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This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education.

We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4Vs of Big Data – volume, velocity, variety, and veracity – help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL “as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting” (p. 1, in press).

Using the guiding frameworks, we identified four educational data crossroads as follows:

Crossroad 1: Unintended Increase in Workload Volume;

Crossroad 2: Unrealistic Expectations of Data Velocity;

Crossroad 3: Data Variety in Silos; and

Crossroad 4: Data Veracity and Policy Agenda Mismatch.

In this paper, we explain each of these crossroads in more detail with some examples.

Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education.

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.

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.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

The shift from data-informed to data-driven educational policymaking is conceptually framed by institutional and transhumanist perspectives. Examples of the shift to large-scale quantitative data driving educational decision-making suggest that data-driven educational policy will not adjust for context to the degree as done by the data-informed or data-based policymaking. Instead, the algorithmization of educational decision-making is both increasingly realizable and necessary in light of the overwhelmingly big data on education produced annually around the world. Evidence suggests that the isomorphic shift from localized data and individual decision-making about education to large-scale assessment data has changed the nature of educational decision-making and national educational policy. Big data are increasingly legitimized in educational policy communities at national and international levels, which means that algorithms are assumed to be the best way to analyze and make decisions about large volumes of complex data. There is a conceptual concern, however, that decontextualized or de-humanized educational policies may have the effect of increasing student achievement, but not necessarily the translation of knowledge into economically, socially, or politically productive behavior.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.

In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.

So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.

Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

The purpose of this paper is to overcome the problems that skill mismatch cannot be measured directly and that demand side data are lacking. It relates demand and supply side characteristics by aggregating data from jobs ads and jobholders into occupations. For these occupations skill mismatch is investigated by focussing on demand and supply ratios, attained vis-à-vis required skills and vacancies’ skill requirements in relation to the demand-supply ratios.

Vacancy data from the EURES job portal and jobholder data from WageIndicator web-survey were aggregated by ISCO 4-digit occupations and merged in a database with 279 occupations for Czech Republic, being the only European country with disaggregated occupational data, coded educational data, and sufficient numbers of observations.

One fourth of occupations are in excessive demand and one third in excessive supply. The workforce is overeducated compared to the vacancies’ requirements. A high demand correlates with lower educational requirements. At lower occupational skill levels requirements are more condensed, but attainments less so. At higher skill levels, requirements are less condensed, but attainments more so. Educational requirements are lower for high demand occupations.

Using educational levels is a limited proxy for multidimensional skills. Higher educated jobholders are overrepresented.

In Europe labour market mismatches worry policy makers and Public Employment Services alike.

The authors study is the first for Europe to explore such a granulated approach of skill mismatch.

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.