligence, participatory cultures and

so on, and counterposes traditional schooling with the smarter possibilities of open

networks, interconnected systems, interactivity and participation facilitated by social

media networks.

Smart school visions like this position the social network as a model for reimagining

learning (Williamson 2014a). The algorithmic mosaic of calculations and connections

constructed by programmers that actually constitutes a social media network is

rendered invisible by these claims. Instead, it appears that social media enables

people to engage in ‘natural’ social learning activities. These conceptions are

strengthened by expert claims about the ‘social brain,’ ‘social learning’ and ‘social

networks’ that emphasize the social nature of the individual rather than

individualist self-interest—claims that resonate well both with the commercial

companies that produce social media networking sites and with the aims of



16

government (Davies 2013). Indeed, in this sense the technical development of the

‘social network’ has mutated into a social explanation of human behaviour and

action, one with significant political implications as politicians, policymakers and

urban administrators increasingly seek insights on human behaviour from mining

social network data in order to seek to influence, shape and modify their future

actions. Rose (2013) has described how the technical infrastructures of the present,

configured by computer code and algorithms, are enabling ‘rapid, agile governance’

through ‘scraping’ and ‘mining’ users’ ‘digital traces,’ compared to the cumbersome

modes of governing through official state administration or corporate bureaucracy.

New practices of governing are emerging by which people and populations are to be

monitored and managed through their social networks, all infused with the

aspiration to shape the ways they act and conduct themselves. The social network

applications of the smart school, then, must be viewed as techniques enabling city

administrators and urban policymakers to generate ‘insights’ about learner

behaviour, which may be rationalized as part of a new social explanation of human

nature.

Claims about human social nature circulating in the context of social media

networks are as politically motivated as they are psychologically or neurologically

rooted. The idea of the malleable ‘social brain’ of the ‘social learner’ connected to

others via ‘social networks’ is not natural and pre-given. It is an expertly constructed

accomplishment based on a particular theory of the brain and human behaviour,

practised in places like Facebook’s ‘data science team,’ which presupposes people

are naturally imitative and therefore manipulable and susceptible to social

influences. Such assumptions are based partly on populist psychological claims

about our natural propensity to social influence, on neuroscientific understandings

of the brain’s neural plasticity, mirror neurons and malleability, and on claims that

our brains consist of neural networks connected to, and shaped by, social networks

and their effects (Rose & Abi-Rached 2013). As Pykett (2013) has argued, the

cybernetic ideal of smart cities is one in which user behaviour can be predicted and

modified, and which therefore relies on particular neuroscientific theories of human

behaviour that can be used to inform the planning or urban services. These ‘social’

theories of human behaviour, social cognition and the social brain suggest that the

human brain is specialized for collective life, and imply that social media networks

are merely facilitating our natural qualities of sociality, rather than actively shaping

social relations, social interactions, and conceptions of what the ‘social’ actually



17

consists. Seemingly natural human qualities of imitancy, malleability and

susceptibility to being socially engineered through social networking effects are

precisely what Facebook’s controversial ‘emotional contagion’ study exploited

(Tufekci 2014).

Within the smart school, then, learners are to be addressed and managed as

malleable and imitative social learners with social brains, whose behaviours and

actions can be nudged, tweaked and improved by their participation in social

networks and social interaction. The smart school may in this sense also be a

‘cerebral school’ planned around ostensible insights from the brain sciences. The

cerebral school is not only a sentient environment, but one where insights from the

brain scan have been translated into the school plan.

Sensors

Another key feature of smart schools will be that they become ‘sensing

environments’ in which a wide variety of sensor devices will constantly capture

information about school facilities, administrative processes, and the behaviour,

progress and movement of students. In particular, smart schools are likely to be the

site for a range of surveillance technologies, including automated attendance

registers, ID cards, face recognition-enabled CCTV networks, and a range of

techniques of ‘dataveillance’ (Raley 2014) utilizing sources of digital data to trace

and track students’ movements and activities. Notable examples of the kind of

sensors enabling these forms of data-based surveillance of behaviour include

wearable electronics and biosensor devices designed to allow users to track, collect

and analyze data on their own activities and health—sometimes called technologies

of the ‘quantified self’ or the practice of ‘wearing the self’ (Evans 2014).

A market in such devices is now being targeted for use in health and physical

education in schools. Such products provide a glimpse of the digitized future of

health and physical education, which is likely to mirror the wider development of

data-driven smart schools in which data tracking, sensing and analysis, facilitated by

software and data analytics algorithms, will increasingly influence and shape

administration, curriculum, pedagogy and assessment. For example, Sqord consists

of a wearable data logger, an online social media environment and a personalizable

onscreen avatar called a PowerMe. Sqord is marketed as ‘one part social media,



18

onepart game platform, and one part fitness tracker’. Extensively piloted and tested

in schools in the USA, Sqord is targeted firmly at the physical education market:

Sqord gives you an administrative reporting tool with quantifiable metrics on the physical

activity, levels, and participation of each of your players. No more guesswork or gray areas in

measuring physical activity. Sqord puts the numbers in plain view, and allows your teachers

and coaches to see exactly what’s what in real-time. (Sqord, 2014)

Sqord users can compete with one another on an online leaderboard through

everyday physical challenges, as measured by their activity trackers, and are able to

win medals and ‘sqoins’ as rewards for completion of goals, which can be used to

purchase upgrades and personalized features. The Sqord social media environment

promotes peer competition as a motivational technique. Sqord also provides an

administrative reporting tool for educators to access metrics on the physical activity

levels and participation of each child player. As smart schools become sensing

environments where data collection devices are worn on the body the functioning of

such devices will interlace with school pedagogies and influence how students learn

about their own bodies and health.

Products like Sqord represent a convergence of devices, software, apps, techniques

and discourses of self-quantification with pedagogic practices, surveillance

techniques, commercial imperatives and governmental health agendas. This hybrid

mix of pedagogic technologies and modes of self-management is ordered and

organized (at least partly) by underlying algorithms and their in-built models of the

body in order to make the health of the child amenable to measurement and

management. Through such technologies, the student’s body is being redefined as a

kind of programmable software that can be de-bugged, upgraded, patched and

optimized. Metaphorically speaking, the quantified student is growing an

‘algorithmic skin’ (Williamson 2014b), an artificial informational membrane that

continually interacts with, and is activated by, a densely coded informational

environment.

Taking such technologies as a model for how sensor devices might permeate

education, in the smart school, the body and behaviour of the student will become

amenable to being predicted and modified by continually being surveilled and

‘sensed.’ The tracking technologies described above indicate how schools might

become increasingly sentient sensing environments, in which bodies, their

placement, their movement and their activity become further data points to fuse



19

with existing datasets, including datasets on learners’ assessment performances and

pastoral needs, but also wider datasets about the learning environment, the quality

of pedagogy, and overall institutional performance as recorded on multiple matrices.

While sensors worn on the body are the most visible manifestation of this trend,

smart schools are sentient environments in which sensing is to become a ubiquitous

surveillant reality.

Visualization

Smart cities are regularly envisioned and displayed through processes of graphic

visualization. While this is clearest in ground-up smart city developments such as

Songdo in South Korea, it is also prevalent in developments such as the Glasgow

Future Cities Demonstrator project which seek to regenerate existing cities as

smarter environments. The kinds of visualizations encountered in such

developments include computer-generated images of buildings and urban spaces

produced by architects and city planners (Rose et al. 2014), as well as video

presentations of everyday life in the smart city intended for public consumption, of

course, but also include newer developments like ‘city dashboards’ that enable

citizens to access (and personalize) data visualizations of their cities. Many major

British cities have their own city dashboards, most notably London, and these are

increasingly the norm as part of the public presentation of the smart city. As

education is increasingly aligned with the data practices of the smart city, it too is

becoming the subject of sophisticated practices of visualization. IBM’s Smarter

Education web pages, for example, include an animated interactive presentation

visualizing the future of learning institutions in the smart city.

Researchers are now pointing to the political significance of data visualization both

in terms of its representational power and its techniques of production. The

visualization of data is no neutral accomplishment but amplifies the rhetorical or

persuasive function of data, allowing it to be employed to create arguments and

generate explanations about the world, and to produce conviction in others that such

representations, explanations and arguments depict the world as it really appears, or

at least how it might appear (Gitelman & Jackson 2013). As Kinsley (2011) claims,

visualizations can bring anticipated futures into the present, making future visions

seem actionable and realizable in the here-and-now. Thus, Beer (2013) argues that



20

researchers need to examine the actors involved in producing visualizations, ask

what data they are using, how those data have been formed, as well as ‘what

software is used in the analysis, what code or algorithms shape the data and the

visualization,’ in order to ‘treat these visuals seriously as they come to envision the

social world.’ These are highly technical acts performed in concrete social

circumstances. As Rose et al. (2014) have identified in their study of the visualization

of smart cities, any visualization produced using software and digital data is

ultimately ‘made’ as it circulates around a network of offices and computer screens,

as it is worked on by a variety of designers, visualizers, project managers,

programmers and data analysts, and as its digital file encounters various software

programmes and hardware devices. A visualization is an ‘interfacial site’ created

through networks of human bodies at work with various kinds of software and

hardware, facilitated by vast repositories of code and databases of fine-grained

information, thus highlighting

the changes wrought to meaning-making by the emergence of digital networks through

which data are constantly mobile, shifting and proliferating, moving between different actors

and media, ported and patched, altered and designed, collaged and commented on. (Rose et

al. 2014: 401)

The visualization and diagrammatization of the world described here is a complex

sociotechnical act involving a variety of actors and technologies with the persuasive

power to shape people’s engagement and interaction with the world itself. As Latour

(1986: 27-28) has argued, the power of any technique of inscription—processes that

transform reality into figures, visualisations, graphics, images, or diagrams—is to

stabilize complex ‘realms of reality’ in one place, ‘just inches apart, once flattened on

to the same surface,’ so as to measure and modify what is ‘out there.’ Visualizations

are material techniques of thought and persuasion that enable reality to be evaluated

and acted upon.

Visualizations of smart forms of education, such as those presented on the IBM

Smarter Education and Microsoft Educated Cities webpages, can thus be seen as

‘interfacial sites’ through which different views and visions of education are

constantly being composed and compared, altered and modified, developed and

designed in order to render certain kinds of meanings and arguments possible. Such

techniques of inscription turn schools into ‘particular realities’ that can be invested

with meanings that ‘make sense’ and can be acted upon in different ways



21

(Decuypere et al. 2014). They guide user interpretation and produce conviction

through the ways they flatten and compress extraordinary complexity into

simplified and seductive visual presentations. This visualization of smart schools

makes present schools actionable through the sociotechnical production and

stabilization of specific kinds of views of what education and learning could and

should be.

Operatives

Many smart city programmes feature a strong emphasis on the idea of ‘smart

citizens.’ The basic logic is that the economic, cultural and political functioning of

smart cities will rely on smart people. There is a requirement in such urban

environments, then, that people can help contribute to the monitoring and

management of the city itself. Recent research from the Citizen Sense project, for

example, suggests cities are now becoming more like ‘datasets to be manipulated,’

with citizens as ‘operatives’ with responsibilities for ‘operationalizing the cybernetic

functions of the smart city. …The citizen is a data point, both a generator of data and

a responsive node in a system of feedback’ (Gabrys 2014).

What kinds of pedagogies contribute to the production of such a smart

computational citizen? One way in which smart citizens might be shaped as

computational operatives of the smart city is by learning to code. Learning to code

acts as a kind of preparation for citizenship in a city where people are required to

develop the computational skills required to become operatives, engineers and

hackers of the smart city’s services and urban processes. The evidence for such an

idea is in how various learning to code programmes and clubs have been aligned

with emerging ‘civic technology’ and ‘coding for civic service’ initiatives by

organizations such as Nesta (Bell 2014). An important book on smart cities by

Townsend (2013) emphasizes the role of ‘civic hackers’ in the creation of citizen-

centred urban services. These initiatives and publications assume that many

problems of urban management and control can be solved through the application of

technical solutions and computational forms of thinking.

A striking example of how learning to code, civic hacking and smart cities are

conjoined is provided by the Future Makers programme, part of Glasgow’s major

£24million Future City initiative. As part of its Open Glasgow scheme, including a



22

major smart city data operations centre, the Glasgow Future City vision emphasizes

the ‘literacies’ required by Glasgow’s citizens to participate in and contribute to the

running of the city. In order to promote these smart city literacies, the Future Makers

programme provides an ‘innovative coding education programme’ to develop

programming and coding skills among young people (Open Glasgow 2014). Future

Makers consists of coding clubs and workshops all aimed at enabling young people

to help shape and sustain the Future City. Related activities in the Glasgow Future

City include ‘hack days’ putting citizens, programmers, designers and government

staff together in teams to focus on coding citizen-centred solutions to urban

problems. Future Makers thus acts in part as a pipeline ensuring that young people

are equipped with the relevant skills of coding and computational thinking to help

‘hack’ the future of the smart urban environment.

As such, learning to code is part of an emerging style of ‘political computational

thinking’ (Williamson 2014c) familiar to many smart city visions, which recasts

complex social phenomena like politics, public health, and education as neatly

defined problems with definite, computable solutions that can be optimized with the

right code, algorithms and urban literacies. This form of ‘technocratic governance’ in

smart cities (Kitchin 2014) requires citizens to learn to code in order to help

programme the city and all its urban services.

Analytics

Smart schools are in many ways techno-utopian futurist fantasies. But they reflect an

emergent socio-technical reality in which data tracking, sensing and analysis,

combined with particular behavioural models and theories of the social brain, are

increasingly influencing educational administration, curriculum, pedagogy and

assessment. Smart schools will mobilize the constant collection and connection of

data as a form of artificial sentience, making every aspect of school performance into

a real-time process of data collection, analysis and feedback. These activities are part

of a wider concern with future-thinking in education that is influenced by the

technical capacity of computational statistics, machine learning algorithms and

predictive analytics to project probabilistic predictions of future actions (Mackenzie

2013). Predictive tracking and sensing technologies include learning analytics

platforms that can track students’ data over time, link them to behavioural models,



23

and then combine those data to project likely future progress, actions, and outcomes.

New kinds of ‘educational data scientists’ (Pea 2014) and learning analytics

companies like Knewton claim to be able to collect and mine millions of data points

on students, with more data on students than Google has on its users. According to

its chief executive, Knewton is based on a combination of ‘low-cost algorithmic

assessment norming at scale’ along with ‘sophisticated database architecture and

tagging infrastructure, complex taxonomic systems, and groundbreaking machine

learning algorithms’(Ferreira 2014).

A clear example of how such analytics capacities may be embedded in smart schools

is provided by the IBM Smarter Education initiative, part of its global Smarter Cities

and Smarter Planet agendas. The IBM ‘smarter classroom’ is a ‘classroom that will

learn you’ through ‘cognitive-based learning systems’ and through the mobilization

of both predictive and prescriptive analytics. Predictive tools, IBM claims, can

answer the question: based on what’s already happened, what’s going to happen

next? And prescriptive analytics then answer: in light of what we believe is going to

happen, what is the best response?

These two dimensions of smarter analytics enable educational leaders to detect patterns that

exist in masses of data, project potential outcomes and make intelligent decisions based on

those projections. (IBM Smarter Education 2014)

The smarter classroom exemplifies how smart schools will become able not only to

provide real-time data on student activities, but also to make ‘future-tense’

predictions of their likely outcomes and to prescribe automated interventions that

might nudge their individual and social behaviour and so pre-empt their futures.

The IBM ideal of a ‘classroom that will learn you,’ enabled by cognitive-based

learning systems, also resonates with the notion that the sentient city is an ambient

intelligent environment that can ‘think of us’ as Crang and Graham (2007) have

memorably phrased it. But this begs important questions. What models of human

cognition underpin cognitive-based adaptive learning systems? How exactly have

these kinds of sentient classrooms been programmed to learn? What machine

learning techniques are mobilized to enable such processes? Processes such as

machine learning rely on adaptive algorithms and statistical models that can be ‘fed

training data’; these are, crudely speaking, ‘taught algorithms’ that can learn from

being taught with example data (Gillespie 2014). Clearly there are important

questions to address about the selection of the training data that the algorithm is



24

expected to learn from. Just as educational sociology has always addressed the

question of how knowledge is selected for inclusion (or exclusion) in school

curricula, and how this might reproduce existing forms of social organization and

control, we might ask about the values and assumptions underpinning the training

data taught to machine learning algorithms, or excluded from it, and how this, too,

might reproduce particular assumptions or preferred models of social and political

order, as well as reinforcing judgements about desirable and undesirable

educational activities.

As educational data science methods and learning analytics become increasingly

powerful through the use of machine learning algorithms and predictive modelling,

smart schools will become important sites for the quantification and control of

students. The ‘quantified student’ is the product of a massive data-mineable

industry and the subject of smart schools where they are to be continually sensed

and identified through their ‘data points’ and subjected to continuous processes of

data monitoring, predictive modelling, real-time prescriptive intervention, and pre-

emptive pedagogic and pastoral practices.

Conclusion: Governing smart schools

By mapping the coded assemblages of databases, networks, sensors, visualizations

and predictive methods that are increasingly facilitating educational governance,

this article has sought to provide an initial indication of how educational settings are

becoming subject to emerging processes that are animated by a ‘sentient

infrastructure’ of real-time data collection, connection, surveillance, display and

anticipatory techniques. While such assemblages are by no means complete

‘technologies of schooling,’ they provide a sense of how new kinds of ‘smart schools’

are emerging: educational ‘code/spaces’ in which many aspects of administration,

leadership, spatial organization, student management, communication and even

pedagogy itself are to be mediated and governed by processes programmed in code.

What I have termed smart schools are imagined spaces undergirded by a dense

infrastructural mosaic of devices, data, discourses and techniques, all combined as a

powerful set of processes that will ultimately make educational institutions and

processes more programmable. Smart schools are fabricated spaces in-the-making,

institutions with administrative, pedagogical and pastoral processes that are to some



25

extent being written in code. As the fundamentally performative layer of software

that does work in the world, code has become a key source of social power in

educational settings, processes and activities, by sociotechnically interlacing

algorithmic procedures with human actions and aspirations. Smart schools are

settings plastered with a surface of data in which code and all it facilitates have

made new kinds of ‘sentience’ possible.

Perhaps most significantly, smart schools will be schools that can ‘think of us’ and

both learn from and about us. Smart schools will be entirely hybrid environments in

which human and nonhuman things will be able to interrelate and learn from one

another—or that are at least designed around the premise of the social brain of the

learner interacting with the artificial sentience of the school. As people learn from

and about machines, machines will learn from and about people in the smart school.

This requires us to consider important questions about processes of governance. The

process of governing the conduct of learners relies on the accumulation of data about

those individuals, in order to make them knowable, visible and thus actionable. Yet

with the emergence of machine learning, cognitive-based adaptive learning systems,

and other artificially sentient systems, the data is to be captured, connected,

calculated and communicated through processes that increasingly exclude direct

human judgment or action. What exactly is it that such software has been

programmed to ‘learn’ from us, and about us, and on that basis what has it been

programmed to do with that information? Moreover, machine learning and

cognitive systems designed for use in education have been programmed into

existence by technical experts whose values, assumptions, systems of thinking, and

theories of the world may not be commensurate with those of educators. Thus the

governing of learners’ conduct in smart schools is to be managed at a distance

through the expertise of programmers and data scientists, whose understandings

and theories of learning and education are encoded in, and enacted by, increasingly

autonomous algorithm machines. In this sense, if governing is to ‘act upon action,’

then in the emerging smart school it is the case that code acts upon action.

Acknowledgement

The research informing this paper has emerged from the Code Acts in Education programme funded

by a grant from the Economic & Social Research Council.



26

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