By Ben Williamson
The Education Foundation, the ‘think tank for education,’ recently published a report on educational technology. Though largely unsurprising, the striking aspect of the report is its continual refrain about data, evidence, and ‘what works.’ Data analysis is described in the report as an essential aspect of school management and improvement, yet also an important skill to develop among students, as the report recommends:
Data science skills: ‘Big Data’ and the ability to get hold of, organise and analyse data to extract valuable insights is a skill that is growing in importance for service based economies.
A very different report from the Chicago Council of Global Affairs focuses on ‘big data’ in the organization and governance of a ‘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 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. … Rapid analysis of multiple datasets is needed to understand drivers, make meaningful and timely adjustments, and thereby address critical gaps in school and student performance….
Data collection and analysis has thus become part of the infrastructure of contemporary schooling, particularly, as the Chicago report demonstrates, in the context of big data in new urban infrastructures. In both the Chicago report and the Education Foundation report, data is seen as a solution to many of the problems that schools face, including pedagogy itself, as well as part of the ensemble of surveillant techniques of governance and monitoring now routinely used to track and measure educational institutions and performances.
This is leading to a situation where the data analysis is actively involved in the production of educational practices and settings, not just in counting or representing them. That is to say that the data is being used not only for measurement but for modelling, prediction, and decision-making. As Neil Selwyn has recently written,
the ‘modelling’ of education through digital data is seen to engender a sense of algorithmically driven ‘systems thinking’ – where complex (and unsolvable) social problems associated with education can be seen as complex (but solvable) statistical problems. Thus, digital data are accompanied by a heightened sense of ‘solutionism’. This leads to a recursive state where data analysis begins to produce educational settings, as much as education settings producing data.
These developments in algorithmic solutionism are under-researched in education, even though they are becoming increasingly significant in how education is governed–there is ‘governing software and algorithmic power‘ at work in education as I’ve argued elsewhere. But in other areas of social scientific inquiry researchers have already begun to offer theoretically nuanced and empirically detailed studies of such data-driven processes.
In the study of what is known as ‘urban informatics,’ for example, researchers are offering studies of the increasing role of information technologies and digital data in the everyday governance and organization of cities. This is taking place in the context of increasing attention to ideas about ‘smart cities’ or ‘programmable cities’ that are undergirded by vast technical infrastructures, just as envisioned in the Chicago report on big data in the megacity. Terms such as ‘code/space’ and ‘software-supported spatiality’ register the tight interweaving of software code and algorithms into the ways that everyday spaces are designed and function.
In a recent short paper on urban informatics, Nigel Thrift writes about an emerging urban environment in which computer code and big data are ‘adding a new layer of skin to the world’:
What we are seeing is data gradually becoming a part of how we see the world as it becomes embedded in all of the surfaces we come across, and the moving actors that span them, whether birds and trees or cars or us. … I can foresee a world in which every surface is overlaid with data, in which every surface can speak: the world as a continuous canvas, if you like. Each and every situation will be haunted by its data analogues. These new alloys of materials and information will call to us in more or less powerful ways, fitting into our lives like slightly out-of-kilter duals to existing surfaces because they can react so quickly. Trees and animals, walls and windows, all of them will be sending and receiving—and representing—data, just like screens do now but generalised to the nth degree.
Within such smart and responsive environments humans and nonhumans are becoming increasingly intimately webbed together. Jennifer Gabrys has produced a really interesting study of smart cities as ‘programmable environments’ as part of a wider project entitled Citizen Sense. In her article, she claims that cities are becoming increasingly saturated with computational sensing technologies where citizens are solicited to participate in computational sensing and monitoring practices. Think of systems such as Nest for monitoring the home and the workplace; wearable devices for monitoring the body; GPS devices for monitoring location; as well as social media and other web services which can monitor every online transaction and interaction, and so on. As a result, cities are becoming more like ‘datasets to be manipulated,’ and citizens are being positioned as ‘operatives’ in computational systems that are focused on monitoring and managing data. In such cities, Gabrys argues:
The actions of citizens have less to do with individuals exercising rights and responsibilities, and more to do with operationalizing the cybernetic functions of the smart city. Participation involves computational responsiveness and is coextensive with actions of monitoring and managing one’s relations to environments, rather than advancing democratic engagement through dialogue and debate. The citizen is a data point, both a generator of data and a responsive node in a system of feedback.
The image of the ‘computational citizen’ as a data point webbed in a vast networked environment–a city overlaid with an artificial surface of dynamic data–is a compelling one.
How might such an understanding orient us to researching the contemporary saturation of schools and other educational institutions by data systems? If we return to the Education Foundation report, we can see, however crudely, how it seeks to position schoolchildren as both generators of and responsive to data. It aspires to turn children into individuals who can write computer code and carry out data analysis; the perfect ‘operatives’ in Gabrys’s terms for participating in contemporary computational systems of governance, who can interact with various sensing systems to produce data that can be used to monitor and measure their every activity. It also positions schoolchildren as sources of data that enable processes of monitoring, surveillance, management and ‘improvement’ to be enacted. The Chicago report on big data in future urban governance is even more explicit about the embedding of data systems in education. In the vision of the future of urban schooling it offers, where data enables real-time customization, personalization and ‘improvement,’ the learner is positioned as a data point in a system of feedback, whose actions and behaviour is to be targeted and potentially improved by that feedback.
This is just a note for further exploration of course, but pursuing the idea of the ‘software-supported school’ or of ‘student sensing’ would be exciting and innovative. Certainly schools are being reimagined by such disparate organizations as the Education Foundation and the Chicago Council of Global Affairs as embedded in complex emerging urban infrastructures where data is constantly being collected and sculpted using advanced analytics and visualization software. Education in the ‘programmable city’ will take place in ‘smart schools,’ planned and managed according to particular visions and aspirations to future urban governance. Such schools will present to the world through surfaces of data constituted out of the monitored and measured performances of their computational systems and human operatives. If we want to understand the ways that modern educational institutions are organized, governed and managed, then we could usefully view them as ‘programmable environments’ or ‘software-supported spaces’ in which learners are constantly to be ‘sensed.’