This chapter examines how different stakeholders understand, value, and assess the outcomes of lifelong learning (LLL), and how these views influence learning priorities, opportunities, and policies. It explores how learning can lead to both immediate results, such as the development of knowledge, skills, and qualifications, and longer-term benefits, including employment, well-being, social inclusion, and civic participation. The chapter highlights the role of research and evidence in understanding these outcomes and in supporting decisions about what learning opportunities should be offered and to whom. It also reviews different ways of measuring learning needs and outcomes, showing how individuals, organisations, and governments may prioritise different goals. Overall, the chapter argues for stronger evidence and better data to support more balanced, effective, and inclusive lifelong learning systems.
A Conceptual Foundation for the Development of a Lifelong Learning Measurement Framework
Abstract
Stakeholder understandings and approaches to valuing learning outcomes play a central role in framing and setting learning objectives as well as identifying interventions to enhance them. The importance and measurability of learning outcomes, along with how stakeholders at different levels articulate, communicate, and negotiate the relative value and thus prioritisation of outcomes, drive coordination and policy design. These factors ultimately shape the demand and supply of LLL. This section emphasises the role of learning outcomes in driving what is viewed as needed (demand), to be provided (supply), or be facilitated (coordination). It does so by considering how stakeholders' differing views on, and approaches to, understanding and valuing the purpose of fostering learning occurrences influences decision-making at all levels. The relative emphasis and negotiation of such perspectives may explain variations in outcomes across levels, contexts, and countries. That is, the relative weight and significance assigned to different needs at different levels are thus shaped by understandings of actual and desirable outcomes at various levels, highlighting the need for diverse perspectives and approaches as well as integrated evidence to inform balanced, effective coordination.
Building on Section 2.2, where the focus was more on general optics of how this is governed and aligned (or not) in terms of coordination, the emphasis in this section is on the role of research and how understandings and approaches to valuing learning outcomes may interact with the framing of perspectives, approaches and understandings and thus objectives to coordination, demand and supply. A central role of research and evidence is to uncover patterns, relationships, and nuanced insights into the effects and impacts of diverse learning opportunities, their uptake, and outcomes at various ages and stages of the life course. To address this, Section 5.1 presents a simple, general framework that distinguishes proximal outcomes (e.g. immediate skill acquisition or capability enhancement) from distal outcomes (e.g. long-term economic, social, or civic benefits). This distinction is discussed within a multi-level coordination framework, linking it to policy design, particularly at the macro level, where systemic alignment can occur depending on the prevailing governance model. Timing is critical in policy design, particularly at the macro level, where instruments such as funding priorities, qualification frameworks, or Active Labour Market Policy (ALMPs) set the structural conditions for coordination; yet these interventions also profoundly shape coordination possibilities at meso and micro levels by enabling or constraining access, recognition, and resource flows during key life transitions.
Current data, information, and associated statistics and indicators have limitations in fully meeting these needs, often providing fragmented or static views that fail to capture dynamic interactions or non-economic benefits. Distinguishing data purposes and needs in more detail is addressed in greater detail in Section 6 emphasising a more careful distinction among the needs of research, policy design, and coordination, and in Section 7 which focuses on data and indicator development strategies to overcome some of the shortcomings. Section 5.2 introduces a range of methodological approaches to understanding and assessing learning needs, demands, and outcomes, summarising their key features and directing to Annex B for detailed analysis. These approaches are driven by different interpretations of learning objectives, or alternatively, different valuations of the relative significance of what is viewed as needed (demand), to be provided (supply), or be facilitated (coordination) – for whom (e.g. individuals, organisations, or societies) and for what reason (e.g. economic productivity, social equity, or personal fulfilment). Beyond the research understanding and approaches, the net result is often a negotiation dependent on the prevailing governance model, determining what needs to be prioritised and how resources are allocated, which connects back to Section 2.2.
Research plays a critical role in fostering an understanding of the processes, outcomes, and dynamics of LLL ecosystems by examining the interplay of demand, supply, and learning occurrences, alongside their proximal (e.g. skill acquisition, certifications) and distal (e.g. social mobility, civic cohesion) outcomes. As depicted in Figure 5.1, learning occurrences link demand and supply to outcomes, with proximal outcomes feeding into distal outcomes, illustrating the consequences of learning occurrences. Thus naturally, the actual and desired outcomes dynamically act as drivers of demand, supply and coordination. Stakeholder perspectives and priorities, varying by governance model, shape the identification and fulfilment of needs, provision, and coordination. These are driven by actual and desired learning outcomes, their perceived beneficiaries, and their influence on priorities and policy design.
Multiple approaches capture learning occurrences and their outcomes, each using metrics shaped by diverse perspectives, purposes, expertise, methods, and disciplines (summarised in the following section and detailed in Annex B). For example, the qualification approach, using certifications as proxies for capabilities, contrasts with direct skills assessments like the Programme for International Student Assessment (PISA) or the Programme for the International Assessment of Adult Competencies (PIAAC), which measure competencies such as literacy or problem-solving. Each can lead to the inference of learning needs differently: qualifications signal broad capability levels, while direct assessments provide precise, but narrow insights. These methods offer complementary lenses to evaluate learning needs and outcomes, reflecting stakeholder priorities (e.g. employers valuing productivity, communities emphasising cohesion) and informing targeted interventions.
Understanding better the underlying processes and estimating relationships among learning outcomes, needs, and provision requires research to determine the relative weights of linkages and under which conditions. High-quality inferences or transpositions among the variables of interest (e.g. from qualifications to direct skills) depend on empirically robust research mapping these relationships. Naturally, quality data, enhanced through strategies like longitudinal tracking, survey-administrative linkages, AI-driven analytics, and participatory logging, can add value for estimating such a comprehensive transposition matrix; one which reflects the nexus of complex relationships among needs, provisions and the desired outcomes across multiple levels. In other words, often desired outcomes from a particular perspective imply the projection of certain needs and demands, or alternatively provision, and vice versa depending on the perspectives, purposes, expertise, methods, and disciplines.
At the micro level, research examines how learning processes (e.g. training programmes, informal learning) translate into immediate capabilities (proximal outcomes) and long-term benefits like employability or well-being (distal outcomes). Key priorities include:
Integrating LLL-focused modules into longitudinal datasets (e.g. PIAAC-L, ELSA) to track learning trajectories and processes over time.
Linking cross-sectional surveys (e.g. PISA, AES) with administrative data (e.g. labour market records) to analyse outcome dynamics and learning processes across contexts.
Developing survey instruments and improving administrative data conceptualisation to capture currently unavailable but relevant LLL data for research, coordination, and policy design.
These efforts will strengthen the evidence base for understanding causal relationships, supporting diverse assessment approaches to infer needs and outcomes. This applies to the meso level too, when organisational and community perspectives, along with available data, are integrated and aggregated effectively.
At the macro level, studying coordination patterns across institutional clusters (formal education, labour market, welfare, civic) informs governance and policy design. Network analysis of stakeholder interactions (e.g. tripartite agreements), for example, has the potential to reveal how policies align supply with demand, addressing gaps like skill mismatches or inequitable access, consistent with multi-level coordination dynamics.
High-quality research, supported by enhanced data through strategies like those outlined in Section 7 is essential for mapping these relationships and processes at micro, meso and macro levels, ensuring coordination aligns with stakeholder priorities and systemic goals.
Extending the discussion, there is a range of methodological approaches for studying, interpreting, and evaluating learning needs, demands, and outcomes across diverse stakeholder groups, building on the multi-level ecology of needs (Section 3) and governance dynamics (Sections 2.2 and 3). These approaches – drawn from fields such as adult education psychology, labour economics, behavioural economics, and sociolinguistics – help bridge data gaps by integrating individual aspirations, organisational objectives, and societal goals. They are essential for informing coordination (e.g. aligning supply with demand), research (e.g. uncovering causal relationships), and policy design (e.g. targeting interventions for equity and efficiency), while highlighting the negotiated nature of priorities under different governance models.
The approaches include:
Qualification approach: Uses certifications as proxies for capabilities, signalling skills for labour market matching.
Direct skills assessment approach: Provides reliable comparative measures of specific competencies, like literacy or problem-solving, through tools such as PISA or PIAAC.
Skills use and mismatch approach: Examines alignment between capabilities and job demands to identify surpluses, deficits, or shortages.
Understanding dispositions to participation approach: Analyses motivations, attitudes, and barriers to turn needs into actual demand.
Workplace and organisational learning approach: Focuses on professional environments shaping skill development and application.
Desirable outcomes approach (individual and family level): Assesses personal and relational well-being impacts, such as resilience or fulfilment.
Desirable outcomes approach (public and collective level): Evaluates societal benefits, including cohesion, sustainability, and equity.
This non-exhaustive list examines key approaches in detail for illustrative purposes, covering their roles in coordination, research, and policy design; their ability to identify learning gaps across micro, meso, and macro levels; their limitations, such as static snapshots or biases; and opportunities for data integration to provide dynamic, equitable insights (see Annex B). The annex can act as a guide for stakeholders and researchers in selecting and combining methods to foster an integrated data ecosystem, supporting comprehensive LLL alignment with economic, social, and personal goals throughout the life course.