The Educational Intelligent Economy: BIG DATA, Artificial Intelligence, Machine Learning and the Internet of Things in Education: Volume 38

With the advent of the fourth industrial revolution and the intelligent economy, this conceptual chapter explores the evolution of educational governance from one based on governing by numbers and evidence-based governance to one constituted around governance by data or data-based educational governance. With the rise of markets and networks in education, Big Data, machine data, high-dimension data, open data, and dark data have consequences for the governance of national educational systems. In doing so, it draws attention to the rise of the algorithmization and computerization of educational policy-making. The author uses the concept of “blitzscaling”, aided by the conceptual framing of assemblage theory, to suggest that we are witnessing the rise of a fragmented model of educational governance. I call this governance with a “big G” and governance with a “small g.” In short, I suggest that while globalization has led to the deterritorializing of the national state, data educational governance, an assemblage, is bringing about the reterritorialization of things as new material projects are being reconstituted.

The implications and impacts of the educational intelligent economy from the vantage point of digital frontierism is explored using a decolonial framework, with a specific focus on Big Data and data sharing in Comparative and International Education (CIE). Recent debates are reviewed about CIE’s past histories and its current directions to tease out their implications for data sharing. The authors demonstrate how data sharing continues to reinforce imperialism through control, dissemination, and application of data, and how electronic and digital colonialism preserve current intellectual and structural hegemonies. Then, we give an example of how donors and funding agencies, including the National Science Foundation, engage in neoliberal scientism and control of data, and how it affects the future of social sciences, including CIE. Our inquiry is at the intersections of economic intelligence and educational intelligence in a rapidly evolving technocentric, data-dominated, and networked economy. The authors demonstrate how educational intelligence in the global economy may exacerbate the asymmetric access to data between the global North and the South, as educational data are increasingly becoming global commodities to be traded between various public and private actors. Finally, the authors argue that decolonial participatory research designs that aim at positive, sustained transformations, as opposed to the stagnancy of Big Data and data mining, should be used to address the problems inherent to the Educational Intelligent Economy.

This chapter considers some of the limit points of contemporary relations between International Large-Scale Assessments, learning analytic platforms, and theories of mind circulating in contemporary comparative and transnational educational policy discourses. First, aspects of the rise of Big Data and predictive analytics are historicized, with particular attention to how emergent notions of concepts like an intelligent educational economy paradoxically seem to offer unprecedented opportunities for personalizing education that increasingly rely on efforts to construct, universalize, and predict transnational benchmarks. Then, the chapter pursues how such efforts to universalize measures and predict changes have located the mind as a primary target for solving social problems through educational reform. More specifically, the emergence and circulation of the perceptron in the United States during the 1950s and 1960s is suggested as one example of how efforts to model the human mind as a neuro-dynamic learning system became entangled with efforts to produce universal, mobile, and adaptive neuro-dynamic learning systems targeting the transnational optimization of human minds.

Though we have recently witnessed the “exponential production of digital data to measure, analyze, and predict educational performance” (Salajan & Jules, this volume), there has not been sufficient attention given to the quantitative methods that are used to process and transform this data in order to arrive at findings related to “what works”. This chapter addresses this gap by discussing a range of constraints that affect the main methods used for this purpose, with these methods being known as “impact evaluation.” Specifically, this chapter addresses its purpose, first, by making explicit the methodological assumptions, technical weaknesses, and practical shortcomings of the two main forms of impact evaluation—regression analysis and randomized controlled trials. Although the idea of Big Data and the ability to process it is receiving more attention, the underlying point here is that these new initiatives and advances in data collection are still dependent on methods that have serious limitations. To that end, not only do proponents of Big Data avoid or downplay discussion of the methodological pitfalls of impact evaluation, they also fail to acknowledge the political and organizational dynamics that affect the collection of data. To the extent that such methods will increasingly be used to guide public policy around the globe, it is essential that stakeholders inside and outside education systems are informed about their weaknesses—methodologically and in terms of their inability to take the politics out of policymaking. While the promises of Big Data are seductive, they have not replaced the human element of decision making.

The digital technological revolution offers new ways for classrooms to operate and challenges the concept of whether brick and mortar schools should exist at all. At the same time, the changes to society as we move from a knowledge-based economy to an intelligent and innovation-based economy challenges us to reassess the purpose of education. This chapter investigates an overarching counterfactual question, “What if compulsory schooling was invented in the twenty-first century”? We used a foresight methodology, based on “anticipation,” to conceptualize possible models for a future system of compulsory schooling arising from an analysis of contemporary catalysts for remodeling. While anticipation does not predict the future, the concept is that when a current system and a model of a system interplay, they impact each other to change both the present as well as possible futures. The design principles of cities, such as Freiburg (Germany), Poundbury (England), and Christie Walk (Australia), which have been developed around the idea of ecologically sustainable and decentralized cities, are focused on approaches to living that can provide a springboard for exploring the impact of changing employment, economic, technological, and social change on future schooling models. Magnetic Resonance Imaging (MRI) has opened up a new field of study to investigate neuroscience, which can inform teaching practice. Postmodern and indigenous ways of thinking provide different insights about how schooling might be reconceptualized. Alternative models of future schooling are conceptualized about (i) the role of the learner and teacher, (ii) design of a school, and (iii) the purpose of compulsory schooling. For each area of remodeling, deviations to current practices as well as paradigm shifts are framed as part of scenario building. Related questions include: how schooling might be different if it had been created today for the first time? How might it better meet the needs of contemporary society? What aspects of schooling now might be lost if it was only invented in the twenty-first century? What are possible side effects from any change ideas as part of research practice? A vital aspect of this chapter is to explore the concept of learning as a general concept versus the more specific concept of schooling. We are at the precipice of a new vision of schooling based on a counterfactual way of thinking about the future of schooling as we have known it in the West.

Almost every detail of our lives – where we go, what we do, and with whom – is captured as digital data. Technological advancements in cloud computing, artificial intelligence, and data analytics offer the education sector new ways not only to improve policy and processes but also to personalize learning and teaching practice. However, these changes raise fundamental questions around who owns the data, how it might be used, and the consequences of use. The application of Big Data in education can be directed toward a wide range of stakeholders, such as educators, students, policy-makers, institutions, or researchers. It may also have different objectives, such as monitoring, student support, prediction, assessment, feedback, and personalization. This chapter presents the nuances and recent research trends spurred by technological advancements that have influenced the education sector and highlights the need to look beyond the technical boundaries using a socio-semiotic lens. With the explosion of available information and digital technologies pervading cultural, social, political as well as economic spaces, being a lifelong learner is pivotal for success. However, technology on its own is not sufficient to drive this change. For technology to be successful, it should complement individual learning cultures and education systems. This chapter is broadly divided into two main sections. In the first section, we contemplate a vision for the future, which is deemed possible based on ongoing digital and computing advancements. The second section elaborates the technological, pedagogical, cultural, and political requirements to attain that vision.

In a world where the continual combining of computer applications and the expansion of artificial intelligence is already necessarily changing the world of work for people, an education system that does not adequately respond to these trends and changes will render itself irrelevant. Education policy and regulation may suffer at the hand of such accelerations due to unexpected consequences and developments. However, the rapid, exponential improvements in computer hardware and software that have enhanced the rate and our ability to gather, transform, manipulate, and interpret these data in an ongoing fashion also present myriad educational opportunities. The so-called Fourth Industrial Revolution offers societies data and information capabilities previously unimagined, making it possible to learn how to combine, innovate, and imagine entirely new avenues for building responsive and intelligent education policies and systems that promote the education and wellbeing of citizens as well as improving their economic participation. These advances necessitate a growing number of educators and education systems who can intelligently respond to Industry 4.0 trends. In this chapter, some considerations regarding the use of large-scale, international datasets and emerging data analytics for analyzing policy for the governance of education are offered, and a discussion of the need for the more systematic use of data analytics as a mechanism for developing socially responsive adult learning and workforce education policy and programing.

This chapter presents an overarching overview of how the rather recent technological phenomena, like data mining, machine learning, and artificial intelligence, are applied in the field of education. The author provides examples of how technological developments associated with the so-called Fourth Industrial Revolution are applied in education and considers the benefits and challenges they may bring regarding the economic system, as education (at least in the higher education sector) tends to be monetized and commercialized. The framework for education is perceived in the context of the economic intelligence of states, which is instrumental in ensuring their economic security. It is further expanded to the global scale, as Digital Education is crossing national borders and is being implemented in broader national processes.

Increasingly, groups external to educational systems are offering time, expertise and products, creating an intricate web of educational governance where entities outside of formal education contribute to state-funded education systems. While this involvement and its motivations have been considered in the literature, it has been less common to explore these interactions between school systems and outside organizations as they relate to the transition from the knowledge economy to the intelligent economy. Such research is important to understand the numerous inputs to education, which can then inform future decision-making. This study traces scripts around the commodification of knowledge, which connects education to individual employability or the economy and cyborg dialectic, or the mutual relationship between humans and technology. These scripts intersect to contribute to the perpetuation of data creation and usage as part of the educational intelligent economy. The scripts traced here originate from Battelle, a primarily a Ohio-based research and development organization, also focused on classroom teaching and learning, specifically in STEM (Science, Technology, Engineering and Mathematics) education. Mapping scripts related to the commodification of knowledge and the cyborg dialectic indicates promotion of the intelligent economy broadly and individually for Battelle itself across Ohio and beyond, through investments in educators, students and policy-makers but also Battelle’s potential employees and collaborators. This data-focus creates an educational intelligence not only in students, teachers and policy-makers but also in Battelle itself, legitimating it as an actor in education.

A wave of technological change in the first decades of the twenty-first century is prefiguring a fundamental restructuring of society. Key among the driving forces behind such change are powerful technologies with the potential to exert major transformations on a range of human activities and, crucially, to do so without direct human intervention. The technologies collectively referred to as Artificial Intelligence, or AI represent a productive lens through which to investigate two interrelated transformations: the emergence of self-driving cars and the coming shifts in education. This is in particular because AI’s versatility has led it to be directly applied (and increasingly valued) both in new automated driving technologies, and in the development of new forms of instruction. From the educational perspective, this means that the same technologies that are transforming workforce conditions are also reshaping – directly and indirectly – the approaches, objectives, and experiences of students and educational institutions. This chapter lays out how these twin transformations are likely to play out in the case of the automotive industry and the educational pathways of two occupations closely associated with it: automotive engineers and repair technicians. Two key arguments underpin this examination. First, educational programs for these two occupations, (and beyond) should be broadened to develop versatility and adaptability through tools and perspectives that allow people to move vertically within organizations and laterally across industries in the face of rapid technological change. Second, these educational programs must explicitly tackle AI and the coming technological revolution from a variety of dimensions that connect technical skills acquisition with the context on how these technologies are incorporated in society, how they are governed, and what are the various responses to them. This will allow students and professionals to navigate a rapidly changing labor landscape better while endowing them with the vocabulary to actively participate in the debates that shape its construction.

Educational intelligence can be considered a prized asset in political actors’ careful calculations in setting policy agendas for radical educational transformations in the age of the Fourth Industrial Revolution characterized by Big Data, Artificial Intelligence (AI), machine learning, and the Internet of Things (IoT). As an agent of globalization, the European Union (EU) is uniquely positioned to steer the direction of this new wave of digital technologies for two cardinal objectives in the EU’s rhetorical discourse: social cohesion and economic prosperity. Conversely, its complex governance architecture, which restricts its role in educational policy, tempers its ability to drive policy reforms in education for the strategic and coordinated deployment of Big Data in educational systems to support those twin objectives. This chapter examines this burgeoning policy arena in the European Union by interrogating the most recent policies on the “data economy” enacted at the EU-level and the positionality of education in this newest wave of policy formulation. A content and discourse analysis of policy documents on Big Data reveals that the EU is launching multiple initiatives to regulate these novel technologies across its socio-economic sectors. However, the amorphous nature and unpredictable impact of these technologies, along with the jurisdictional barriers in the education sector stemming from the delimitation of governance layers in the EU, pose difficulties in generating a coordinated approach to policy implementation to engender tangible results. Hence, the contours of an educational intelligent economy in the EU needs considerable policy attention and technical resources in its transition from the current ideational stage to its concrete manifestation.

The coming of Big Data is offered as a salve that will reduce global inequalities and grow national economies. The chapter pursues how notions of progress have traveled into schooling through technology and generate differences and exclusions in the past and present. The chapter explores how transnational school reforms during the colonial era were directed to adapting education to “the African,” which connected expertise in the U.S., UK, and Africa through a shared set of standards, principles, and values about what constituted civilization and development. In school reforms today, the “African” has disappeared today in favor of the “all”; however, residues of educational values and judgments that made up the African as a peculiar and pathological target of colonial schooling still haunt the present. The chapter argues that today’s transnational school reforms continue to presume target communities are passive, pathological objects whose transformation depends upon their learning to act rationally. Whereas in the past this was envisioned as individuals’ and communities’ assimilation through surveys and questionnaires, today rationality is managed through integration in systems and optimizing users’ choices through data mining and algorithms. The narrative of data as grounding rational thought and action is a seductive one that offers optimism to schooling; however, faith in the coming of technology impairs historical reflection and ethical reflexivity toward schooling’s values and judgments, and the differences and exclusions they generate.

New technology tends to invite speculation on the future of societies, inspiring visions of both hope and horror. This chapter continues that tradition, exploring the application of emerging technology, such as artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) to processes of governance and learning in education. Drawing on both utopian visions and twin nightmares of machinic-dystopias, the analysis reflects on the application of new technology designed to fulfill the UN’s post-2015 agenda in education. In highlighting divergent traditions, the analysis then shifts to the application of the same technology in China as part of the Chinese Dream, under which the Chinese government aims to become the world leader in AI while revitalizing the nation’s cultural traditions. These ambitions are explored through the introduction of Smart Cities, a system of Social Credit, and Smart Schools. Finally, the chapter reflects on these visions of twenty-first century pedagogy and possible resources for thinking about a future that cannot be fully apprehended.

This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.