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Sentient schools? Education in the smart city Ben Williamson University of Image: Upgrade Studio,

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1 Sentient schools? Education in the smart city Ben Williamson University of Image: Upgrade Studio,

2 The old city of concrete, glass and steel now conceals a vast underworld of computers and software. Linked up via the internet, these devices are being stitched together into a nervous system that supports the daily lives of billions in a world of huge and growing cities. Machines now run the world on our behalf. This digital upgrade to our built legacy is giving rise to a new kind of city  a ‘smart’ city  Anthony Townsend 2013

3  Sentient cities that ‘think of us’  Mike Crang & Steve Graham 2007  As computational objects have developed, cities are able to take on new forms of vitality, but only gradually, 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  Nigel Thrift 2014 Image: Mark Dorf,

4  The programming of environments  urban processes, citizen engagements, and governance unfold through networks of sensors, algorithms, databases and mobile platforms that constitute the environments of smart cities. These technologies are generative of political techniques for governing everyday ways of life  Jennifer Gabrys 2013  ‘Smartmentality’  the production & circulation of knowledge, rationalities, subjectivities & moralities suited to the management of the smart city  Alberto Vanolo 2014 Image: Upgrade Studio:

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6 While many cities are already well on their way toward modernizing their technology infrastructures, they also need to respond to today’s most important education trends. … To thrive, cities need to provide access to powerful learning devices, tools, & apps that empower education…. Microsoft CityNext solutions transform education to create more innovative schools and teaching practices  Microsoft CityNext 2014

7 Digital technologies and education: almost all our work in this area has a smart cities aspect (though oddly education has tended to be excluded from the SC field), and we’re spreading out into new ways of promoting digital making of all kinds in cities  Geoff Mulgan 2014

8  Smart cities as a social imaginary, often idealized & unrealized, vs. ‘actually existing smart cities’  Taylor Shelton et al  Sociotechnical imaginaries  collectively imagined forms of social life and order, reflected in technological projects, that describe attainable futures & prescribe futures that states believe ought to be attained  Sheila Jasanoff & Sang-Hyuan Kim 2009 Sociotechnical imaginaries Image: Robert Cameron,

9 Fabricated spaces  Governing cities as ‘laboratories of conduct’  the city as a way of diagramming human existence, human conduct, human subjectivity, human life itself  Thomas Osborne & Nikolas Rose 1999  Fabricated spaces are models, diagrams & distillations of practices for shaping of conduct, with aspiration that reality can be made to conform with them  technologies for visualizing & governing urban life  Margo Huxley 2001 Image: Robert Cameron,

10 Technologies of schooling Q What is the ‘technology of schooling’ in smart cities?  Pedagogic knowledges, civilizing aspirations, techniques of discipline and organization, professional standards and obligations, schoolrooms of a certain design, mental exercises, material and technical infrastructures, textbooks and other discursive products, all infused with the aim of shaping and inculcating particular forms of conduct  to act upon action  Nikolas Rose 1999 Image: Emely Jensen,

11 Built pedagogy Q What is the ‘built pedagogy’ of the smart school?  Built pedagogy describes how spaces teach individuals what should and should not be done  Built pedagogies of ‘technological spaces’ embody politics & values, catalyze and foreclose particular actions & experiences  Torin Monahan 2005 Image: Emely Jensen,

12 Fabricating smart schools  Smart schools as sociotechnical imaginaries & fabricated spaces  Technical  devices, information, data, code, & algorithms  Social  actors, groups & organizations e.g. software producers, commercial companies, designers, government  Discursive  texts, documents, images, infographics, visualizations  A ‘technology of schooling’ infused with aim to shape student behaviour through coded built pedagogies Image: Upgrade Studio:

13 1 Schools as data platforms  Schools, universities & classrooms as ‘data platforms’ in a ‘big data ecosystem’ Image: Tomas Saraceno,

14 To be competitive, cities need to ensure that their citizens have access to twenty-first century productivity tools. All schools, including higher-education institutions, can benefit from complementary, world-class apps and online services that make it easier to interact and collaborate. Microsoft CityNext offers students and educators solutions built on technology they’re already using, including cloud, Big Data, mobile and social  Microsoft Educated Cities 2014

15 The rise of the ‘smart classroom’: The rapid digitization of the education industry and the emergence of cognitive systems is already happening in parallel. Digital education creates a tremendous amount of data about all aspects of teaching and learning—test scores, information about student behavior on digital learning platforms, attendance, and more. IBM envisions educational institutions adopting cloud-based cognitive systems to collect and analyze all of this data over a long period of time  IBM 2013

16 Schools and universities have always recorded and stored data as they tracked grades, attendance, test scores and demographics. With the increasing availability of technology in the instructional process, educational institutions now collect, in real time, data about what their students learn and how they progress. Using big data and analytics, everything from attendance to a campus building’s energy usage has a place in identifying targets for improvement… Today’s students expect their learning environments to mirror the environments in which they live ― punctuated by always-on, available-anywhere information & personalized, multichannel learning  IBM Smarter Education 2014

17 Just as in other sectors of the economy and government, ‘Big Data’ and its potential for personalized learning holds the key to significant increases in student achievement. Everything students do in digital environments  from completing online lessons to playing games  generates massive amounts of data about their activities and learning. Researchers in the Center for Digital Data, Analytics & Adaptive Learning are making progress in harnessing the power of that data to assess, enable, and personalize learning without the disruption of traditional tests  Pearson 2014

18  ‘Datafying the learning process’  Feedback  Online courses enabling logging & tracking of learners through clickstream data  ‘Knowing’ e-textbooks that can ‘learn’ from use and ‘talk back’ to the teacher  Personalization  Algorithmically personalized pedagogic ‘playlists’  Probabilistic prediction  Predictive learning analytics to optimize student learning  Viktor Mayer-Schonberger & Kenneth Cukier 2014

19 Big data are being used in the development of smarter cities, smarter governments, and a smarter planet. So why not smarter colleges or universities? The Big Data movement could help build smarter universities  institutions that can use the huge amounts of data they generate to improve the student learning experience, enhance the research enterprise, support effective community outreach, and advance the campus’s infrastructure  Jason E. Lane 2014

20 Sqord gives you an administrative reporting tool with quantifiable metrics on the physical activity, levels, & participation of each of your players. No more guesswork or gray areas in measuring physical activity. Sqord puts the numbers in plain view, & allows your teachers & coaches to see exactly what’s what in real-time  Sqord 2014

21 Biosensors & biometrics in the classroom  Gates Foundation  ‘student sensor bracelets’ to detect excitement, stress, fear, engagement, boredom & relaxation through the skin  EngageSense  ‘face-cams’ & computer vision algorithms produce automated metrics of student engagement

22  ‘Quantified self-city’ hybrids  cities are rethought as organisms and human bodies quantified as systems  Matthew Wilson 2015  Quantified student-school hybrids  smart learners in smart schools, whose behaviours can be nudged, tweaked & changed through interaction with flows of data Image: John A Rogers,

23 2 Digital policy instruments Policy instruments make policy agendas operational  devices that enact particular policies, & that constitute a condensed form of knowledge about exercising social control  Pierre Lascoumes & Patrick le Gales 2007

24  Transparency, open data, comparison, marketization  Making school data more visual, publicly intelligible & persuasive

25  Display & visualization of school data replacing expert judgment of the inspector  Jenny Ozga 2014

26 MoneySupermarket for school choice  configuring parents as educational comparative analysts

27 ‘Public social statistics’ enable ‘media-assisted government from afar’ whereby authorities are enabled to act upon schools through indirect forms of control, by guiding users to make rational evidence-informed decisions  Nelli Piattoeva 2014

28  A ‘virtual world of educational data’ that displaces a focus on the ‘real world of education’  Makes different ‘school realities’ visible & actionable  A ‘realist epistemology’ of ‘visualized facts’  Rob Kitchin et al 2015

29 Smart schools as nodes in global data networks  A web of data, feedback & prediction required to coordinate & control the system  Jenny Ozga 2009  Software and data companies are the ‘hidden’ new managers of the ‘virtual educational landscape.’ The management of education systems is increasingly connected to the capacity of data servers, software developments, & the use of data-mining tools  Martin Lawn 2013

30 3 Learning analytics  Intelligent software devices that facilitate unobtrusive classroom data collection in real time  Track learning & teaching at the individual student and lesson level every day in order to personalise & optimise learning  Application of data analytics and the adoption of new metrics to generate deeper insights into & richer information on learning & teaching  Online intelligent learning systems, data analytics & automated artificial intelligence systems to provide ongoing feedback to personalise instruction & improve learning and teaching  Pearson 2014

31 Educators already have plenty of administrative and economic data—the challenge is gaining insights from it … for improved tracking and evaluation. Microsoft education analytics solutions help students perform better and can be adapted to meet individual needs. The analytics tools improve administration as well with a 360-degree view of performance and operations. Easy-to-use reporting tools simplify access to data for everyone, and make it easier to share information with stakeholders  Microsoft Educated Cities 2014

32 Analytics translates volumes of data into insights for policy makers, administrators and educators alike so they can identify which academic practices and programs work best and where investments should be directed. By turning masses of data into useful intelligence, educational institutions can create smarter schools for now and for the future  IBM Smarter Education 2014

33  P-TECH as test bed for the next plank in IBM’s Smarter Cities effort, which is to build for schools what its operations center is for cities: a single system for collecting, aggregating and analyzing data from students and teachers alike, then writing algorithms to prescribe how to cope  a software ‘infrastructure layer’ for schools, running behind the scenes to manage students’ digital textbooks & analyze their performance  a research project for gleaning best practices that can be codified into software  Greg Lindsay 2013

34  The IBM ‘smarter classroom’ is a ‘classroom that will learn you’ through ‘cognitive-based learning systems’  Predictive tools: based on what’s already happened, what’s going to happen next?  Prescriptive analytics: 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

35  Anticipatory governance  from ‘real-time’ monitoring to ‘future-tense’ pre-emption  Classrooms that learn you in ‘sentient cities that think of us’  Mike Crang & Stephen Graham 2007 Image:

36  Citizens learning to be participatory ‘computational operatives,’ ‘human capital’ & ‘civic hackers’ of the smart city 4 Smart citizens

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39  Mobilising the next generation of digital makers  the world is becoming increasingly digitised & filled with products that change the way we connect & interact. … We want to help people shift from consuming digital technologies, to … hacking, re-mixing and making things with technology  Make Things Do Stuff 2013  Promoting digital making of all kinds in cities  Geoff Mulgan 2014

40  Learning to code as production of the smart people and innovative human capital required by smart cities  The urbanization of code  Californian coder culture infuses the work of code & its coders  Adrian Mackenzie 2014

41 Making smart cities into ‘maker cities’  makers are starting to reimagine the systems that surround them. They are bringing the ‘maker mindset’ to the complex urban challenges of health, education, food, & even citizenship. Makers will make the future of their cities  Institute for the Future 2014

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43  AltSchools as ‘startup schools’  $28million in venture capital funding, set up by former Google engineering exec  Hybrid maker-schools  part ‘makerspace’, part school  ‘Our world-class team of educators, entrepreneurs, and technologists is working together to build a technology-driven network of micro-schools that offers a new, radically- personalized experience’  AltSchool 2014

44  Learning to ‘code for x’—merging ‘what is (technically) possible and what is (politically) feasible’  Nesta 2014  ‘Civic hackers’ in ‘civic laboratories’ coding solutions to smart city problems … knowing how to code will be an important skill for civic improvement  Anthony Townsend 2013  Computationally skilled citizens who have learned to code to facilitate ‘technocratic solutionism’ in the smart city

45  Smart citizens  actions of citizens have less to do with individuals exercising rights & responsibilities, & more with operationalizing the cybernetic functions of the smart city. The citizen is a data point, both a generator of data & a responsive node in a system of feedback  Jennifer Gabrys 2014  Smart citizens are very subtly asked to participate in the construction of smart cities & implicitly considered responsible for it, re-subjectified as active citizens  Alberto Vanolo 2014 Image: Mark Dorf,

46 Fabricating smart schools  Emerging ‘technology of schooling’ in the ‘laboratory’ of the smart city: 1 Databases making schools more ‘known’ & ‘knowing’ 2 Digital policy instruments & global policy networks 3 Analytics trained to self-adapt & learn the learner 4 Learning to code to interpolate students into the computational systems of the city Image: Tomas Saraceno,

47 Conclusion  Smart schools as smart cities’ laboratories of conduct where ‘the computer boys take over’  Nathan Ensmenger 2010  The politics of programmers, their coder cultures, models of the world, values, methods, ways of thinking & doing are built- in to the pedagogic spaces & practices of the classroom  Schools more data-driven, smart, sentient  with a life of their own… Image: Upgrade Studio:

48 Further research  Network analysis of smart schools promoters & programmers  tracing commercial, governmental, academic, entrepreneurial, & third sector connections  Tracing the genealogy of smart schools discourses  analyzing policy imaginaries, technology fantasies, & visualizations  Software studies of smart schools technologies  social & cultural creation of tech; its programming, code & algorithms; its underlying assumptions, values & models  Ethnographies of ‘actually existing’ smart schools  following ‘live’ smart city education developments in-the-making Image: Upgrade Studio:

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