OCDE - The many faces of adult learners Who learns, why, and who is left behind

Policy Paper The many faces of adult learners Who learns, why, and who is left behind2 

THE MANY FACES OF ADULT LEARNERS © OECD 2026

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© OECD 2026

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This paper introduces a new way to understand how and why adults take part in learning. It groups adults into different learner profiles to help design policies that are more focused and effective. Using survey data, it identifies typical learner types based on what motivates people to learn – or what holds them back. The profiles reveal the wide range of reasons why adults choose to engage in, or avoid, learning. These include personal goals, practical barriers and views on the value of learning. By showing how different groups think and behave, this approach can help policymakers create more tailored and relevant learning opportunities. The paper presents a learner segmentation model, first developed for Flanders (Belgium), and then applies it to four other countries: Bulgaria, Finland, Ireland and Portugal.

Contact Bart STAATS ( bart.staats@oecd.org) Abstract4 

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Table of contents Disclaimers 2 Abstract 3 Contact 3 Executive summary 6 A segmentation approach to target and tailor adult learning policies (Chapter 1) 6 Adult learner profiles in Bulgaria, Finland, Ireland and Portugal (Chapter 2) 7 Considerations for applying learner profiles in policy and practice (Chapter 3) 7 1 A segmentation approach to target and tailor adult learning policies 8 1.1. The need to better target and tailor adult learning policies 8 1.2. A segmentation approach to adult learning 9 1.3. Approaches for using a segmentation approach to improve adult learning policies 11 2 Adult learner profiles in Bulgaria, Finland, Ireland and Portugal 13 2.1. Overview 13

1.2. A segmentation approach to adult learning 9 1.3. Approaches for using a segmentation approach to improve adult learning policies 11 2 Adult learner profiles in Bulgaria, Finland, Ireland and Portugal 13 2.1. Overview 13 2.2. Cross-country patterns in adult learner profiles 16 2.3. Profiles for Bulgaria 18 2.4. Profiles for Finland 20 2.5. Profiles for Ireland 22 2.6. Profiles for Portugal 25 3 Considerations for applying learner profiles in policy and practice 28 3.1. Operationalising more targeted and tailored policies based on learner profiles 28 3.2. Risks and limitations of learner profiles 30 3.3. Potential areas for further research 31 4 Conclusions 32 Annex A. Methodological description 33 References 38

FIGURES Figure 1. The nine adult learner profiles and their characteristics 10 Figure 2. Comparison of adult learning in Bulgaria, Finland, Ireland and Portugal 14 Figure 3. Profiles Bulgaria, Finland, Ireland and Portugal, as a share of adult population 17 TABLES Table 1. Overview of adult learner profiles in Bulgaria 18 5

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Table 2. Overview of adult learner profiles in Finland 20 Table 3. Overview of adult learner profiles in Ireland 23 Table 4. Overview of adult learner profiles in Portugal 256 

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Adults engage in learning for a wide range of reasons, shaped by their individual circumstances, needs, and aspirations (OECD, 2019[1]; OECD, 2021[2]). While many adults participate in learning for professional purposes – such as meeting employer requirements, pursuing career advancement, or preparing for occupational change – others are driven by personal interest or the desire for self -development. These motivations are often closely linked to individual characteristics, including age, educational attainment,

purposes – such as meeting employer requirements, pursuing career advancement, or preparing for occupational change – others are driven by personal interest or the desire for self -development. These motivations are often closely linked to individual characteristics, including age, educational attainment, employment status and previous learning experiences. By contrast, many adults do not engage in learning, often due to a combination of structural and attitudinal barriers. Common structural challenges include time constraints related to work or family responsibilities, financial limitations and restricted access to appropriate learning opportunities. Attitudinal barriers may also play a role, with reluctance to engage in learning frequently rooted in low self -confidence, limited perceived relevance, or negative prior experiences with education and training. A main challenge is that adults who stand to benefit most from learning opportunities are often among the least likely to participate and facing most significant barriers. Existing policies and support measures frequently struggle to reach and engage these groups. To better understand this complex set of behaviours and influences, this paper introduces a methodology for identifying distinct adult learner profiles based on the interaction of factors that influence learning participation. Using Latent Class Analysis (LCA), a quantitative clustering technique, the analysis groups individuals according to shared patterns of motivation and barriers. This approach provides a structured and evidence-based representation of the diversity within the adult learning population , offering valuable insights for policymakers and enabling more targeted and effective design, delivery and allocation of resources. This policy paper outlines the rationale for adopting a segmentation approach (Chapter 1), presents learner profiles in four countries – Bulgaria, Finland, Ireland and Portugal – along with their policy implications (Chapter 2), and describes considerations for the implementation of learner profiles (Chapter 3). Together, the results provide a solid evidence base to support the development of more inclusive, tailored and effective adult learning systems. Key findings are listed below. A segmentation approach to target and tailor adult learning policies (Chapter 1) • Adults who stand to benefit most from learning opportunities are often among the least likely to participate. Existing policies and support measures frequently struggle to reach and engage these groups. • Segmentation approaches can strengthen adult learning policy by enabling more targeted and

A segmentation approach to target and tailor adult learning policies (Chapter 1) • Adults who stand to benefit most from learning opportunities are often among the least likely to participate. Existing policies and support measures frequently struggle to reach and engage these groups. • Segmentation approaches can strengthen adult learning policy by enabling more targeted and effective design, delivery and allocation of resources. Learner profiles generated through segmentation capture the interaction between motivations and barriers, off ering a more nuanced and actionable basis for policymaking than conventional demographic classifications. Although the resulting profiles represent stylised or “ideal” types rather than exact individual experiences, they serve as a practical tool for interpreting patterns in adult learning behaviour. Executive summary 7

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• In practice, learner profiles can support the development of more responsive and effective adult learning strategies. The profiles can be used for: i) the identification of priority target groups; ii) the design of more responsive guidance and information services; iii) the tailoring financial and nonfinancial incentives; and iv) the strengthening the monitoring and evaluation framework for adult learning policies.

Adult learner profiles in Bulgaria, Finland, Ireland and Portugal (Chapter 2) • This paper develops learner profiles for Bulgaria, Finland, Ireland and Portugal using the quantitative methodology applied in the OECD study OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders (OECD, 2022[3]). These four countries were selected based on their participation in the 2022 EU Adult Education Survey (AES), as well as their geographic and socio -economic diversity within the EU, variation in adult learning behaviours and differing levels of performance in adult learning systems. • In all four countries, a substantial share of non-participants falls into a Disengaged Adults profile – characterised by low motivation (i.e. indicating not wanting to participate in education and training) rather than external barriers – with the largest share observed in Bulgaria. This underscores the importance of addressing psychological and attitudinal factors in efforts to re -engage adult learners.

characterised by low motivation (i.e. indicating not wanting to participate in education and training) rather than external barriers – with the largest share observed in Bulgaria. This underscores the importance of addressing psychological and attitudinal factors in efforts to re -engage adult learners. • Although each country includes profiles of adults facing participation constraints, the nature of these barriers differs. In Ireland and Bulgaria, cost and time emerge as key obstacles across several profiles; in Portugal, health and age are more influenti al; and in Finland, personal and structural barriers are more clearly delineated. These findings highlight the importance of contextspecific policy responses. • Among participants, motivational profiles are diverse and unevenly distributed. All countries exhibit a combination of Obligated but Unmotivated learners and those driven by intrinsic or multifaceted motivations. However, the relative prevalence of these groups differs, with intrinsically motivated learners more common in Ireland and Portugal and less so in Bulgaria – suggesting differing enabling conditions for sustained and meaningful engagement. Considerations for applying learner profiles in policy and practice (Chapter 3) • Translating learner profiles into effective policy requires embedding segmentation insights into both policy design and implementation. This involves developing diagnostic tools to identify learner types, alongside sustained investment in practitioner capa city and data infrastructure to support ongoing use. • While learner profiles offer a valuable basis for more targeted adult learning policies, their application must be approached with care. There is a risk of oversimplifying complex individual circumstances, reinforcing stereotypes, or relying on outdated assumptions. Regular review and application of profiles with nuance, flexibility and critical reflection are essential. • Further research could enhance the practical utility of learner profiles by extending the approach to additional datasets (e.g. the Programme for the International Assessment of Adult Competencies, PIAAC), incorporating a wider range of socio -demographic and labour market variables, and exploring how profiles can inform the design of practical tools and interventions for more personalised and effective policy responses.8 

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Competencies, PIAAC), incorporating a wider range of socio -demographic and labour market variables, and exploring how profiles can inform the design of practical tools and interventions for more personalised and effective policy responses.8 

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1.1. The need to better target and tailor adult learning policies Adult learning plays a central role in enabling individuals and countries to adapt to global megatrends and remain competitive by facilitating upskilling and reskilling throughout life. The traditional model of frontloading skills development in initial education is becoming increasingly untenable amid rapid technological progress – including major advances in generative artificial intelligence (AI) – the green transition and ongoing geopolitical tension . These forces are reshaping patterns of production and consumption and fundamentally altering demand for skills. In this context, a dult learning equips individuals to continuously acquire new knowledge and skills, thereby enabling countries to better respond to evolving labour market needs and address persistent skills imbalances (OECD, 2019[4]). Governments in OECD member countries are actively taking steps to promote adult learning, recognising its wide-ranging social and economic benefits. Within the European Union, this aligns closely with targets under the European Skills Agenda and the Europe an Pillar of Social Rights, including raising adult participation in education and training to 60% by 2030 and strengthening access to upskilling and reskilling opportunities. These commitments reflect a shared ambition to ensure that adults can adapt to l abourmarket changes, progress in their careers and participate fully in society. Despite ongoing efforts by governments, those who stand to benefit most from adult learning are often the least likely to participate . For instance, workers with low levels of education are overrepresented in occupations that face a high risk of automation, making them particularly vulnerable to the disruptive effects of digital transformation (Georgieff and Milanez, 2021 [5]). Upskilling and reskilling are therefore essential, not only to help these adults adapt to changing skills requirements in their current jobs, but also to facilitate

occupations that face a high risk of automation, making them particularly vulnerable to the disruptive effects of digital transformation (Georgieff and Milanez, 2021 [5]). Upskilling and reskilling are therefore essential, not only to help these adults adapt to changing skills requirements in their current jobs, but also to facilitate their transition into new forms of work. However, participation rates remain highly unequal. In 2024, only 5.5% of adults with less than upper-secondary education across the European Union (EU) participated in education and training (in the previous four weeks), compared to 21.6% of adults with a tertiary degree (Eurostat, 2025[6]). This participation gap contributes to a "Matthew effect”, whereby individuals with higher levels of education continue to accumulate advantages, while those with lower levels fall further behind. First introduced by Merton in 1968, the concept underscores how unequal access to learning can reinforce and widen disparities in educational and labour market outcomes (Merton, 1968[7]). Another critical challenge is that many adults are not willing to engage in educational activities, particularly those from the more vulnerable segments of society . These individuals often do not express a desire to participate in learning activities, nor do they perceive a need for further education. Even when motivated to learn, they frequently face obstacles. While time-related barriers, such as busy schedules, and family responsibilities, are common, adults with low er levels of education are disproportionately affected by additional constraints, including health issues, age-related limitations, and financial costs (Eurostat, 2024[8]). 1 A segmentation approach to target and tailor adult learning policies 9

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To address persistent challenges in adult learning participation, countries are placing increasing emphasis on better targeting and tailoring adult learning policies to those most in need. This focus has become particularly salient in the context of growing budgetary pressures and the imperative to allocate public funds efficiently and equitably. Evidence suggests that policies directed at specific groups generally yield

To address persistent challenges in adult learning participation, countries are placing increasing emphasis on better targeting and tailoring adult learning policies to those most in need. This focus has become particularly salient in the context of growing budgetary pressures and the imperative to allocate public funds efficiently and equitably. Evidence suggests that policies directed at specific groups generally yield greater impact than universal measures (OECD, 2020 [9]). While universal approaches may offer advantages in terms of administrative simplicity and broad accessibility, they often fail to reach underrepresented groups and risk generating significant deadweight losses – by subsidising learning activities that would have occurred without public intervention. In practice, less targeted adult learning policies tend to disproportionately benefit individuals who are already highly skilled. To mitigate these inefficiencies, many countries have adopted more targeted approaches that respond specifically to the needs of underrepresented groups – such as adults with low skill levels, the unemployed and migrants. These efforts typically involve adapting learning provision (e.g. offering basic skills training) and by targeting financial and non-financial incentives through tailored eligibility criteria. Despite growing efforts to improve targeting, effectively engaging adults most in need of learning remains a persistent challenge. Skills systems and existing support structures often struggle to reach their intended beneficiaries (OECD, 2019[1]). For example, 13.4% of adults with low levels of education who are interested in learning report that a lack of support from public services is a barrier to their education, compared to only 6.1% of adults with tertiary degrees (Eurostat, 2024[8]). Similarly, the 2022 OECD Survey of Adult Skills (PIAAC) shows that financial barriers are more commonly cited by adults with lower educational attainment. In Canada, for example, 16.6% of adults with low levels of educational attainment identify cost as a barrier, compared to 12.6% among highly educated adults. These findings indicate that current public support services are not consistently reaching those who would benefit most. Moreover, while policies increasingly aim to target underrepresented groups, they often fail to account for the diversity that exists within these populations. Adults with low levels of education, for example, constitute

support services are not consistently reaching those who would benefit most. Moreover, while policies increasingly aim to target underrepresented groups, they often fail to account for the diversity that exists within these populations. Adults with low levels of education, for example, constitute a diverse group with varying motivations, constraints and learning preferences. Factors influencing their participation range from attitudinal barriers – such as low confidence or limited perceived relevance – to practical obstacles, including time constraints, high costs, and limited acces s to appropriate learning opportunities. Relying solely on broad socio-demographic categories risks obscuring important differences behavioural differences. Identifying learner groups based on motivations and barriers can support a more nuanced understanding of adult learning participation and provide a stronger foundation for designing adult learning policies that are more inclusive, responsive and effective. 1.2. A segmentation approach to adult learning Market segmentation is a well-established strategy in the private sector for tailoring products and services to distinct consumer groups. First introduced by Wendell R. Smith in 1956, the concept refers to the division of a heterogeneous market into smalle r, more homogeneous segments based on shared preferences (Wendell R. Smith, 1956[10]). Traditionally, segmentation enables firms to maximise value by concentrating efforts on the segments where they can offer the greatest benefit or by customising products to meet varying consumer needs. For example, marketing strategies typically involve identifying the characteristics and preferences of specific segments, designing products accordingly, and targeting communications to those groups (Wind, 2007[11]). Segmentation may be based on geographic, demographic, psychographic, or behavioural factors (Goyat, 2011[12]). While commonly applied in commercial contexts, segmentation also holds considerable relevance for public policy, including in the field of adult learning. Applying segmentation to the adult learning population can support more effective policy design, impl ementation and resource allocation. By identifying distinct learner groups – particularly those underserved by existing incentives – policymakers can develop more targeted and responsive interventions that better address the diverse needs of adult learners .10 

can support more effective policy design, impl ementation and resource allocation. By identifying distinct learner groups – particularly those underserved by existing incentives – policymakers can develop more targeted and responsive interventions that better address the diverse needs of adult learners .10 

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Some OECD countries and regions have begun to apply segmentation strategies to improve the development of adult learning policies. Flanders (Belgium) is one of the regions at the forefront of this effort (see Box 1.1). The OECD supported the Flemish Government in developing a segmentation methodology, as outlined in the report “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders ” (OECD, 2022[3]). That study applied a quantitative approach to identify learner profiles and the model developed through this work forms the basis of the analysis presented in this paper. Box 1.1. Segmentation strategies for adult learning in Flanders (Belgium) The 2022 report “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders” (OECD, 2022[3]) identifies nine distinct adult learner profiles (see Figure 1) that can be used to help Flanders better target and tailor its adult learning policies. The nine adult learner profiles were identified using Latent Class Analysis, which enabled the identification of subgroups of adults who possess a shared set of motivations to learn and obstacles to participation. Flemish stakeholders have played a key role in developing this segmentation by sharing their expertise and perspectives in multiple workshops and bilateral meetings on the methods, findings and potential uses of the profiles for policymaking purposes. Figure 1. The nine adult learner profiles and their characteristics

In 2021, Flanders conducted an additional study to identify different types of learners. The study “Customer journey of non -participating and participating adults in lifelong learning ” (Van Cauwenberghe et al., 2021[13]) builds on customer journey research and aims to provide insights into learning needs, motivations Profile 9: Participating for professional and personal

journey of non -participating and participating adults in lifelong learning ” (Van Cauwenberghe et al., 2021[13]) builds on customer journey research and aims to provide insights into learning needs, motivations Profile 9: Participating for professional and personal development (7% of the population) Profile 7: Participating to strengthen career prospects (5% of the population) Profile 6: Participating in response to work pressures (17% of the population) Profile 2: Unmotivated due to age & health obstacles (18% of the population) Profile 1: Disengaged from learning (19% of the population) Profile 3: Motivated but facing time-relatedobstacles (6% of the population) Profile 4: Motivated but facing multiple obstacles (9% of the population) Profile 8: Participating for personal development (3% of the population) Profile 5: Reluctant but requiredto participate (16% of the population) •Motivational profile: Do not want to learn, and perceiving no need to learn •Primary characteristics: Low education levels; many nonnative speakers; many unemployed and inactive •Motivational profile: Not motivated, seeing no need to learn due to age/health obstacles •Primary characteristics: Comparatively old; comparatively low education levels; many inactive (early retirement, disabled) •Motivational profile: Motivated, but no time due to schedules / family responsibilities •Primary characteristics: Often young adults; often with children; large share of women; many non-native speakers; high education levels; often employed in full-time jobs •Motivational profile:Motivated, but facing range of obstacles, including high cost, no suitable learning offers, health and age •Primary characteristics: low income; comparatively low education levels; often employed in medium-skilled jobs •Motivational profile: Learning because they are required to do so by employer or law •Primary characteristics: Very young; many unemployed; low learning intensity; many learn informally •Motivational profile: Learning to adapt to changing

education levels; often employed in medium-skilled jobs •Motivational profile: Learning because they are required to do so by employer or law •Primary characteristics: Very young; many unemployed; low learning intensity; many learn informally •Motivational profile: Learning to adapt to changing workplaces, or perform better at work •Primary characteristics: Training provided by employer; limited informal learning •Motivational profile: Learning to improve career prospects, gain formal certification, or to perform their jobs better •Primary characteristics: Very young; often female; comparatively high education levels; high intensity learning •Motivational profile: Learning for non-work relatedreasons (e.g. personal interests) •Primary characteristics: high incomes; comparatively high education levels; often employed in high-skilled jobs •Motivational profile: Learning for both work and non-work related reasons (e.g.to improve careers and for their personal interests) •Primary characteristics: High incomes; high education levels; often employed in high-skilled jobs; long job tenure Adults participatingin non-formal or formal learning activities Adults not participating in non-formal or formal learning activities 11

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and obstacles to adult learning. Based on in-depth interviews with 34 interested, but non-participating, and 49 participating learners, a total of eight personas were identified based on their motivation, ambition, and obstacles and levers. The study then looked at the respective possible journeys of each persona. As described in the next section, the model identifies representative or “idealised” types of adult learners based on combinations of motivations and obstacles to learning. Unlike existing target groups in lifelong learning policies – which are often defined by a single characteristic, such as education level, employment status, or age – these profiles are shaped by multiple factors known to influence decisions to learn. Specifically, the segmentation approach considers motivation to learn, obstacles to lea rning and the

learning policies – which are often defined by a single characteristic, such as education level, employment status, or age – these profiles are shaped by multiple factors known to influence decisions to learn. Specifically, the segmentation approach considers motivation to learn, obstacles to lea rning and the interactions between these factors (e.g. age or health barriers may also reduce motivation to learn). While indicators of adult’s willingness to learn and the obstacles they face have been examined in many reports, the added value of a segmentation approach lies in its ability to reveal how a combination of factors influences the decision to learn. 1.3. Approaches for using a segmentation approach to improve adult learning policies The insights derived from adul