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

Annex B. Approaches to understanding and assessing learning needs and outcomes

This annex outlines a range of methodological approaches for understanding and assessing learning needs, demands, and outcomes in the context of LLL. It explores how these approaches serve key purposes in coordination, research, and policy design. Each approach is examined for its role in inferring learning gaps across micro, meso, and macro scales; its limitations, such as static snapshots or indirect proxies; and prospects for integration with complementary data sources to enable more dynamic, equitable, and comprehensive insights into capability development over the life course. The annex aims to guide stakeholders in selecting and combining these methods to better align LLL with economic, social, and personal goals, addressing barriers and fostering inclusive transitions.

B.1 Qualification approach: individual, organisational, and collective perspectives

Qualifications are used to certify an individual's knowledge and capabilities at a widely recognised level, gaining broad acceptance among stakeholders like employers, educators, communities, and governments. They serve multiple roles, distinguished as follows:

• Purpose for Coordination: Qualifications facilitate labour-market efficiency by signalling hard-to-observe skills, enabling clear communication across the labour market to match capabilities with opportunities (Spence, 1973[29]). They support governance alignment by fostering negotiated settlements among stakeholders, reducing information asymmetries, and enhancing coordination pathways, particularly in diverse institutional clusters (Rothstein and Stolle, 2008[26]).

• Purpose for Research: Qualifications have played a critical role in empirical applications of human capital and signalling theory, providing measurable but indirect proxies for skills and productivity (Becker, 1964[30]; Arrow, 1973[31]; Marcotte et al., 2005[32]; Hanushek and Woessmann, 2015[33]). In particular, they have been used to decipher participation in lifelong learning, earnings and other outcome differentials; however, debate persists regarding the mechanisms underlying these observed differences.

• Purpose for Policy Design: Qualifications can serve different roles depending on context and perspective. From a critical lens, they may reproduce the unequal distribution of power and resources by favouring privileged groups (Illich, 1971[34]; Bourdieu and Passeron, 1977[35]), yet from a modernist and development perspective, they extend access to knowledge and skills, fostering individual social mobility and advancing society by investing in capabilities for economic and social purposes (Schultz, 1959[36]; Schuller et al., 2004[24]; Marcotte et al., 2005[32]; Cedefop, 2025[37]).

There are several ways in which qualifications can be used to infer learning demands and needs. At a universal level, primary qualifications can be seen to represent a foundational right (UNESCO, 1996[38]), with gaps below this level signalling basic deficiencies in literacy or other essential capabilities. Depending on societal development, goals, and aspirations, higher-level qualifications can infer needs for economic success at micro, meso, and macro levels, such as certifying vocational skills for job entry or advanced degrees for specialised roles essential for economic functioning. They also imply needs and demands related to social and cultural development, including meeting everyday requirements, from civic responsibilities (e.g. understanding taxes) to health management (e.g. interpreting medical advice) and consumer choices (e.g. evaluating products). In advanced post-industrial market democracies, lacking upper secondary education is often interpreted as a marker of unresolved needs and demands, reflecting barriers to mobility or fulfilment, while at organisational and societal scales, it indicates unmet demand for potential increases to productivity and equity. On the supply side, this may imply to set up pathways for later access to qualification.

Limitations: While common in research due to easy access via administrative data and survey collection methods, qualifications serve as indirect proxies for true capabilities, overlooking non-formal skills or leading to mismatches, such as so-called overqualification (OECD, 2024[39]). They also provide a static picture of what people can do at their qualification point, thus ignoring the dynamic nature of the processes underlying the accumulation and depreciation of capabilities over the life course (Hodge et al., 2025[40]; Milana et al., 2025[41]). This restricts deeper analysis, as they do not capture actual capabilities in real time, nor contextual nuances, such as bounded agency constraints faced by disadvantaged groups (Rubenson and Desjardins, 2009[19]). These limitations highlight the need for complementary data sources to address gaps in life-wide learning domains and evolving life course dynamics.

Prospects: Integrating qualification-related data (e.g. national qualification registries) with complementary sources, such as indicators of skills mismatches or direct assessments of skills, yields more precise insights into learning needs and demands. Such integration may enable more tailored coordination of life-course transitions. Linking national qualification registers with broader datasets, including surveys and longitudinal studies, provides a fuller picture of individuals’ accumulated capabilities over time and facilitates policy intervention for closer alignment with stakeholder needs. It also strengthens research by tracing the longitudinal processes through which capabilities are built and eroded across diverse contexts. Ultimately, this approach enhances policy design by illuminating both the scope and distribution of capabilities, as well as the effects of interventions on developmental trajectories across the life course.

B.2 Direct skills assessment approach

Unlike the indirect nature of qualifications, direct assessments of capabilities such as literacy, numeracy, and problem-solving skills provide up-to-date, comparable, reliable, and valid measures of what individuals can do in specific domains and controlled scenarios, serving multiple purposes:

• Purpose for Coordination: Direct assessments like PISA or PIAAC deliver high-quality, reliable data to align labour-market needs, such as job and task distribution, by pinpointing capability gaps and fostering collaboration between learners and employers to bridge opportunities (Desjardins and Warnke, 2012[42]; van der Velden and Bijlsma, 2019[43]).

• Purpose for Research: These measures offer data to generate empirical evidence on skill acquisition and depreciation (Rammstedt et al., 2024[44]; Massing et al., 2025[45]), supporting studies on cognitive and emotional growth and the relationship of skills with organised learning in the educational system, as well as informal learning practice at home (Grotlüschen et al., 2016[46]; Hanushek et al., 2025[47]).

• Purpose for Policy Design: Assessments shape policy by identifying skill deficiencies and enabling targeted strategies, such as designing interventions to address gaps in literacy or numeracy, widely recognised as essential for meeting basic human needs (e.g. Grotlüschen et al. (2016[46]), Borgonovi and Suarez-Alvarez (2025[48]) and OECD (2024[39])).

Basic proficiency levels address core requirements, with shortfalls signalling essential gaps in daily functioning, such as literacy for communication. Advanced evaluations connect to professional demands, like problem-solving in dynamic environments, and extend to practical areas, including civic participation (e.g. ability to analyse conflicting news reports critically) or consumer decision-making (e.g. compare different mobile phone plans, identify hidden costs, and select the most cost-effective option for a family). In developed settings, subpar results often reveal unmet demands linked to well-being or adaptability, while at group levels, they inform organisational efficiency.

Limitations: While quantifiable and adaptable for surveys like PISA and PIAAC, these assessments offer partial views of a narrow range of capabilities, influenced by context or cultural factors, failing to fully capture wider life applications or the dynamic nature of capability needs over the life course (Elias, Hogarth and Pierre, 2002[49]; Desjardins and Warnke, 2012[42]; Schultheiss and Backes‐Gellner, 2023[50]).

Prospects: Integrating direct assessments with sources such as qualification registries, mismatch patterns, or motivational insights provides a more comprehensive understanding of needs and demands, supporting smoother life course transitions through targeted interventions. Combining data (e.g. PISA/PIAAC or national assessments with administrative datasets) may enhance coordination by aligning skills with formal education opportunities.

B.3 Skills use and mismatch approach

The skills use and mismatch approach examines alignment between individuals’ capabilities and their application in the labour-market, establishing up-to-date, reliable measures of utilisation, including surpluses, deficits, and shortages. It is valued by stakeholders, including employers, educators, and policymakers, for its economic and personal insights into capability alignment.

• Purpose for Co-ordination: Mismatch evaluations may enhance labour market efficiency by identifying skills imbalances to align skills with opportunities (Cedefop, 2010[51]; 2025[37]). They foster collaboration among stakeholders by highlighting underuse or shortages, strengthening governance in diverse institutional settings (Desjardins, 2014[52]).

• Purpose for Research: This approach provides data to generate empirical evidence on capability utilisation, impacting productivity, wages, and job satisfaction, building on human capital theories (Becker, 1964[30]; Hartog, 2000[53]; Hanushek and Woessmann, 2015[33]). It fuels debates on overqualification and underutilisation across economic and cultural contexts (Brown, Lauder and Cheung, 2020[54]; Hodge et al., 2025[40]; Milana et al., 2025[41]; Cedefop, 2025[37]).

• Purpose for Policy Design: Mismatch insights shape policies targeting imbalances, such as targeted training or labour-market reforms. From a critical perspective, mismatches may reinforce inequalities by limiting opportunities for certain groups (Bourdieu, 1986[55]) or lead to overly narrow LLL policies that focus solely on the supply side of the mismatch equation (Hodge et al., 2025[40]; Milana et al., 2025[41]), in contrast, a developmental view sees addressing them as essential for economic efficiency and social equity (Desjardins, 2017[23]; Cedefop, 2025[37]).

Basic mismatches, such as unused literacy skills, signal gaps in the effective use of core capabilities. Advanced mismatches, such as automation-driven shortages, highlight the economic need for upskilling. In advanced economies, persistent mismatches indicating under-skilling can signal unmet demands, affecting organisational innovation and societal equity across micro, meso, and macro levels.

Limitations: Data from surveys like the Adult Education Survey (AES), Labour Force Survey (LFS), or PIAAC rely on proxies, such as educational attainment and employment status (OECD, 2024[39]; 2019[56]), which oversimplify complex dynamics, including systemic barriers (e.g. socioeconomic disparities, discrimination) and informal capability use (e.g. skills acquired through workplace or community experiences). These surveys provide static snapshots, failing to capture the dynamic, longitudinal utilisation of capabilities over the life course, particularly for marginalised groups like migrants or low-skilled workers. This limitation hinders understanding of how structural obstacles and informal skills shape equitable skill development and labour-market outcomes. Moreover, they do not inform on whether younger people still in education have the skills they need in their personal life.

Prospects: Integrating data, such as motivational insights, provides a more comprehensive understanding of learning needs and demands, supporting coordinated life course transitions. This enhances research through longitudinal tracking of capability alignment dynamics and informs policy design by addressing inequities and fostering efficient capability utilisation.

B.4 Understanding dispositions to participation approach: motivations, attitudes, and barriers

This approach analyses various demand factors influencing participation in learning opportunities, establishing reliable measures of motivations, barriers, and attitudes. It is valued by stakeholders like researchers, educators, and policymakers for its behavioural insights into learning engagement and turning learning needs into a factual demand:

• Purpose for Co-ordination: Demand analyses enhance alignment between learning aspirations and learning provision by identifying LLL participation drivers, such as intrinsic motivation or external incentives, facilitating stronger connections between individuals and opportunities (Desjardins, 2017[23]; West and Michie, 2020[20]). They strengthen governance by aligning educational offerings with the needs of stakeholders across diverse contexts (Billett, 2002[57]).

• Purpose for Research: This approach provides empirical evidence on motivational and attitudinal factors of LLL engagement (Kalenda, Vaculíková and Kočvarová, 2022[58]; Kalenda, Boeren and Kočvarová, 2023[59]), building on adult motivation theories (Houle, 1961[60]; Boshier, 1971[61]; Cross, 1981[62]). Furthermore, it also contributes to debates on how barriers like cost or access shape participation across cultural and socioeconomic settings (Boeren, 2016[6]; Roosmaa and Saar, 2017[63]; Cabus, Ilieva-Trichkova and Štefánik, 2020[64]).

• Purpose for Policy Design: Uptake insights shape policies targeting participation barriers, such as financial support or flexible learning formats. From a critical perspective, barriers may reflect structural inequalities (Rubenson, 2018[65]), whereas a developmental view considers addressing them essential for inclusive growth and social mobility (Neidhöfer et al., 2021[66]).

Fundamental motivations, such as personal growth, address basic gaps in capability development, while barriers, including a lack of access or of relevant supply, highlight unfulfilled needs. Advanced drivers, like career aspirations, connect to economic demands for upskilling. These factors also relate to wider needs, including civic participation (e.g. attitudes toward community learning) or health (e.g. overcoming barriers to health literacy) or personal well-being (e.g. learning something one is passionate about).

Limitations: Relying on self-reported survey data introduces risks of bias, such as over-optimism or social desirability, and tends to oversimplify the complexity of motivational dynamics, including differences between motivation related to emotions and habits and more deliberate, reflective motivation (Michie, van Stralen and West, 2011[67]; West and Michie, 2020[20]). This approach also provides only a static snapshot, overlooking how motivations and barriers shift across the life course. In addition, large-scale international surveys often employ limited items to capture motivation and barriers, offering only a partial view of attitudinal factors linked to LLL (Kalenda, Vaculíková and Kočvarová, 2022[58]; Kalenda, Boeren and Kočvarová, 2023[59]). Crucially, they also fail to assess barriers and motivations among those who do not participate in organised learning activities (Kalenda and Kočvarová, 2022[68]; Karger, Kalenda and Kočvarová, 2022[69]; Kalenda, Boeren and Kočvarová, 2023[59]; Broek et al., 2025[70]). In the case of younger learners still enrolled in formal education, they also fail to assess barriers and motivations for non-formal and informal learning.

Prospects: Enhancing measures in this field to enable international comparison, and linking them with complementary data such as skills mismatch analyses or workplace insights, would provide a sharper understanding of learning needs and demands, thereby supporting more coordinated life-course transitions. Moreover, longitudinal tracking is essential for uncovering the dynamics of motivation and informing policy design, particularly by identifying barriers and fostering more inclusive learning pathways.

B.5 Workplace and organisational learning and needs approach

The workplace and organisational learning approach examines how professional environments shape the needs and demands for skill development and application within the job environment, establishing reliable measures of workplace-based education and learning, as well as the factors that influence them. It is valued by stakeholders, including employers, employees, and policymakers, for its practical insights into workplace capability development.

• Purpose for Coordination: Workplace learning analyses enhance labour-market efficiency by aligning organisational training with workforce needs, fostering collaboration between employers and employees (Eraut *, 2004[71]). They support governance by integrating learning into workplace strategies to address capability gaps across diverse sectors (Cedefop, 2016[72]).

• Purpose for Research: This approach provides empirical insights into how workplace cultures and job-related characteristics influence skills acquisition and application, building on organisational learning theories (Kluge and Schilling, 2003[73]). It contributes to debates on the role of informal learning in driving productivity and innovation (Messmann, Segers and Dochy, 2018[74]; Lancaster, 2020[75]).

• Purpose for Policy Design: Workplace learning informs policies promoting LLL, such as incentives for employer-led training. From a critical perspective, limited learning cultures may reinforce inequities by favouring certain groups of workers (Erola, Mills and Solga, 2023[76]; Hornberg, Heisig and Solga, 2024[77]; Kalenda, Vaculíková and Kočvarová, 2024[78]; Mertens et al., 2025[79]), while a developmental view sees them as vital for economic and social progress (Vignoles, Galindo‐Rueda and Feinstein, 2004[80]; Stiglitz and Greenwald, 2014[81]).

Core workplace learning cultures address fundamental gaps, such as the development of basic capabilities, while constrained environments often signal unfulfilled potential. More advanced forms of learning, including innovation-driven training, respond directly to economic pressures for competitiveness. At the same time, they extend beyond market needs, encompassing wider priorities such as civic engagement (for example, team-based civic skills) and workplace well-being initiatives. In mature economies, however, weak or limited learning cultures reflect unmet demands that undermine productivity, compromise employee well-being, and reinforce inequalities across society.

Limitations: Most studies of workplace learning remain narrow in scope, typically concentrating on employees within a single firm or a small set of companies. Their capacity to generate findings generalisable to wider populations is therefore limited. Research also tends to over-represent workers in service and high-technology industries, while giving less attention to lower-qualified employees (Kalenda, Vaculíková and Kočvarová, 2022[58]; Broek et al., 2025[70]). Large-scale representative surveys, such as the Continuing Vocational Training Survey (CVTS), provide valuable insights; however, they largely exclude informal learning and precarious work, narrowing their relevance. Furthermore, CVTS focuses primarily on private enterprises, neglecting public or non-governmental sectors, unlike the European Working Conditions Survey (EWCS), which offers broader sectoral coverage, including self-employed workers. Both surveys, however, offer static snapshots rather than tracing dynamic capability development over the life course, an omission significant for non-traditional workers and marginalised groups (Grimshaw, 2022[82]).

Prospects: On the one hand, scaling up selected methodological approaches from workplace learning research to the level of representative and comparative large-scale measurement could be highly beneficial. Such an effort would enable more robust evidence on workplace learning, including the needs of different actors (employees and organisations) and the dynamics of learning cultures. On the other hand, updating the CVTS to include a module on non-private entities would broaden our understanding of learning needs and practices beyond enterprises. Finally, integrating these forms of data with complementary sources, such as uptake patterns or skills mismatch analyses, would provide a more comprehensive picture of learning needs and demands in this field.

B.6 Desirable outcomes approach: individual and family level

The individual and family outcomes approach assesses the impact of learning on personal and relational well-being, developing robust measures of achievement in areas such as resilience and fulfilment. Stakeholders - including families, communities, and policymakers - value this perspective for its human-centred insights into the outcomes of learning.

• Purpose for Coordination: Outcome analyses align learning with personal and family goals, facilitating stakeholder collaboration to support life course transitions (Schuller et al., 2004[24]). They strengthen governance by integrating well-being metrics into educational and social strategies.

• Purpose for Research: This approach provides empirical evidence on learning’s contributions to personal growth, mental health, and family dynamics, building on capability theories (Sen, 1999[1]; Nussbaum, 2011[2]; Boyadjieva and Ilieva-Trichkova, 2021[3]). It fuels debates on non-economic benefits across diverse contexts.

• Purpose for Policy Design: Outcome insights inform policies promoting holistic development, such as family-focused education programmes. From a critical perspective, unequal access to learning may exacerbate well-being disparities, while a developmental view sees it as essential for social cohesion and resilience (OECD, 2007[83]).

Fundamental outcomes, like improved self-efficacy, address basic gaps in personal development, while low fulfilment signals unfulfilled aspirations. Advanced outcomes, such as intergenerational benefits, are closely tied to economic and social stability. They also relate to broader needs, including civic participation (e.g. family roles in community activities) or health and consumption (e.g. household resilience). In advanced settings, suboptimal outcomes often indicate unmet demands, which can impact personal growth, family dynamics, and societal well-being.

Limitations: Data from longitudinal surveys, such as the English Longitudinal Study of Ageing (ELSA), the British Cohort Study (BCS70), or Germany’s National Educational Panel Study (NEPS) (NEPS Network, 2021), and the PIAAC longitudinal study (PIAAC-L) offer valuable insights. However, with the exception of PIAAC-L, their coverage of LLL remains limited, focusing primarily on more formal and structured types of learning provision with minimal account of demand factors. Moreover, these surveys vary considerably among countries.

Prospects: Integrating these data with cross-sectional surveys that focus directly on LLL, such as those examining skills use or workplace learning, can sharpen the understanding of learning needs and demands, while supporting smoother life-course transitions. Such integration could strengthen research by enabling the longitudinal tracking of well-being dynamics and enhancing policy design by addressing equity gaps and fostering more inclusive personal and family development.

B.7 Desirable outcomes approach: public and collective level

The collective outcomes approach assesses the societal impact of learning, establishing reliable measures of achievement in areas such as cohesion, sustainability, and equity. It is valued by stakeholders, including governments, communities, and policymakers, for its macro-level insights into societal benefits.

• Purpose for Coordination: Collective outcome analyses promote societal alignment by linking learning to shared goals, facilitating stakeholder collaboration to reduce inequalities and enhance sustainability (Schuller et al., 2004[24]). They strengthen governance by integrating collective outcomes into national and regional strategies.

• Purpose for Research: This approach provides empirical evidence on learning’s contributions to social cohesion, economic growth, and environmental sustainability, building on social capital (Putnam, 2000[4]) and capability theories (Sen, 1999[1]). It contributes to debates on the wider societal impacts of education (Elias, Hogarth and Pierre, 2002[49]; Grotlüschen et al., 2023[84]).

• Purpose for Policy Design: Collective outcome insights inform policies aimed at reducing inequalities and promoting sustainable development, such as inclusive education initiatives. From a critical perspective, uneven outcomes may perpetuate social divides (Bourdieu and Passeron, 1977[35]), while a developmental view sees them as vital for societal progress and equity (Nussbaum, 2011[2]).

Basic collective outcomes, like improved social cohesion, address foundational gaps in community vitality, while low outcomes signal unfulfilled societal needs. Advanced outcomes, such as reduced inequality, are closely tied to economic and social demands for inclusive growth. They also relate to broader needs, including civic unity (e.g. community engagement).

Limitations: Data from reports such as the Global Education Monitoring Report (GEMR), originating in the Education for All (EFA) Global Monitoring Reports in 2002 and formalised under the Education 2030 Agenda in 2016, support large-scale comparisons by aggregating standardised metrics across diverse national contexts. These include literacy rates, enrolment figures, and equity indicators, which together enable global benchmarking and policy analysis. Yet such reports risk overlooking important local dimensions, including cultural influences, regional disparities, and community-specific learning practices. A more balanced approach requires integrating qualitative insights and locally grounded data collection to capture the full complexity of LLL ecosystems.

While GEMR offers valuable periodic snapshots, it nonetheless presents static assessments that often fail to convey the dynamic, evolving processes of societal change across the life course -for instance, the cumulative impact of early education on later-life capabilities, or the adaptive shifts triggered by technological and demographic transitions. This limitation underscores the need for longitudinal and real-time data strategies, as highlighted by Schuller et al. (2004[24]) and Elias, Hogarth and Pierre (2002[49]) in their critique of how the wider societal benefits of education are captured.

Prospects: Pairing with complementary data, such as individual outcomes, offers a more precise understanding of learning needs and demands at the collective level, thereby supporting more effective macro-social coordination across the life course. This approach strengthens research by enabling longitudinal tracking of societal impact dynamics and informs policy design by fostering equitable and sustainable learning ecosystems.