Big Data in Education

The digital future of learning, policy and practice

Ben Williamson, Author

• Publisher: SAGE Publications Ltd
• Year: 2017
• Print ISBN: 9781473948006, 9781473947993
• eBook ISBN: 9781526416322
• Edition: 1
• Topic: Education
• Subtopic: Education Theory & Practice

Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research.

This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future.

Key topics include:

·         The role of learning analytics and educational data science in schools

·         A critical appreciation of code, algorithms and infrastructures

·         The rise of 'cognitive classrooms', and the practical application of computational algorithms to learning environments

·         Important digital research methods issues for researchers

This is essential reading for anyone studying or working in today?s education environment!

1 IntroductionLearning machines, digital data and the future of education

In October 2015, over 1,000 young software developers and hackers attended HackingEDU, a three-day educational hackathon held at the San Mateo Event Center in San Francisco. Originally launched at the 2014 Google Summit, the annual HackingEDU event – the ‘world’s largest educational hackathon’ – is intended to help software developers and programmers, most of them college students, ‘revolutionize the education industry’ while competing for over US$100,000 in prizes (Hunckler, 2015). Featuring expert workshops, panel discussions and guest speakers, HackingEDU 2015 was supported by major technology companies including IBM, Google, Uber, PayPal and Automattic, as well as by successful educational technology businesses such as Chegg and EdModo. It emphasized the ways in which technologies might be used to ‘disrupt’ and ‘revolutionize’ education, much as ‘Uber revolutionized the transportation industry based on a simple concept: press a button, get a ride’, as the event’s partnership director phrased it (Uber 2015). The technology projects produced during HackingEDU 2015 included titles such as Learnization, CereBro, PocketHelp, QuizPrep, BrainWars and StudyTracker, almost all of them relying on a combination of digital data and database technologies and constructed by their young designers using a variety of programming languages, software programs and hardware devices.

Elsewhere in San Francisco, many other fledgling edtech projects are annually developed through the support of edtech ‘incubator’ or ‘accelerator’ programs. Incubators typically help entrepreneurs and new startups to test and validate ideas, while accelerators turn products into scalable businesses, often through direct equity investment, and help provide entrepreneurs with legal, IT and financial services along with mentorship, working space and access to educators, entrepreneurs, business partners and potential investors (Gomes 2015). For example, Imagine K12 is ‘a startup accelerator focused on education technology’:

Our goal is to improve your company’s chances of success. We do this through a combination of strategic advice and mentorship, a series of speakers and seminars designed to help founders make better decisions, value-added networks of entrepreneurs and educators, and $100,000 of initial funding. … Companies begin receiving support from Imagine K12 immediately upon their acceptance, including $20k of funding. … [A]ll accepted startups are required to move to Silicon Valley for an intensive four-month program. (Imagine K12 2015)

Edtech incubator and accelerator programs like Imagine K12 provide the space, support and investment required for programmers to write educational technologies, and ultimately act as mechanisms that might realize the ‘revolutionary’ ambitions of entrants to competitions like HackingEDU. Notably, Imagine K12 has since merged with another accelerator program, Y Combinator, an organization established by billionaire PayPal founder Peter Thiel, a major donor and spokesperson during Donald Trump’s US presidential campaign in 2016. A key educational technology advocate, Thiel has supported and funded many companies and startups that focus on ‘revolutionizing’ education through data-driven software applications (Levy 2016). For new startups that successfully graduate from the incubation and acceleration stage, entrepreneurial investors from Silicon Valley have been funding educational technology projects with unprecedented financial enthusiasm since about 2010 (EdSurge 2016). With webs of political support and entrepreneurial investment for educational technology growing, a new digital future for education is being imagined and pursued in governmental and private sector settings alike, with significant consequences for learning, policy and practice.

HackingEDU is an important event with which to start this book for a number of reasons. It locates education as it currently exists as a problematically broken system which is in need of revolutionizing. It proposes that the solution is in the hands of software developers and hackers who can write code. It suggests that the availability of masses of educational data can be used to gain insights into the problems of education, and to find solutions at the same time. And it also demonstrates how private sector technology companies have begun to fixate on education and their own role in fixing it. Incubators and accelerators such as Imagine K12 and Y Combinator can then step in with entrepreneurial experience to grow new products into successful startup businesses, to enable programmers to fine-tune the code and algorithms required to make their product run, and to gain financial investment required to push it out into practice. The promise appears simple. Take a model like Uber, the mobile app that has transformed taxi services by harvesting locational data from its millions of users, and then translate that model into a template for educational reform. Fund, incubate and accelerate it until it performs optimally. All it takes to revolutionize education for the future is a few million lines of software code and big piles of digital data.

Digitizing and Datafying Education

The goal of this book is to understand and detail how digital data and the code and algorithms that constitute software are mixing with particular political agendas, commercial interests, entrepreneurial ambitions, philanthropic goals, forms of scientific expertise, and professional knowledge to create new ways of understanding, imagining and intervening in education. Education is now a key site in which big data and algorithmic techniques of data mining and analysis performed with software are proliferating and gaining credibility.

Yet the quantitative increase in data brought about by recent developments and the qualitative effects they are beginning to exert in education have gone largely unnoticed amid much more high-profile concerns about the data mining conducted by social media companies on their users, targeted online advertising that is driven by consumer data, or the data-based forms of surveillance being practised by governments (van Dijck 2013). A ‘new apparatus of measurement has drastically expanded’ with the availability of digital data in diverse areas of public and private life, ‘allied with a set of cultural changes in which the pursuit of measurement is seen to be highly desirable’ (Beer 2016a: 3). Education, by contrast, appears more ‘ordinary’:

Given that so much attention has already been paid to social media corporations and governmental and security agencies, what we now need to attend to is other, more ordinary actors, as social media data mining becomes ordinary. (Kennedy 2016: 7)

This book takes up the challenge of investigating the digital data technologies, organizations and practices that are increasingly becoming integrated into many aspects of education. A vast apparatus of measurement is being developed to underpin national educational systems, institutions and the actions of the individuals who occupy them.

While the pursuit of educational measurement has a long history stretching back to the nineteenth century (Lawn 2013), it is being extended in scope, enhanced in its fidelity, and accelerated in pace at the present time as new technologies of big data collection, analysis and feedback are developed and diffused throughout the system (Beneito-Montagut 2017; Selwyn 2015). Similarly, schools, colleges and universities have employed e-learning programs for many years in their pedagogic and instructional processes (Selwyn 2011), but with big data and analytics processes now increasingly augmenting them, these resources can now adapt to their users and ‘talk back’ to educators (Mayer-Schönberger and Cukier 2014). Software and digital data are becoming integral to the ways in which educational institutions are managed, how educators’ practices are performed, how educational policies are made, how teaching and learning are experienced, and how educational research is conducted.

The presence of digital data and software in education is being amplified through massive financial and political investment in educational technologies, as well as huge growth in data collection and analysis in policymaking practices, extension of performance measurement technologies in the management of educational institutions, and rapid expansion of digital methodologies in educational research. To a significant extent, many of the ways in which classrooms function, educational policy departments and leaders make decisions, and researchers make sense of data, simply would not happen as currently intended without the presence of software code and the digital data processing programs it enacts.

To fully appreciate how digital data are being generated and exerting material effects in education, then, it is essential to view data and the software code and algorithms that process it in relation to a range of other factors that frame their use. Political agendas relating to education policy and governance, commercial interests in the educational technology market, philanthropic and charitable goals around supporting alternative pedagogic approaches, emerging forms of scientific expertise such as that of psychology, biology and neuroscience, as well as the practical knowledge of educator professionals, all combine with new kinds of data practices and digital technologies. That is, the mobilization of digital data in education happens in relation to diverse practices, ways of thinking, ambitions, objectives and aspirations that all shape how data is put to use, define the tasks and projects through which data is deployed, and co-determine the results of any form of educational data analysis. The role and consequences of digital data in education cannot be understood without appreciating their relations with the other ordinary features of education – policies, accountability mechanisms, commercial imperatives, charitable intentions, scientific knowledge and professional practice.

In this sense, the subject of this book is the combined process of ‘datafying’ and ‘digitizing’ education. Putting it simply, ‘datafication’ refers to the transformation of different aspects of education (such as test scores, school inspection reports, or clickstream data from an online course) into digital data. Making information about education into digital data allows it to be inserted into databases, where it can be measured, calculations can be performed on it, and through which it can be turned into charts, tables and other forms of graphical presentation. ‘Digitization’ refers to the translation of diverse educational practices into software code, and is most obvious in the ways that aspects of teaching and learning are digitized as e-learning software products. If you want to build some digital e-learning software, you have to figure out how to do that in lines of code: to encode educational processes into software products. Diverse aspects of education from policy, leadership, management and administration to classroom practice, pedagogy and assessment are now increasingly subjected to processes of digitization, as software is coded and algorithms are designed to augment and rework everyday tasks and processes across the education sector.

Datafication and digitization support and complement one another in myriad ways. For example, when a piece of e-learning software is coded in digital form, it is often designed in such a way that it can generate information about the ways that it is used (visible in, for example, the log files that demonstrate how a user has interacted with the software). That information can then be used, as analysable digital data, to help the producers of the software learn more about the use of their product, data which can then be used to help inform the writing of better code (a software patch, upgrade or update) or the programming of new software products altogether. To take another example: when millions of learners around the world all take a standard global test, the activities they undertake ultimately contribute to the production of a massive database of test results. Making sense of the vast reserves of data in such a database can only be accomplished using software that has been coded to enable particular kinds of analyses and interpretations. The software does not have to be especially appealing – the datafication of education depends to a significant degree on the digital coding undertaken to produce very mundane software products like spreadsheets and statistical analysis packages – but it is certainly becoming more seductive with the ready availability of highly graphical forms of data visualization software, as well as more accessible and easier to use. With both educational technologies and educational data, processes of digitization and datafication support and reinforce each other.

In short, much of education today is being influenced and shaped by the production of lines of code that make digital software function, and by the generation of digital data that allows information about education to be collected, calculated and communicated with software products. Does this matter? Yes, it matters urgently, because the coding of software products for use in education, or the application of coded devices that can process educational digital data, are beginning to transform educational policies, pedagogies and other practices in ways which have so far been the subject of very little critical attention.

As new kinds of software are developed for use in educational contexts that rely on both software code and digital data, we are beginning to see new ways in which schools, universities, educational leaders, teachers, students, policymakers and parents are influenced. Schools are being turned into data-production centres, responsible for constantly recording and auditing every aspect of their performance (Finn 2016). Leaders are being called on to act on their data to improve the institutions they manage (Lewis and Hardy 2016), often using ‘learning management systems’ to assist in administrative tasks (Selwyn et al. 2017). Students are becoming the subjects of increasingly pervasive data mining and data analytics packages that, embedded in educational technologies and e-learning software, can trace their every digital move, calculate their educational progress and even predict their probable outcomes (Suoto-Otero and Beneito-Montagut 2016). Students in universities are experiencing ever-greater use of online tools to measure their progress (Losh 2014), with their assignments being entered into massive global plagiarism detection databases (Introna 2016). At the same time, university managers are required to make use of complex performance indicator metrics and institutional data dashboards to facilitate decision-making and planning (Wolf et al. 2016). Even early years settings such as nurseries are increasingly required to collect data on young children’s development so that it can be tracked against national and international benchmarks (Roberts-Holmes 2015; Moss et al. 2016), which is mirrored by the growing use of analytics technologies in adult education and professional learning (Fenwick and Edwards 2016).

Beyond the spaces of learning, policymakers are increasingly exhorted to develop data-driven or ‘evidence-based’ policies that are crafted in response to insights derived from digital data (Sellar 2015a), including school inspection data presented on institutions’ ‘data dashboards’ (Ozga 2016). Parents, too, are encouraged to become educational data analysts who use digital ‘school comparison’ websites to inform their choices about which schools to enrol their children in (Piattoeva 2015). For teachers, a new industry in educational ‘talent analytics’, or ‘labour market analytics’, has even appeared (Beneito-Montagut 2017), with fully-automated software products like TeacherMatch acting as ‘advanced education talent management’ platforms for the recruitment, assessment, professional development and ‘talent investment’ of teachers, using matching algorithms to match schools with staff just like a social media dating service (TeacherMatch 2015).

Many commercial organizations are changing their business models and practices to engage in education, such as Google with its Google Apps for Education suite of free-to-use cloud services for schools (Lindh and Nolin 2016). Meanwhile, existing commercial ‘edu-businesses’ such as Pearson – a global education textbook publisher – have moved to become prominent educational software providers and key collectors of educational data (Hogan et al. 2015). Commercial tools for data collection, processing and analysis are finding their way into the discipline of educational research, knowledge production and theory generation too, in ways that are reshaping how education is known and understood (Cope and Kalantzis 2016). And finally, an increasing number of private sector ‘data brokers’ are starting to collect education-related data, curate and aggregate it using analytics tools, and sell it back to education stakeholders (Beneito-Montagut 2017).

It’s not just the people and organizations of education that are affected by the recent acceleration of data-processing software, but curriculum, pedagogy and assessment too. The notion of a curriculum containing the content-knowledge to be taught in schools is itself being challenged, as new kinds of ‘adaptive’ learning software are developed that can semi-automate the allocation and ‘personalization’ of content according to each learners’ individual data profile (Bulger 2016). Pedagogy is being distributed to automated machines such as ‘teacher bots’ and ‘cognitive tutors’: computerized software agents designed to interact with learners, conduct constant real-time analysis of their learning, and adapt with them (Bayne 2015). And the notion of assessment as a fixed event is being supplanted by real-time assessment analytics and computer-adaptive testing, which automatically assess each learner on-the-go and adapt to their responses in real-time (Thompson 2016). What is even meant by ‘learning’ is being questioned with the collection of datasets so large that enthusiasts believe they can reveal new truths about learning processes that educational researchers working within disciplinary frameworks such as psychology, sociology and philosophy have been unable to detect before (Behrens 2013).

Many of these developments and innovations with digital software and data in education exist technically, but they are also the product of extensive claims, promotional activity and imaginative marketing which centres on the idea that technical solutions have the capacity to transform education for the future. Businesses with products to sell, venture capital firms with return on investment to secure, think tanks with new ideas to promote, and policymakers with problems to solve and politicians with agendas to set have all become key advocates for data-driven education. Of course, we need to be at the very least cautious about many of the claims made about the transformative and revolutionary potential of many new developments, if not downright sceptical – and, indeed, a little resistant.

But the point I pursue throughout is that what we are currently witnessing are signs of a new way of thinking about education as a datafied and digitized social institution. Seriously powerful organizations are at work in this space, organizations with a forceful and influential shared imagination concerning the future of education. It is easy to be dismissive of the claims-making, hype and hubris that surround emerging developments like learning analytics and computer-based cognitive tutors. But it’s less easy to dismiss these developments and the claims that support them when you can see that some of the world’s richest and most powerful companies are dedicating extraordinary research and development resources to them; when you can read reports advocating and sponsoring them by influential think tanks; when you hear that politicians are backing them; when you discover that enormous sums of venture capital and philan...

Table of contents

• Cover
• Half Title
• Publisher Note
• Title Page
• Copyright Page
• Contents
• About the author
• Preface
• Acknowledgements
• About the book
• 1 IntroductionLearning machines, digital data and the future of education
• 2 Conceptualizing digital dataData mining, analytics and imaginaries
• 3 Software, code and algorithmsProgramming, automating and governing everyday life
• 4 Digital education governancePolitical analytics, performativity and accountability
• 5 The social life of education data scienceLearning analytics, educational data mining and metrological platforms
• 6 The CompPsy complexNon-cognitive learning, psychological enhancement and behaviour change
• 7 Rewiring brainsArtificial intelligence, cognitive systems and neuroeducation
• 8 Making and coding culturesDigital citizens, DIY makers and apprentice data analysts
• 9 ConclusionProgrammable public pedagogies of software and big data
• References
• Index