A Conceptual Foundation for the Development of a Lifelong Learning Measurement Framework

2. Enabling societal coordination and interactions between citizens’ needs and structures

Abstract

This chapter examines coordination as the integrative pillar of lifelong learning (LLL) systems, linking learning needs (demand) with learning opportunities (supply) across the life-course. It explores how coordination mechanisms align individuals, institutions, communities, and policy actors to address information gaps, incentive misalignments, governance fragmentation, and skill imbalances. It analyses how learning pathways are shaped by accumulated educational experiences, life transitions, and different approaches to articulating learning needs, ranging from state-led to community- and individual-driven models. It also discusses the role of data, governance, and policy instruments in anticipating and responding to changing learning demands.

The coordination pillar serves as the integrative mechanism of the LLL framework, aligning learning needs (demand) with available opportunities (supply) to ensure pathways that are responsive, equitable, and adaptive across the life course. It facilitates interactions among individuals, families, organisations, communities, and national institutions, enabling the identification of learning needs, the mobilisation or subsidisation of appropriate provisions, and, to varying degrees, the fulfilment of individual aspirations.

This pillar addresses core policy design challenges, including information asymmetries (e.g. learners unaware of available opportunities), incentive misalignments (e.g. underinvestment in general skills), externalities (e.g. societal benefits not captured by individual or market decisions), and fragmented governance across institutional domains. Effective coordination requires multi-level, multi-actor, and multi-instrumental approaches that operate through structured pathways, transitional support, and adaptive feedback mechanisms.

Coordination extends beyond labour market transitions to encompass life-course junctures such as youth civic engagement, family formation, migration, and retirement. For younger individuals, it connects formal schooling with non-formal and informal initiatives that foster inclusion and capability development, particularly for disadvantaged groups. For older adults, it bridges health, welfare, and education sectors to support cognitive resilience, digital inclusion, and intergenerational learning.

Drawing on complexity and actor-network theories (Prigogine and Stengers, 1984[1]; Latour, 2005[2]), this section conceptualises LLL ecosystems as dynamic networks of emergent interactions that require multi-level governance to buffer disruptions, such as demographic ageing or technological change. As the synthesising pillar, coordination links demand and supply, informs policy design, and strengthens data strategies for measurement and alignment.

2.1. History and accumulation of interactions: educational careers and pathways

An individual’s learning biography traces the development of their capabilities, their acquisition, maintenance, and potential erosion, across knowledge, skills, attitudes, and capabilities from early childhood through to later life. This learning biography links directly to the demand pillar, revealing how early capabilities shape LLL needs and aspirations, and to the supply pillar, showing how diverse provisions, ranging from formal education to informal, family-, or employer-supported opportunities, expand or channel accessible pathways. Coordination operates as the integrative mechanism on two levels:

• Actual coordination between demand and supply, often a pragmatic "make-do" alignment driven by real-world decisions and resource flows (e.g. state funding for vocational training, employer-sponsored upskilling, or community-supported informal learning). These interactions generate learning data points (concrete instances of learning occurrence) at the intersection of demand and provision, forming the empirical backbone of LLL trajectories.

• Envisioned coordination, where policymakers proactively or responsively design systems based on available evidence, stakeholder priorities, and strategic goals. While large-scale assessments like the Programme for International Student Assessment (PISA), the Programme for the International Assessment of Adult Competencies (PIAAC), or the EU Adult Education Survey (AES) inform policy redesign, they rarely drive real-time alignment; instead, coordination emerges from institutional mechanisms, governance models, and adaptive interventions that respond to observed patterns in learning data points and demand.

Thus, coordination not only bridges gaps but actively constructs viable pathways, enabling individuals and systems to build on strengths, leverage existing resources, and adapt to evolving opportunities across life stages.

The central premise is that educational experiences accumulate over time, creating path dependencies in which early advantages or disadvantages compound in line with the “Matthew principle” (Boeren, 2016[3]). Coordinated interventions are therefore required to break cycles of inequality (Giddens, 1984[4]) and strengthen access to learning opportunities.

To take the lifelong dimension seriously in understanding how changing learning needs interact with actors’ opportunity structures, the data collection on LLL must be significantly expanded. On the one hand, it is essential to broaden the conventional age framework (typically 16–64 or 25–64 years) by capturing more data on early and late LLL. This can be achieved through the use of administrative datasets or, in the case of OECD data collections, by establishing stronger longitudinal tracking and linkages between respondents in the PISA and PIAAC programmes. On the other hand, traditional adult education surveys should be expanded to include older age cohorts, extending participation beyond 64 or 69 years. Doing so would provide deeper insight into LLL in later stages of the life course, particularly the mechanisms that support the retention of cognitive and other abilities.

2.2. Co-ordination of learning needs: optics on who and what decides the learning needs

Co-ordination of learning needs within LLL ecosystems is shaped by the interaction of governance structures, institutional arrangements, and cultural contexts. These factors influence how priorities are identified, articulated, and addressed – whether by matching them to existing provision structures or by designing and implementing new measures.

This process navigates the convergence or divergence of individual and collective interests, which may lead to either consensus or conflict (see Section 3, Figure 3.1). Drawing on structuration theory (Giddens, 1984[4]) and recent policy literature (Busemeyer, Carstensen and Emmenegger, 2022[5]), we argue that such decision-making is mediated by power relations, stakeholder interactions, and prevailing governance logics. Consequently, LLL governance must be adaptive to local and national realities.

These dynamics directly shape the demand pillar by revealing how learning needs are expressed within society. This, in turn, influences the articulation of national LLL policies – not only the selection of target groups and the variation between them, but also the types of LLL activities countries prioritise in their policy design (formal education vs. less organised types of learning).

To capture this variation, we propose a typology of approaches to articulating learning needs, based on two dimensions: top-down (centralised authority from state or sectoral entities define needs) versus bottom-up (decentralised, community- or individual-led approach to articulation of learning needs); and collective/sectoral (group-oriented priorities among learning needs) versus individualised (person-centred focused on learning needs).

Crossing these axes produces four ideal types:

• Top-down collective – state-led strategies addressing broad population needs (e.g. compulsory education or national reskilling programmes).

• Top-down sectoral – targeted interventions for specific groups or industries, often based on the needs of employers or sectoral associations (e.g. vocational education and training, ICT and tech industry certification, or automotive industry).

• Bottom-up collective – community-driven identification of shared learning priorities (e.g. cultural associations, social movements approach).

• Bottom-up individualised – personalised learning agendas driven by individual aspirations.

Beyond these ideal types, mixed approaches could also exist, such as neo-corporatist arrangements that blend state oversight with structured stakeholder input (Desjardins, 2017[6]).

Top-down collective approaches prioritise state-led strategies to advance societal needs, such as social inclusion and economic competitiveness, often through integrated national skill formation systems at the macro-social level. Mandatory education for younger people is probably the most obvious pillar of this approach, which is adopted in the early years in all OECD countries, often with state-or jurisdiction-driven curricula. The Nordic institutional model of LLL (Tuijnman, 2003[7]; Rubenson, 2006[8]), exemplified by current Sweden, integrates LLL within a wider welfare regime, ensuring equitable access through generous public funding. According to AES 2022–2023 (Eurostat, 2025[9]), this model achieves a 74% adult participation rate in formal and non-formal education, significantly reducing barriers for low-skilled groups (Nylander and Rubenson, 2025[10]). Such frameworks deliver strong macro-level alignment but risk overlooking local variation, making stakeholder consultation essential to reconcile national priorities with regional realities.

In top-down sectoral approaches, often led by industry and state interests, vocational education and training is primarily oriented towards meeting immediate economic objectives and sectoral needs. This model tends to prioritise the needs of employers and sectoral associations, particularly in contexts such as the United States and the United Kingdom, where employer-led reskilling initiatives are designed to address short-term market demands (Busemeyer and Trampusch, 2012[11]; Desjardins, 2017[6]). As a result, groups of workers whose skills are perceived as having lower economic value and those who are outside of the labour market may receive less attention. Moreover, such approaches often downplay learning needs linked to wider societal aspirations and personal development, such as wellbeing, civic participation, and cultural engagement. In countries where such approaches dominate, data strategies could be designed to serve a dual purpose. First, they should enhance alignment between labour market needs and training provision by tracking sectoral skill mismatches, thereby informing responsive policies that integrate demand, supply, and coordination into adaptive learning ecosystems. Second, they should help reduce tensions when sector-specific objectives conflict with individual LLL needs related to wellbeing and civic capabilities. By doing so, they could support the development of mechanisms within the LLL ecosystem that foster more effective public-private partnerships, balancing efficiency with equity.

Bottom-up collectivised approaches harness community-led processes, often through NGOs or social movement networks, to identify, aggregate, and articulate shared learning needs. For example, Botswana’s literacy campaigns demonstrate how grassroots networks, working in public – NGO partnerships, can amplify marginalised voices and influence policy agendas (UNESCO, 2019[12]). By prioritising empowerment, these governance models challenge resource constraints and promote more participatory, inclusive forms of decision-making. We argue that the articulation of LLL needs within this model can be further strengthened through measures outlined in Section 7 and Annex B. One promising avenue is participatory data logging, where community members contribute directly (e.g. via mobile applications, local hubs, or facilitated workshops) to shared databases of learning needs and opportunities. This collective knowledge base enables policymakers and providers to design programmes that reflect community priorities, enhancing inclusivity, responsiveness, and long-term sustainability in diverse settings.

Bottom-up, individualised approaches emphasise personal agency in identifying and articulating learning needs. For adult learners, such approaches are common in countries with limited LLL policies and governance, where responsibility for LLL rests largely with individuals. However, this model often overlooks key constraints, particularly the socioeconomic barriers faced by disadvantaged adults, as well as the challenges many individuals encounter in formulating their learning needs and translating them into tangible learning demands.

Integrating these typologies reveals systemic tensions, such as conflicts between top-down collective goals (e.g. national reskilling for automation or "green transition") and bottom-up individualised pursuits (e.g. hobby-driven learning), which can generate mismatches between individual and societal needs and actual LLL practice, undermining effectiveness, adaptability, and equity of LLL systems.

2.3. Coordination of current and future learning needs: navigating life transitions and aspirations

Coordination within LLL systems must address the evolving needs that emerge at key life-course transitions: nonlinear junctures such as career changes, family formation, migration, or retirement. In their case, individual aspirations intersect with external forces, including automation-driven obsolescence or demographic change. Building on the discussion of biographical accumulation and coordinated transitions in Section 2.1, these moments create new learning aspirations and needs and require connecting demand with supply. They require institutional measures that ease navigation and minimise disruption.

In this regard, real-time instruments, such as skill audits embedded in digital platforms (Google, 2025[13]), could generate reusable data for predictive alignment. A focus on coordinated transitions thus ensures that micro-level needs (e.g. reskilling for career shifts) are met by meso-level provisions (e.g. workplace training).

Nonetheless, compounded barriers – shaped by intersections of gender, socioeconomic status, migration, or age – can intensify exclusion, with dispositional constraints often obscuring underlying needs (Kalenda and Kočvarová, 2022[14]; Kalenda, Boeren and Kočvarová, 2023[15]). Participatory methods, such as community assessments, collaborations with NGOs, or sensitive techniques of interviewing, like the score-card method (Broek et al., 2025[16]), give voice to marginalised groups and support inclusive coordination, thereby reducing disparities and aligning policy design with equity.

Platformisation theory (van Dijck, Poell and De Waal, 2018[17]) adds further nuance, underscoring how digital platforms may expand access while simultaneously risking exclusion through algorithmic curation and inequalities based on the initial digital skills (Ma, 2023[18]; Deng and El Hag, 2024[19]; Karger et al., 2024[20]). By targeting transitional phases, LLL ecosystems can cultivate purpose and resilience, integrating the system’s pillars into comprehensive, stakeholder-responsive arrangements.

2.4. Skills surplus, deficits, and shortages: impacts on organisations and communities

Skill imbalances, whether surpluses (overqualification), deficits (underutilisation within roles), or shortages (labour market gaps), emerge from dynamic interactions across macro, meso, and micro levels of coordination (Brown, Green and Lauder, 2001[21]; Desjardins, 2014[22]). These imbalances reflect not only labour market dynamics but broader coordination challenges in aligning learning needs with opportunities throughout the life course (Milana et al., 2025[23]; Hodge et al., 2025[24]).

At the macro level, imbalances signal systemic coordination gaps. Shortages in emerging sectors (e.g. green or digital industries) may stem from delayed policy responses, while surpluses among older or migrant workers indicate underutilised human capital due to institutional or cultural barriers (Cedefop, 2025[25]). Deficits in foundational skills across cohorts highlight gaps in lifelong access to learning opportunities.

At the meso level, organisations and communities experience imbalances as operational and social constraints. Surpluses can demotivate workers and reduce engagement, while deficits in adaptive or collaborative skills hinder innovation and resilience (Cedefop, 2025[25]). Shortages in caregiving or community leadership roles strain local support systems, particularly in ageing or rural contexts.

At the micro level, individuals face imbalances during life transitions. A worker with surplus formal qualifications but a deficit in digital skills may struggle with career mobility. Conversely, informal skills developed through volunteering or family responsibilities may remain unrecognised, limiting access to formal opportunities.

Timing is critical in co-ordination. Proactive coordination – through skill forecasting and early intervention – prevents imbalances from entrenching during key transitions (e.g. school-to-work, mid-career reskilling, retirement) (Section 2.3). Responsive coordination enables real-time adaptation via flexible instruments such as micro-credentials or recognition of prior learning.

Effective coordination across levels requires:

• Integrated data to detect emerging imbalances (e.g. linking AES with employer training records).

• Adaptive instruments (e.g. modular training, skills passports, community hubs) to convert surpluses into assets and close deficits swiftly.

• Multi-actor governance involving employers, welfare agencies, and civic networks to align incentives and share coordination costs.

Agent-based modelling (ABM) can simulate these multi-level interactions, helping anticipate outcomes and design resilient coordination strategies (Squazzoni and Gandelli, 2014[26]; Combs et al., 2022[27]). By framing skill imbalances as coordination challenges across levels and life stages, rather than isolated market failures, this approach supports adaptive, inclusive pathways that enhance resilience at individual, organisational, and societal scales.

2.5. Policy instruments for effective coordination in LLL

Effective LLL systems are built on a carefully designed suite of policy instruments that address coordination failures – such as information asymmetries, misaligned incentives, and unpriced externalities – while promoting equity, autonomy, and wider societal benefits. These instruments span formal education, labour market, and welfare domains, complemented by direct LLL measures. Their common purpose is to align stakeholder interests, reduce mismatches between supply and demand, and ensure that learning opportunities contribute to both individual empowerment and collective resilience.

Foundational policy instruments for LLL coordination are qualifications and education structures:

• Open, flexible, and permeable formal education structures equip the younger population with a minimum level of education and prepare students for further learning. They are financed mainly through public investment, which counters inequality by offering free public education (for some time) and modular, stackable pathways (e.g. micro-credentials, laddered degrees). These opportunities enable learners, particularly those from disadvantaged groups to re-enter and progress through education at various stages of life.

• Qualifications systems provide a structured framework for certifying skills, enabling both labour market mobility and personal development. Innovations such as the recognition of prior learning extend access to diverse groups, ensuring that non-formal and informal learning achievements are valued.

Labour market policy instruments then aim to align skills and economic change:

• Active labour market policies, including targeted training subsidies or job-matching schemes, help align workforce capacities with shifting economic demands, mitigate risks of unemployment, and support productivity and innovation.

• National workforce strategies that anticipate structural change – for instance, in the green economy or digitalisation – can set priorities for reskilling and upskilling at scale, creating coherence across regional and sectoral initiatives.

Equity instruments support learning for welfare and targeted support:

• Welfare policies and social safety nets support participation in lifelong learning by reducing the opportunity costs of learning, such as lost income during training.

• Targeted measures for disadvantaged groups (including migrants, low-skilled adults, or workers at risk of automation) address dispositional and situational barriers. Tailored financial support, guidance, and preparatory programmes strengthen inclusion and widen the base of lifelong learning participation.

Finally, direct LLL instruments provide guidance and digital innovation:

• Guidance systems play a pivotal role in connecting learning needs with appropriate opportunities. By reducing information asymmetries, they ensure that individuals can make informed choices about training, upskilling, and career transitions.

• AI-enabled guidance platforms expand these possibilities. Drawing on real-time labour market intelligence, such systems can generate personalised recommendations, anticipate emerging skills demand, and map dynamic career trajectories beyond traditional linear ladders. They can also support firms by identifying workforce skill gaps and recommending tailored upskilling pathways.

The effectiveness of all these instruments ultimately depends on governance arrangements that secure legitimacy and coherence. Negotiated settlements, such as tripartite agreements between governments, employers, and trade unions, embed social capital into institutional design. By aligning diverse interests, they help overcome coordination failures, balance competitive pressures with collaboration, and reinforce trust in the system.

In sum, resilient LLL systems combine foundational qualifications and education structures with targeted labour market and equity instruments, direct guidance measures, and robust governance. Their effectiveness lies not in isolated tools but in their integration: instruments must reinforce one another across individual, organisational, and national levels, creating learning ecosystems that are adaptive, inclusive, and future-oriented.

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