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

Executive Summary

Building a conceptual foundation for a lifelong learning measurement framework

This report develops a conceptual foundation for a measurement framework that captures learning trajectories across the life course and diverse contexts, and enables systematic assessment of how policies shape participation and outcomes. It provides policymakers with a comprehensive approach to understanding how lifelong learning (LLL) systems function, where gaps exist, and how interventions can strengthen opportunities across populations.

LLL has become a central policy lever for productivity, inclusion, and resilience in the face of demographic ageing, technological change, and evolving skill demands. Yet existing data systems were designed mainly to measure educational attainment and training participation at specific life stages. They do not adequately capture learning as a lifelong and life-wide phenomenon, creating four major limitations that constrain policy design and evaluation.

First, coverage is too narrow across the life course. Most international measurement focuses on adults aged 25-64, offering limited visibility into learning outside formal schooling in childhood and excluding adults over 64. Learning through families, communities, and digital environments is largely invisible, despite its growing importance. As a result, critical phases of human development, skill maintenance, and the links between them remain poorly understood.

Second, measurement is largely cross-sectional, providing static snapshots rather than tracking learning over time. Current instruments do not follow how skills evolve through key life transitions such as entering the workforce, changing occupations, forming families, or retiring. The absence of systematic longitudinal linkages between surveys like the Programme for International Student Assessment (PISA) and Programme for the International Assessment Competencies (PIAAC) prevents a coherent view of skill development across decades. Policymakers cannot assess whether early investments yield long-term returns or identify where trajectories diverge.

Third, outcomes are too focused on the labour market. While employability and earnings matter, learning also produces broader social benefits, including civic participation, health literacy, social cohesion, personal agency, and wellbeing. These wider returns are largely absent from current metrics, limiting understanding of the full value of LLL and narrowing policy debates to economic productivity alone.

Fourth, informal learning is insufficiently captured. Much skill development occurs through everyday activities in workplaces, families, communities, and increasingly through digital resources such as online tutorials and platforms. These forms of learning remain weakly measured, creating blind spots precisely where learning is expanding most rapidly.

A three-pillar framework for measuring lifelong learning systems

The proposed framework organises measurement around three interconnected pillars: demand, supply, and coordination. Together they provide a system-level perspective for monitoring and policy analysis, recognising that LLL systems depend not only on provision but also on how well supply aligns with needs and how effectively individuals are connected to opportunities.

Demand captures learning needs at three levels. At the individual level, it refers to skill gaps and capabilities required for work and daily life. At the organisational and community level, it reflects skill requirements within firms, sectors, and public services. At the societal level, it encompasses system-wide pressures such as demographic ageing or technological change that generate population-wide learning needs.

Supply maps learning opportunities across four institutional clusters: formal education systems; employers and workplace learning; welfare and labour-market programmes; and civic and community organisations such as libraries and voluntary associations. Countries vary significantly in how these clusters are developed, accessed, and integrated, which shapes participation patterns and outcomes.

Coordination refers to the mechanisms that align demand and supply. Strong provision and clear needs are insufficient if individuals cannot access or navigate appropriate pathways. Coordination includes governance arrangements, policy instruments such as qualifications frameworks, and guidance services that help learners make informed choices. It is the core function that converts available opportunities into effective learning pathways.

Differences across these three pillars explain variation in LLL systems and outcomes across countries. Systems with similar levels of investment can produce very different results depending on how well supply matches demand and how effective coordination mechanisms are. This perspective helps diagnose system weaknesses and understand why some reforms succeed while others fail.

Redefining what counts as learning in a hybrid, digital context

The framework also updates how learning is classified. The traditional distinction between formal, non-formal education, and informal learning remains useful but is increasingly strained by digitalisation and the emergence of hybrid learning models and (ex-post) certification mechanisms.

New forms of provision blur these boundaries. Micro-credentials, for example, may resemble formal education in structure but function like non-formal training outside traditional degree pathways. Platform-based learning enables organised informal learning that is structured and scalable but not institutionally embedded in formal systems.

To address this, the framework proposes a two-dimensional representation of learning. The horizontal axis captures the degree of formalisation, ranging from highly structured, credentialed provision to unstructured learning embedded in daily life. The vertical axis captures the degree of digitalisation, from fully face-to-face to fully online, including hybrid formats.

This approach allows emerging and mixed forms of learning to be properly classified and measured. It also has direct implications for data collection: if surveys are not designed to capture blended and boundary-crossing forms of learning, they will continue to underestimate where and how learning occurs.

Strategic pathways to implementation

Operationalising the framework requires investment in data infrastructure that builds on existing systems while addressing key gaps. Five implementation streams should proceed in parallel.

First, survey instruments should be extended and upgraded to cover the full life course and better capture informal learning, motivations, barriers, and contexts. Measurement of digital learning should go beyond simple online/offline distinctions to include delivery modes, platforms, and AI-supported learning. New items should be piloted internationally to ensure comparability.

Second, longitudinal linkages should be developed to improve life-course visibility. Connecting adolescent measures (e.g. PISA) with adult outcomes (e.g. PIAAC) would allow tracking of learning trajectories over time. Integrating LLL modules into national longitudinal studies and linking survey data with administrative records would further strengthen analysis, provided robust privacy safeguards are in place.

Third, administrative data systems should be harmonised and integrated across sectors. Common definitions and reporting standards would improve data quality and comparability. Pilot projects could link learning records with outcomes in employment, health, and civic participation, enabling a fuller assessment of returns to learning investments.

Fourth, cumulative learning records or “skills passports” should be scaled. These would integrate formal qualifications, non-formal certificates, and workplace competencies into portable, individual-controlled profiles. Such systems would make prior learning visible, reduce duplication, and support more efficient learning pathways, while requiring strong standards for interoperability and privacy.

Fifth, advanced methods should be deployed to expand measurement capacity. AI-enabled analytics can link platform data with survey data, while participatory logging via mobile applications can capture informal learning in real time. These approaches should be piloted carefully to address issues of bias, data quality, and participant burden.

Together, these steps would create a coherent measurement infrastructure capable of supporting evidence-based lifelong learning policy. By moving from fragmented data toward a comprehensive system that captures learning across the life course, contexts, and outcomes, the measurement framework supports more effective, equitable, and responsive LLL systems.