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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.

Drawing on assemblage theory (Deleuze & Guattari, 1987; DeLanda, 2006), this conceptual chapter seeks to provide an analytical lens for examining the power and capacity of Big Data analytics to exercise territorializing and deterritorializing effects on compound polities and supranational organizations. More specifically, the modern massive agglomeration of data streams and the accelerated computational power available to sort and channel them in effecting actions, decisions, and reconfigurations in contemporary assemblages, necessitate new exploratory tools to examine the impact of such trends on educational phenomena from a comparative perspective. In the first part, the chapter builds an analytical instrumentarium useful in theoretically elucidating the effects of Big Data on complex assemblages and serves as a methodological extension in investigating the ramifications of these effects on educational systems, spaces, and policyscapes. The second part sets out to illustrate how assemblage theory can explain the tension between the formal use of large official statistical data sets as a type of “regulated” Big Data, and the informal use of social media, as a type of “unregulated” Big Data, to construct or deconstruct, respectively, interlacing/interlocking components of assemblages, such as supranational organizations or compound polities. The European Union (EU) and the Caribbean Community (CARICOM) are taken as examples of complex assemblages in which the long-standing utilization of EU’s Eurostat and CARICOM’s Regional Statistical Database have served as territorializing forces in consolidating policy logics and in legitimizing decision-making at the supranational level, while the emergence of “loose” social networking technologies appears to have deterritorializing effects when employed deliberately to delegitimize or subvert socio-political processes across supranational polities.

Over the past few years, assemblage theory or assemblage thinking has garnered increasing attention in educational research, but has been used only tangentially in explications of the nature of comparative and international education (CIE) as a field. This conceptual examination applies an assemblage theory lens to explore the contours of CIE as a scholarly field marked by its rich and interweaved architecture. It does so by first reviewing Deleuze and Guattari’s (1987) principles of rhizomatic structures to define the emergence of assemblages. Secondly, it transposes these principles in conceiving the field of CIE as a meta-assemblage of associated and subordinated sub-assemblages of actors driven by varied disciplinary, interdisciplinary or multidisciplinary interests. Finally, it interrogates the role of Big Data technologies in exerting (re)territorializing and deterritorializing tendencies on the (re)configuration of CIE. The chapter concludes with reiterating the variable character of CIE as a meta-assemblage and proposes ways to move this conversation forward.

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.

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.

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.