Making education tweetable

What happens when education is made into something tweetable? As in other professional areas, social media increasingly intervene in and interfere with how educational matters are communicated. Little parcels of educational information and knowledge are now routinely packaged up and passed around in the networks of Twitter and other social media. What difference does this make to how educational issues, problems and concerns are received and comprehended?

A simple critical line might suggest that social media tools like Twitter are leading to a reductive, mediatised transformation of complex educational issues to sound bites, spin, and info-nuggets of marketable policy puff. That line of analysis oversimplifies matters.

What if, instead, we asked how education actually might be changed or transformed by the forms in which it is communicated, whether in printed material form or virtual form?

To provide a brief example, in a book on the use of statistical data in education policy in Europe, Jenny Ozga and coauthors write of seeing “pupils transformed into numbers,” distributed in the tables and graphs of policy papers, and “entering conference rooms throughout Europe, having undergone a series of transformations.” In this example, the children who populate schools have been transformed into data as their examination performance results have been collected and collated into databases, crunched by data analysis technologies, converted into graphical displays, and from there transported as “evidence” in the papers of educational policy discussions and proposals. The pupils themselves have been entirely displaced as they have been translated into a coordinated series of events, actions and materials.

With the addition of social media, the potential transformations are amplified. In order to explore such things theoretically and empirically, what might we need to do?

Exploring these matters would mean taking a close critical interest in the material and virtual form of, for example, policy texts, curriculum guides, and school websites, and particularly in the social media tools and resources now commonly used to share and distribute information and knowledge about education. Drawing from literary theory, it would require us to examine the bibliographic codes of educational texts—the various textual, typographic, visual and material techniques they deploy—and to explain how these ultimately transform the linguistic codes they contain. This kind of approach would examine the radial social factors that surround and shape the physical production of a text—such as the role of publishers, editors, typographers, designers, distributors and so on—and their effect on its reception and meaning. In order to capture the distinctive forms of digitally mediated educational sources, it would also take up a software studies approach to the political, cultural and conceptual formation of software and a close analysis of its layers of computer code, algorithmic logic, programming languages, visualization, and ordering. If education can be tweeted, then how does the computationally coded form of the tweet affect it?

Paying close attention to the bibliographic codes, linguistic codes and computer codes which shape the distribution, reception and meanings of educational matters would be valuable given the extent to which we now find education scattered kaleidoscopically across a variety of material print forms, electronic resources, social media and, increasingly, in myriad forms of data presentation.

Now it might be useful to outline (albeit crudely and highly generally for now) some of the steps taken in making education tweetable, including some indication of the radial factors and the bibliographic and computational codes involved in that process:

1 Education into learning… The first transformation we can see is that of education (a complex of institutions, policies and practices) being turned into “learning.” Learning has become one of the most prominent discourses in contemporary education, as signified for example by the growing academic influence of the “learning sciences”—a hybrid blend of psychology, neuroscience, and even computer science—which claims to be able to assess and measure the technical and social processes involved in learning, but ignores the wider social and institutional contexts of education. The growing emphasis in recent years on “learning skills,” “lifelong learning,” “learning to learn,” and so on, provide further evidence of the shift from understanding education to explaining the technicalities of learning.

2 … learning into performances… Once learning is made into the main objective of the education system, it needs to be measured. The OECD PISA tests have become the global standard for measuring learning and with them has come a cascade of concerns about performance measures and metrics. With PISA, learning is transformed into the measurement of individual test performance in order to generate evidence that can be collated and compared across different national and local education systems and sites. Likewise, teachers are now routinely judged on the basis of their performance, and schools are compared and judged on performance league tables. What matters is how well all the components of the system are performing.

3 … performances into data… As performance transforms learning, data then transforms performance. All performances must be transformed into data that can be collected, collated and calculated. The collection of educational data is nothing new but today almost every aspect of curriculum, pedagogy, and assessment, as well as administration, leadership and governance, needs to be evidenced through data. There has been a pervasive datafication of education. Performance data on all of these elements of education, and more, can now be brought together with the aim of generating quantifiable, calculable, and statistically significant evidence.

4 … data into database software… The torrent of education data needs to be managed and stored. This is where databases, powered by sophisticated software, take over. Emerging database driven technologies, with the capacity for ordering, sorting, counting, and calculating complex datasets, transform data itself into tiny informational units that can be expressed in the language of computer code and processed through the logical procedures of algorithms. Whatever the act of “learning” that has been previously performed, measured, and datafied, it must now undergo a computational transformation into forms of “input” that can be operated on by software. Learning has been softwarised.

5 …databases into data visualisations… By this point in the process, learning has been disassembled into calculable, sortable, and combinable data traces capable of being carried by the computer code and algorithmic processes of software, and it is time to reassemble them. Here, visualisations, tables, charts, diagrams, infographics, and other graphical re-presentations of data enabled by data analytics software come into action. Data visualisation does a huge amount of work. Bruno Latour might suggest that the power of a data visualisation, like any graphic, image, or diagram, is to stabilize ideas, problems, concepts, explanations and arguments in one place, so that “realms of reality that seem far apart are just inches apart, once flattened on to the same surface.” Visualisations act as material techniques of thought that can be moved around, copied, reshuffled, recombined, superimposed and reproduced in other places.

6 …visualisations into tweets. Visualisations transform their original contents into an easily transportable format that requires little commentary. They flatten and freeze all of the transformations detailed above into one form. This makes them very easy to share, forward on, and circulate through social media tools like Twitter. Almost any aspect of education can now be reduced to less than 140 characters and a bit.ly link. Education has been made tweetable!

In this series we can see education being transformed through a cascade of encounters with bibliographic, linguistic and computational codes. If we were to populate this set of transformations with people, then we would find pupils not only transformed into numerical data, but into computational code, into graphical visualisations, and then into 140 character messages on Twitter. As in the example above of pupils transformed by education policy into numbers, tables and curves, it is now possible to transform pupils into tiny bits of tweetable computational data. That’s what happens when you make education tweetable.

Ben Williamson

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