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This contribution argues that there is a fundamental problem for the multi-level governance (MLG) approach in that what the approach is trying to explain has never been fully agreed by the vast group of scholarship that references it. The chapter then examines and proposes that ideas and concepts from network governance, principal–agent (PA) and learning can provide the necessary micro foundations for the MLG approach.

The chapter examines and critiques the original MLG formulations and the later efforts at elaboration. It then reviews the literature and concepts for three public policy approaches that have been associated with European governance to see how core explanations can be elaborated upon in a multi-level context: network governance, principal–agent (PA) and learning.

This contribution suggests that co-ordination, and the resources that help maintain this co-ordination, is the key dependent variable that underpins the MLG approach. With multiple principals and multiple agents, operating at a number of levels of analysis, direct authority and control is harder to evoke. The key explanatory variable underpinning this MLG co-ordination is learning by the participants.

Researchers need to concentrate both their theoretical and empirical efforts in understanding the conditions that support multi-level governance and that sustain its effort.

The contribution outlines some of the key practical questions that policy-makers must face. Can they manage resources and induce learning from all the relevant public and private stakeholders to engage in the MLG effort?

Not only does an effective MLG process involve engaging a wide range of societal stakeholders, these stakeholders have to be persuaded to invest effort in learning about the nature of the governance system, the challenges of the policy problem and the implications of the efforts to resolve these problems.

This chapter isolates the fundamental lacuna at the heart of the MLG project and offers academics and practitioners a conceptual lens for building a clearer analytical structure for studying MLG.

This chapter reviews the theoretical literature about Europeanisation and argues the need for further analysis of post-Soviet Europeanisation. This chapter also connects post-Soviet Europeanisation to the notion of policy learning, which is introduced as a theoretical perspective. The chapter discusses the challenges around the definition of policy learning in relation to other policy processes such as transfer, translation and diffusion. Policy layering does not presuppose mutual exclusion between path-dependence and change, but rather the fruitful mutual development of both. It implies a gradual change of certain policy aspects and the retaining of others. The development of the links between path-dependence and change in layering is a highly messy process. Multiple actors participate in learning, and they create policy as they learn. There are no distinct stages of learning, and the line between policy-makers and practitioners is often blurred.

The difference between policy-making on the European level and the post-Soviet domestic context is significant. The literature about post-Soviet countries recognises the presence of a struggle between Europeanisation and post-Soviet legacies there. Europeanisation in this literature is presented as change; while the influence of the post-Soviet legacies is seen as an obstacle that hinders it. The policy learning concept is key here to frame Europeanisation in the post-Soviet context as an area of enquiry which may develop according to the logic of layering.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

This chapter focusses on the situation of part-time learners and explores the extent to which policy in England has confounded, rather than facilitated, lifelong learning opportunities. A brief overview of Lifelong Learning policy at Pan-European level is presented with the findings of a specific project which sought to establish what the barriers were to access for diverse student bodies in England, Denmark, Finland and Germany. Then, the authors focus on the ‘perfect storm’¹ in English Higher Education where a catastrophic decline in the numbers of part-time student, generated due to the clash of several policy ‘clouds’, raises questions about the government’s commitment to lifelong learning.

The process of differentiating each of the dimensions of learning is demonstrated by the application of three possible conceptual frameworks for each dimension, which are based on the theories of learning, instruction, and environment. Multiple existing theories apply to each dimension of the curriculum, including one framework that is a synthesis of several related theories. The purpose of this chapter is to demonstrate how theories may be adapted into design templates and used to configure the components of the curriculum. The outcome of this process is to create coherent curricula through the practical application of theories of learning as design templates.

A blueprint template is presented to visualize the internal alignment, interconnectedness, and overall coherence of each curriculum. This template visually depicts the functional interactions between the curricular components as dynamic relationships. This tool reveals the design relationships within the curriculum for purposes of design and evaluation. For curriculum design purposes, this form is used to establish and maintain the alignment among the dimensions of a curriculum (horizontally in the template) as well as the interconnectedness of the components. Engagement with the learning process begins by translating the content of each learning objective into instructional objectives, which aligns the instructional components with each learning objective. The instructional objectives are configured to align the content and structure contained in the outcomes and objectives with the instructional components. In this curriculum design system, the instructional taxonomies of Bloom, Engelhart, Furst, Hill, and Krathwohl (1956) are adapted as design templates to demonstrate three strategies to configure the structure of the learning engagement dimension into three distinct purposes of developing cognition, skills, or values within each dimension (vertically in the template).

The learning experience in this curriculum demonstration differentiates three distinct instructional functions: the learning of thinking skills, the learning of performance skills, and the learning of values-based performance. A template adapted from credible theories of instruction configures the specified learning.

Three models also differentiate the learning environment dimension of a curriculum. The learning environment is structured to deliver learning through individual, cooperative, or collaborative processes. Although the environmental considerations mostly impact the activities through which learners interact with the content of the curriculum (reinforcement activities, assignments, assessments), the environmental factors influence all components of the curriculum and can be differentiated to promote and enhance learning. From the learner perspective, the learning environment is created by the dynamic interaction of all components of the curriculum to facilitate an unobstructed path to learning.

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 draws together the findings about both the Bologna actors and instruments to explain the mechanism of the Bologna reform in Ukraine until 2014 and its place in Europeanisation in the post-Soviet context.

This research demonstrates that continuity was mainly perpetuated by the Ministry of Education and Science, and change was facilitated by civil organisations. There was a lot of fluidity in the interaction of old practices and policy innovation in Bologna in Ukraine. The interaction between the path dependency and change was primarily a gradual chaotic, yet creative, and shared build-up of minor innovations by different higher education actors. These innovations in the development of the Bologna instruments may be seen as leading to more substantial transformations over time.

The research findings may also serve as a first step towards a reconceptualisation of the Europeanisation process particularly in the post-Soviet context in the first couple of decades after the collapse of the Soviet Union. Bologna in Ukraine can be seen as an illustration of the ways in which Europeanisation may not always necessitate the elimination of past conventions and practices – indeed, in a policy field such as education, abandoning history and tradition would have been a futile endeavour. Policy continuity in the post-Soviet context may be a foundation in the Europeanisation process during which minor innovations are slowly yet continuously being accumulated. This foundation shapes the nature of changes. Therefore, perhaps, the debate regarding a slow pace of Europeanisation in the post-Soviet space might be erroneous, since it carries a hidden assumption – that it is slow in relation to a much faster Europeanisation and resulting transformations in the EU. Such a comparison should be revisited in light of a potential difference in the nature of Europeanisation in the two spaces and the acknowledgement of growing overlaps between the two spaces as well.