The many faces of adult learners

3. Considerations for applying learner profiles in policy and practice

The learner profiles identified for Bulgaria, Finland, Ireland and Portugal offer valuable insights into the diverse and often complex interplay of motivations, barriers, and enabling factors that shape adult learning decisions. By capturing both the reasons for participation and non-participation, these profiles can inform more nuanced, targeted, and responsive policy interventions tailored to the specific needs of different learner groups.

While these profiles provide a valuable framework for improving adult learning policies, their practical application raises important considerations. Key questions remain around how best to implement targeted approaches at scale while ensuring equity, and how to apply learner profiles in ways that are context-sensitive and ethical. This section explores the practical considerations for operationalising learner profiles, identifies associated risks and limitations, and highlights areas where further research could support more effective implementation.

3.1. Operationalising more targeted and tailored policies based on learner profiles

As described above, learner profiles can support the development of more responsive and effective adult learning strategies, including by (1) identifying priority groups; (2) improving information and guidance services; (3) tailoring financial and non-financial incentives; and (4) strengthening the evaluation and monitoring of policies. In practice, this means moving beyond one-size-fits-all strategies towards interventions that reflect the specific motivations, preferences and constraints of different groups of adults. Operationalising such an approach could involve the use of diagnostic tools to identify learner profiles and embedding learner profiling in systemic practice.

3.1.1. Using diagnostic tools to identify learner profiles

Tailored approaches could benefit from the development of diagnostic tools that enable education and training providers, guidance services, and policymakers to identify which learner profiles are most prevalent in their context. Such tools could inform programme and policy design, guide the targeting of financial support, or help shape learner support services. As described above, collaboration with education and training providers, social partners, and community organisations will be essential to ensure that interventions are context-sensitive and responsive to learners’ lived experiences.

These tools can take various forms but generally aim to identify how closely individuals or groups align with specific learner profiles. For example, in collaboration with the Flemish Government, the OECD developed a diagnostic tool based on the “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders” (OECD, 2022[3]). This diagnostic tool (see Box 3.1 ) estimates how individuals align with nine predefined profiles based on their characteristics, enabling a dual-level analysis: individual classification and aggregate-level assessment of reach and policy effectiveness.

To function effectively, such tools require an understanding of socio-demographic and labour market characteristics associated with each learner profile. While this was done in the Flemish study, it remains outstanding for the profiles developed in this paper. Further analysis would be needed to establish these characteristics for Bulgaria, Finland, Ireland and Portugal (See also the section ‘Potential areas for further research’ below for further discussion of this next step).

Box 3.1. Screening tool to implement learner profiles in Flanders

To support the Flemish Government in operationalising the nine learner profiles identified in the “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders” the OECD developed a screening tool to assess of how individual adults align with these profiles. Specifically, the tool estimates the extent to which individual’s characteristics correspond to those associated with each of the nine learner profiles. This enables a more concrete and policy-relevant application of the learner developed in the Flanders report.

The tool is underpinned by a model that assigns weights to a range of characteristics for each learner profile through a four-step process. Based on these weights, scores are calculated to indicate the strength of association with each profile. Rather than assigning individuals to a single ‘best-fit’ category, the tool generates scores for all nine profiles, reflecting the reality that individuals often display traits linked to multiple learner types.

To support diverse use cases, the screening tool was made available in three formats:

i. An interactive Excel version, which allows users to calculate profile scores by selecting different sets of characteristics. This version is particularly suited for assessing how closely individual adults align with one or more learner profiles.

ii. A questionnaire and codebook, designed to support data collection by practitioners such as guidance counsellors who work directly with adult learners.

iii. A syntax-based version, enabling policymakers and researchers to calculate profile scores at both the individual and aggregate level using survey data (e.g. from evaluations of learning incentives). This format facilitates the analysis of adult learning patterns and the reach of specific policies across different learner profiles.

Source: OECD (2022[3]), OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders, https://doi.org/10.1787/7887a565-en.

3.1.2. Embedding learner profiling in systemic practice

Beyond diagnostic tools, the effective operationalisation of learner profiles requires integrating this approach into wider systems of data collection, programme evaluation, and continuous improvement. Embedding learner profiling in systemic practice means that information about learner needs and motivations informs not only individual learning pathways but also institutional planning, curriculum design, and policy development.

A key enabler is capacity-building for frontline practitioners – teachers, trainers, and counsellors – so that they can recognise different learner types and adapt their pedagogical and guidance strategies accordingly. This may involve professional learning on interpreting learner data, using adaptive teaching methods, and supporting learners with diverse motivations and barriers

At the same time, sustained investment in digital infrastructure, learner support, and inclusive design principles. Learner support refers to the systems and services that help individuals access, navigate, and succeed in learning – such as academic guidance, mentoring, financial assistance, and flexible delivery modes. Inclusive design principles, in turn, ensure that learning environments and digital tools are accessible and responsive to diverse learner characteristics, including differences in language, disability, prior experience, and socio-economic background.

Taken together, these elements can help make tailored learning approaches scalable, equitable, and sustainable, allowing learner profiling to contribute meaningfully to more adaptive and inclusive education and training systems.

3.2. Risks and limitations of learner profiles

Learner profiles can serve as a powerful instrument for enhancing understanding the diversity of adult learners and for informing more targeted and effective policy responses. By drawing on data about individuals’ motivations, barriers, and socio-economic contexts, they offer a structured way to support tailored interventions. However, their use also carries limitations and potential risks that must be carefully managed.

3.2.1. Potential risk of oversimplifying a complex reality

While learner profiles can help bring structure and clarity to a highly diverse adult learning landscape, they inevitably simplify a complex reality. The findings from Bulgaria, Finland, Ireland and Portugal illustrate that adults are distributed across multiple profiles, and few individuals align neatly with a single category. Learners’ experiences are shaped by overlapping motivations, barriers, and socio-demographic as well as labour market contexts.

This complexity may be overlooked if profiles are interpreted too literally – for example, by focusing only on profile labels without considering the variation that exists within and across them. The challenge for policy is therefore to use profiles as tools for insight rather than as rigid classifications.

Learner profiles should be understood as analytical frameworks that highlight broad patterns and inform more tailored interventions, not as fixed descriptions of individuals. Recognising their boundaries and inherent diversity helps ensure that policy responses remain flexible, inclusive and responsive to the nuances of real learning experiences.

3.2.2. Potential risk of reinforcing stereotypes

There is also a risk that learner profiles, if applied uncritically, may unintentionally reinforce stereotypes or stigma. Descriptive labels that emphasise low motivation or disengagement can be misinterpreted as placing responsibility solely on individuals, rather than recognising the structural barriers – such as unequal access, limited guidance, or poor-quality provision – that often underlie such patterns.

If used without reflection, profiles could reproduce existing biases instead of supporting efforts to reduce them. To mitigate this risk, profiles should be communicated and applied in ways that respect individual agency, acknowledge diverse pathways into and through learning and focus on identifying and addressing systemic constraints rather than individual shortcomings.

When thoughtfully used, learner profiles can thus serve as tools for inclusion and equity, helping policymakers and practitioners better understand diversity among adult learners without resorting to reductive or normative assumptions.

3.2.3. The static nature of profiles

Learner profiles are necessarily based on data collected at a single point in time and thus represent static snapshots of what are often dynamic and evolving circumstances. Adults’ learning needs, aspirations, and constraints can shift in response to personal, economic, or social changes. If not regularly updated, profiles may become misaligned with current realities or fail to capture emerging needs There is also a risk of focusing too heavily on groups that are easier to identify, at the expense of those whose situations are more complex or less visible. To remain relevant and effective, learner profiles should be used with critical reflection, updated regularly, and complemented by more adaptive, learner-centred approaches.

3.3. Potential areas for further research

Several areas for further research could strengthen the evidence base and enhance the practical utility of learner profiles.

3.3.1. Using other data

The use of additional datasets could provide deeper insights. While this paper draws on data from the 2022 AES, future analyses could benefit from incorporating data from the OECD Survey of Adult Skills (PIAAC), which includes direct measures of skill levels in literacy, numeracy and problem-solving. Such integration could shed light on how skill levels intersect with learning motivations and barriers. Alternatively, adult learning surveys conducted at the national level can provide complementary insights that enrich the learner profiles identified through international surveys.

3.3.2. Enriching profiles with additional characteristics

Enriching profiles with further socio-demographic and labour market variables could enhance their relevance and applicability. To create more nuanced and detailed learner profiles, additional socio-demographic variables (e.g. education level, age, income) and labour market characteristics (e.g. occupation, employment status) could be integrated, provided they are available in the chosen dataset. Once the initial learner profiles are identified using LCA baseline models (see description in Annex A), individuals can be assigned to profiles, and their characteristics could subsequently be aggregated. This would allow for a clearer understanding of the composition of each profile, using observable variables such as education, employment status or income. These additional insights could support the implementation of more targeted and feasible interventions.

Although this paper does not include such a step, the methodology is illustrated in the OECD report “OECD Skills Strategy Implementation Guidance for Flanders, Belgium: The Faces of Learners in Flanders” (OECD, 2022[3]). For instance, the report found that Profile 3 – “Motivated but facing time-related obstacles” – was predominantly composed of young, highly educated adults with children.

3.3.3. Analysing their use in policymaking

Further research is needed on the use of learner profiles in policymaking. To date, only a limited number of OECD countries and regions have applied segmentation strategies to inform adult learning policies. More evidence on practical application – including how to develop effective diagnostic tools, embed them in institutional practice and evaluate their impact – would provide a stronger foundation for broader adoption.