The many faces of adult learners

1. A segmentation approach to target and tailor adult learning policies

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 front-loading 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, adult 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 European 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 labour-market 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 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 lower levels of education are disproportionately affected by additional constraints, including health issues, age-related limitations, and financial costs (Eurostat, 2024[8]).

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 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 access 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 smaller, 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, implementation 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.

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 policy-making 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 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 learning 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 adult learner profiles offer valuable guidance for policymakers and stakeholders seeking to better understand the diverse motivations and barriers faced by learners. This enhanced understanding can inform the development of strategies to foster engagement and address obstacles to participation. Learner profiles can support evidence-based decision-making by guiding the design and refinement of adult learning policies and practices. For instance, they can be used to assess the effectiveness of existing measures, identify gaps in reaching underserved groups and inform the development of more comprehensive approaches to tackle the full range of barriers to learning.

Beyond government, key stakeholders of skills systems – such as training providers, public employment services and research institutions – can apply these insights to improve the relevance and impact of their activities. This may include strengthening outreach strategies, tailoring training offers to better reflect learner needs and aligning programme design with the characteristics of the diverse profiles identified.

The practical application of adult learner profiles in policymaking can be approached in several ways, as demonstrated by the examples below:

• Identifying which learner groups to target: Insights from learner profiles can inform the targeting of adult learning policies by identifying groups that are inadequately reached or supported by existing skills policies. These profiles help to uncover differences in motivations and barriers, revealing, for example, that common target groups – such as adults with low levels of education –are far from homogeneous. Based on these insights, policymakers can design multifaceted interventions that respond to the specific needs of different sub-groups, thereby improving the effectiveness and equity of adult learning provision.

• Enhancing information and guidance services: Adult learner profiles can support the tailoring of information and guidance services. For example, profiles can inform digital outreach strategies – such as tailored messaging on social media platforms – by aligning content with the specific motivations and barriers of different learner types. Adults can be assigned to specific profiles based on their characteristics and approached with information that meets their learning needs. Profiles can also support making information on centralised online portals more tailored to user needs through a series of questions that direct them to appropriate learning opportunities and support measures. Additionally, career and learning counsellors can compare individual client characteristics with established learner profiles to gain insights into suitable learning opportunities, incentives, and support measures.

• Adapting incentives to the needs of learners: Financial and non-financial incentives – such are subsidies, tax advantages, paid training leave – can be targeted towards individuals and employers to help reduce both the direct costs of training (e.g. course fees, transportation costs) and indirect costs (e.g. lost wages, time away from work). Learner profiles can enhance the effectiveness of these incentives by identifying the groups most likely to benefit from specific types of support. For instance, individuals in profiles characterised by time-related obstacles could be prioritised for incentives that address time barriers, such as paid training leave or flexible learning schedules. Conversely, profiles characterised by limited financial resources may benefit more from support measures that reduce out-of-pocket expenses, such as grants, subsidised courses or low-interest loans. This targeted approach can help ensure that incentives are not only equitable, but also more likely to drive participation among underrepresented groups.

• Strengthening policy evaluation and monitoring: The learner profiles can strengthen evaluation and monitoring by offering a structured lens through which to assess whether existing initiatives are effectively reaching different groups and how future programmes can be better tailored to learners’ specific motivations and obstacles. Governments could introduce guidelines requiring ministries responsible for designing adult learning policies and programmes to evaluate their expected impact on distinct learner profiles. More comprehensive programme evaluations could also reveal whether certain profiles are utilising available incentives, or whether support is being disproportionately accessed by those with less pressing need. This approach can improve the efficiency, equity and responsiveness of adult learning systems over time.

The report “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders” (OECD, 2022[3]) provides several concrete examples of how the learner profiles can be applied to improve the design of adult learning policies and practices, providing important lessons for other countries seeking to adopt similar approaches.