This chapter introduces the report’s three research aims and outlines a multidimensional conceptualisation of lifelong learning (LLL) that could underpin the development of a structured data framework. It situates LLL along both life-long and life-wide dimensions and centres the analysis on the interaction between the demand for, supply of, and coordination of learning opportunities across individual, organisational, and national levels. The chapter concludes by presenting how the remainder of the report is organised, including how subsequent chapters develop these pillars, their measurement, and their policy implications.
A Conceptual Foundation for the Development of a Lifelong Learning Measurement Framework
Abstract
This report introduces a conceptual and data framework to enhance understanding of how individuals and cohorts engage with lifelong learning (LLL) across their lives and to enable examination of the impact of diverse education policies and support measures. The focus is on three interrelated Research Aims (RAs):
RA1: Scoping current approaches to the definition and taxonomy of learning types across the life-course – from early age to elderly.
RA2: Considering factors that shape national differences in LLL.
RA3: Examining options to collect, reuse, or integrate data across the life-course.
The stated purposes are to advance a foundation for identifying current gaps in data and inform future international data collection, as well as to support the ability to articulate the outcomes of LLL at individual, national, and global levels and generate policy lessons for countries aiming to strengthen educational engagement across the life-course (Desjardins and Kalenda, 2025[1]; OECD, 2023[2]).
For reference, the initial OECD/CERI project description and the underlying purpose of this paper are as follows:
"This project would scope the development of a data framework to enhance understanding of how individuals and cohorts engage with learning across the life-course and to enable examination of the impact of different education policies and supports. This would include: Scope current approaches to the definition and taxonomy of the types of learning across the life-course (RA1); Consideration of factors that shape national differences in lifelong learning (RA2); and Examination of options to collect, reuse, or integrate data across the life-course (RA3). This would provide the foundation for the development of such a framework to identify current gaps in data and inform future international data collection. Lifelong learning enables a more responsive and capable citizenry to changing global demands. However, understanding about how individuals and cohorts move through education across their lives is limited, reducing capacity to analyse how engagement is shaped by education policies and broader economic drivers. This project would support the ability to articulate the outcomes of lifelong learning at an individual, national and global level and offers policy lessons for countries aiming to strengthen educational engagement across the life-course."
To address these aims and purposes, we begin by delineating the conceptual space of LLL and its associated data environment. This provides the foundation upon which we construct the core framework guiding our examination of the LLL data landscape. The scope of LLL may be conceptualised along two principal axes (Figure 1.1).
The first is the life-long axis, which represents the temporal continuum of learning across the lifecourse. It encompasses all phases of learning engagement – from early childhood and preschool education, through compulsory schooling and adult education and learning during the mid-life period, to learning that occurs well beyond retirement age. Within this temporal continuum, specific transition points and transition stages can be identified. These transitions may be viewed as part of a continuous and individualised process that can occur at any age or stage, shaped by personal circumstances and learning needs. Nonetheless, specific transition points related to the life-long axis are institutionally or socially structured – for instance, the completion of compulsory education, post-secondary choices, or career transitions in early, mid-, and late adulthood. Others are more diffuse yet still patterned, such as family formation or retirement, which tend to cluster at particular life stages.
The second is the life-wide axis, which reflects the multiplicity of learning contexts and modalities. This dimension encompasses learning that occurs across formal, non-formal, and informal settings, extending beyond educational institutions to include workplaces, families, communities, and civic spaces. The life-wide axis, therefore, captures the relational and contextual nature of learning as an activity embedded within diverse spheres of social life – not only within the school or later within the labour market.
The intersection of these two axes defines the multidimensional space within which all manifestations of LLL can be located. Within this space, we identify learning occurrences (instances of learning activity and engagement) along with their associated proximal and distal outcomes. Each occurrence represents a possible data point within the broader learning trajectory of an individual, potentially arising at any age, in any context, and for varied purposes.
This starting point has several implications for constructing the framework.
First, to develop a genuinely comprehensive LLL data framework, it is essential to collect data from those dimensions of this multidimensional space for which data are currently absent or only partially available. At present, the available data for a research and policy perspective on this wider learning landscape remains fragmented and incomplete. Foremost, significant gaps exist in relation to learning among children and young people outside formal schooling, the oldest age cohorts, and learning that takes place in informal settings. Therefore, this framework explicitly incorporates two groups often overlooked in both data and policy: those under 18 and those over 64. For children and young people, this entails moving beyond formal schooling to acknowledge the growing significance of informal, civic, and digitally mediated learning environments that shape early educational trajectories. For older adults, it calls for a reframing of ageing, not solely in relation to labour-market participation, but as an active stage of cognitive resilience, well-being, and intergenerational engagement. Including these groups secures a genuinely life-wide and life-long perspective.
Second, as learning experiences accumulate along the life-long axis in the case of each individual, we must establish mechanisms to collect and integrate data from different contexts across the life-course in a cumulative manner.
Third, and closely related to the previous point, it is crucial to devise methods for integrating data from diverse stages and learning settings into a holistic representation – one that does not reduce LLL to a single domain or to a specific phase of life.
Fourth, successful life transitions inherently depend on a degree of coordination between the demand for and supply of learning opportunities. Such coordination may be pre-determined by legal or regulatory frameworks, or shaped by selection processes, labour market and career needs, employer requirements, and the aspirations of individuals and communities. Consequently, an LLL framework should not only serve the purpose of systematic data collection for research but also serve as an instrument to enhance coordination of learning opportunities at both the individual and societal levels.
To map a full scope of LLL data space, we have developed a conceptual framework to provide the basis for the proposed data framework, addressing gaps in data collections and proposing new approaches. In building this foundation, we have adopted a “blue-sky” approach, reimagining LLL as a holistic, multi-level, and multi-domain process that transcends traditional labour market-centric models to encompass outcomes such as health and financial literacy, civic engagement, and psychological well-being. Furthermore, it views the acquisition of skills not as separated stages (erg. before formal schooling, during education, and afterwards) but as a continuous process. Likewise, it recognises that knowledge and skills are developed in settings and through forms of learning that are interdependent and extend well beyond the classroom. Consequently, this perspective moves past a narrow focus on labour market demands to encompass diverse life situations and transitions across the life-course (Figure 1.1).
In the framework (Figure 1.2), each learning occurrence (or data learning point) emerges from the interaction between the demand for, and the supply of, learning opportunities. Building on this premise, the LLL framework rests on three interrelated pillars:
Demand for LLL: the recognition of both explicit and latent learning needs that arise from aspirations or gaps in knowledge, skills, attitudes, or capabilities at individual, organisational, and societal levels.
Supply of LLL: the provision of varied learning opportunities across formal, non-formal, and informal modes of learning situated within diverse institutional contexts.
Coordination of LLL: the structuring of pathways and facilitation of transitions that align learning aspirations or needs with available opportunities, underpinned by multi-level policy design and stakeholder cooperation.
The framework adopts a multi-level perspective on coordination and LLL policies, including education policies. Effective coordination must be orchestrated not only in response to individual needs and opportunities but also across organisational and national levels, ensuring alignment and coherence throughout the system.
This multi-level logic also underpins the interaction between the supply and demand for LLL. At the macro level, state policies shape both the provision of and the demand for learning opportunities. At the meso level, employers, organisations, and communities operate as dual actors, serving simultaneously as providers of learning and as entities with their own learning needs. At the micro level, attention shifts to individuals (their learning interests and needs) as well as to their potential role as contributors to LLL. Individuals may themselves act as providers of learning, particularly in informal or less structured settings involving peer or community-based learning.
Furthermore, we distinguish between two principal types of outcomes arising from the interaction of supply, demand, and coordination. On the one hand, there are learning occurrences – i.e. the instances or processes through which learning takes place. On the other hand, there are learning outcomes – i.e. their manifestations, ranging from proximal expressions such as the acquisition of skills, to more distal impacts, including broader social benefits.
Finally, the last key element of the framework is the recognition that capturing the dynamics of these processes requires a robust data ecosystem. Such an ecosystem must serve three interrelated purposes: facilitating smooth and effective coordination, supporting research to uncover the underlying patterns and dynamics shaping supply, demand, and coordination, and informing policy design to enhance coordination mechanisms and address potential market failures.
The current state of data collection, utilisation, and integration regarding LLL reveals significant limitations (e.g. Rubenson (2018[3]; 2019[4]); Elfert and Rubenson (2023[5]); Holford (2023[6]); Draghi (2024[7]); Boeren and Kalenda (2025[8]) and Karger, Kalenda and Vaculíková (2025[9])). Among the most pressing critiques is the fragmentation of data systems that measure learning activities and their outcomes across the life-course; for instance, the link between the assessment of skills acquired in school and those developed later in life. Other challenges include poor comparability across emerging forms of learning (such as blended formats or short online certifications), the narrow age ranges, centred around adults aged 25 to 64 years, covered by most adult learning data collections, the underreporting of needs and aspirations among low-skilled groups, and the predominant emphasis on the economic dimensions of LLL. Not least, the distinct functions and purposes of data do not necessarily feed into each other as well as they could to foster and inform better research, policy design, and coordination. The ultimate goal is a more integrated data ecosystem – one that connects individual survey results (e.g. Programme for International Student Assessment – PISA, and Programme for International Assessment of Adult Competencies – PIAAC), organisational records, and national aggregates into a coherent whole, enabling a system-level approach to knowledge mobilisation regarding LLL (OECD, 2023[10]).
This report develops a conceptual and data framework for LLL that systematically addresses the three research aims. Following the introduction, which outlines the multidimensional conceptual space of LLL, transitions from conceptual framings to data strategies, and presents the core framework, the report progresses through seven core sections, weaving conceptual foundations with practical measurement and policy implications to foster a resilient data ecosystem.
Section 2 establishes coordination as the integrative pillar aligning demand with supply. It traces educational careers, navigates life transitions, addresses skill imbalances at organisational and community levels, and outlines policy instruments for effective coordination, emphasizing multi-level governance to resolve asymmetries and externalities.
Section 3 analyses the demand pillar, clarifying how learning needs and aspirations are formed and expressed across stakeholders. It traces dynamics at three levels – micro (individual), meso (organisational), and macro (national) – and shows how these layers interact to shape demand.
Section 4 maps the supply pillar, detailing the prevalence and diversity of learning opportunities. It critiques the traditional triadic typology of learning activities, proposes revisions accounting for flexibilisation, hybridisation, and digitalisation, and situates provisions within four institutional clusters (formal education, employer, welfare policy, civic), that are mainly responsible for cross-national variations in LLL.
Section 5 synthesises the pillars by exploring the processes, outcomes, and dynamics of LLL systems through stakeholder objectives, interventions, and outcomes. It distinguishes proximal (e.g. skill acquisition) and distal (e.g. societal benefits) outcomes, evaluates diverse assessment approaches, and directs to Annex B for detailed approaches, linking to research, policy design, and coordination.
Section 6 clarifies data purposes and needs for research (pattern discovery), policy design (intervention evaluation and informing the policy process), and coordination (managing real-time alignment and system alignment driven by policy design).
Section 7 advances data and indicator development, reflecting on sources, constraints, and gaps while proposing strategies for cross-sectional and longitudinal survey development, administrative data enhancement, cumulative records (e.g. skills passports), and innovative methods like AI-driven analytics. Detailed exploratory indicators are provided in Annex C and are tied to the framework’s pillars.
Section 8 concludes with recommendations that summarise the framework’s insights, propose implementation pathways (e.g. OECD-led pilots and observatories), and outline next steps for scaling innovations to enhance LLL engagement and equity.
Annexes supplement the analysis: Annex A details theoretical foundations (e.g. capability theory, complexity theory), Annex B outlines research approaches for assessing learning needs and outcomes, and Annex C proposes exploratory indicators and questions for empirical exploration.
This structure progresses from coordination as the synthesising pillar, through demand and supply foundations, to outcome-driven integration and data innovations, ensuring coherence across the research aims while supporting actionable policy lessons for diverse national contexts.
References
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