3. Policy implications of the nine adult learner profiles in Flanders, Belgium

The insights generated from the nine adult learner profiles can help make policy makers and stakeholders more aware of both the different motivations of learners and the different obstacles they face, as well as the means to boost their motivation and overcome obstacles. Government representatives and stakeholders consulted in this project noted that the segmentation allows for a more nuanced understanding of the different types of learners and the multiplicity of reasons why adults are participating or not in learning activities. Stakeholders also confirmed that the profiles identified in this project are recognisable in the population of adult learners in Flanders.

This more nuanced understanding of the different types of learners could help to strengthen the design of future adult learning policies and practices at various levels. For example, organisations with an important role in the adult learning system, such as training providers and research institutes, could use insights from the model to improve their own initiatives, such as outreach campaigns, their training offers, and more. Many stakeholders consulted during this project noted that the profiles would help them assess whether they are currently targeting the right groups of learners with the right initiatives.

The insights these profiles provide into how differing motivations, obstacles and other factors combine to influence decisions to participate or not in learning can help Flanders (Belgium) to develop policies that are better targeted and tailored (see Box 3.1 for definitions) to the needs of learners. Policies that are targeted and tailored to the specific needs of learners are more capable of raising the participation of underrepresented groups than more universal approaches (OECD, 2020[1]). While universal instruments may have the benefit of low administration costs and ease of implementation, this can come at the expense of under-investment in key target groups. Indeed, adult learning measures that are less targeted tend to disproportionately benefit the high-skilled and, therefore, can result in high deadweight losses – i.e. the financing of learning activities that would have occurred even without public support.

In Flanders, adult learning initiatives are already targeted and tailored to a range of specific priority learner groups. However, these initiatives offered by different organisations are often not well co-ordinated, and as discussed in Chapter 1, have not yet led to a significant increase in participation among targeted groups. For example, while several incentives target adults with low levels of education, there is still a comparatively large 47 percentage point gap in participation rates between adults with low and high levels of education (Eurostat, 2021[4]). Moreover, reaching adults most in need of upskilling or reskilling has remained challenging for Flanders, with the various incentives not always being used by the adults who could benefit the most (OECD, 2019[5]). The nine profiles provide new insights into how multiple factors combine to influence the decisions of people in existing target groups to participate, which can support more targeted and tailored policies.

Government representatives and stakeholders consulted during this project shared many ideas about how the nine adult learner profiles could be used to inform policy making. This chapter will summarise and expand on these discussions by reflecting on the use of the nine profiles from two perspectives. First, it will examine policy insights for each adult learner profile, including what policies could be most helpful to them, how current policies could be further improved, and what important policies are currently insufficient or lacking. Second, it will assess the general policy implications of the adult learner profiles, including how they could be applied to support the evaluation and monitoring of policy, and how they could be used for the design and implementation of adult learning policies.

Insights from the nine profiles can help Flanders make its adult learning policies even more targeted and tailored, including by helping to identify groups not well served by existing skills policies, and by demonstrating the need for additional or different policies to bolster the motivation to learn and overcome the obstacles faced by specific profiles. In this section, relevant insights for policy making will be discussed for each of the four categories of motivational profiles (“unmotivated”, “motivated but facing obstacles”, “extrinsically motivated” and “intrinsically motivated”) and their underlying nine profiles (see Figure 3.1).

The first category of motivational profile, “unmotivated”, represents those least motivated to learn. This category of motivational profile can be further divided into Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles”. Together, these unmotivated adults represent 37% of the Flemish adult population. For both profiles, most adults indicate that they are unmotivated to learn, and this is largely driven by their sense that there is no need to learn.

To a great extent, the lack of motivation of these profiles is related to socio-economic and demographic characteristics. Profile 1: “Disengaged from learning” in particular is characterised as facing a variety of socio-economic challenges stemming from high-levels of unemployment and inactivity, which likely makes learning a relatively low priority. For example, adults with this profile may prioritise finding a job or overcoming challenges linked to poverty and inadequate housing over participation in learning. Profile 2: “Unmotivated due to age and health obstacles”, on the other hand, is characterised as being older, often in early retirement or having a permanent disability, which may again make participation in upskilling or reskilling a relatively low priority (e.g. adults feeling too old to learn new things).

Both profiles are also characterised as having low levels of education and are estimated to have comparatively low levels of skills, especially Profile 1: “Disengaged from learning”. Working adults with these profiles tend to be employed in jobs that require comparatively low- to medium-level skills (e.g. manufacturing is a common occupation for adults with Profile 1: “Disengaged from learning”) and that face a high risk of automation. These profiles are also characterised as having relatively low levels of income. Furthermore, within Profile 1: “Disengaged from learning”, non-native speakers are a significant minority. Participation in informal learning is also comparatively low for both profiles, but in particular for Profile 2: “Unmotivated due to age and health obstacles”.

Arguably, “unmotivated” profiles are in greater need than other profiles to upskill to address their low levels of skills, or reskill to support a return to the labour market or facilitate a transition to jobs with a lower risk of automation. It is therefore particularly concerning that not only are they not participating, but they are also unmotivated to learn.

Stakeholders consulted for this project considered adult learning information and guidance the most important policy lever for raising motivation to learn and for strengthening participation in learning by disadvantaged groups (e.g. adults with low levels of education, non-native speakers, adults with disabilities), which are highly concentrated in these “unmotivated” profiles. Improving the provision of adult learning information and guidance was considered even more important than financial and non-financial incentives for stimulating learning by these adults. However, stakeholders also noted that further study is needed to better understand which measures would work best to stimulate learning by “unmotivated” adults to support their career goals, personal growth, social participation and integration, and more.

The segmentation could help to strengthen adult learning policies to more effectively reach these “unmotivated” adults by providing detailed insights into their diverse characteristics. Some key policy insights for “unmotivated” adults are described in more detail below.

Adult learning information, including information on the benefits of learning and availability of learning opportunities and incentives, as well as guidance services, are considered some of the most effective policy levers for raising adult participation in learning (European Commission, 2015[6]). Analysis of Adult Education Survey (AES) data for Flanders shows that even when controlling for various socio-demographic variables, information and guidance is strongly associated with higher motivation and participation in learning.

High-quality information and guidance services are particularly important for engaging those adults who are least motivated to participate, i.e. adults with “unmotivated” profiles. These profiles are characterised by having low levels of education; those with low levels of education are generally less likely to seek out information. Guidance systems are therefore essential to help the least motivated adults identify their training needs, learn about available learning opportunities and incentives, and understand the skills needs of an evolving labour market (OECD, 2021[7]). However, a recent OECD survey of six countries found that many groups not participating in learning, and which are most vulnerable to changes in labour markets and societies, are not using career guidance as often as other groups of adults (OECD, 2021[7]).

As noted in the OECD Skills Strategy Assessment and Recommendations report for Flanders (OECD, 2019[5]), adults in Flanders have relatively good access to information and guidance about learning opportunities (see Box 3.2). However, stakeholders consulted in this project mentioned a range of challenges, including fragmentation in the overall provision of information and guidance, which makes it difficult for some groups of adults to navigate their options; limited links between learning and career development support; and a strong emphasis on learning for the purposes of work, which may not always be the best means for encouraging a culture of learning throughout life. A key challenge is that certain socio-demographic profiles are much less likely to make use of information and guidance than others. For example, 55% of Flemish adults with tertiary education received free information or guidance on learning opportunities, compared with only 19% of adults with less than upper secondary education (Eurostat, 2021[4]).

The current offer of information and guidance is not yet effectively reaching adults with Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles” (see Figure 3.2). Overall, a significant information gap can be observed between participating (Profiles 5-9) and non-participating (Profiles 1-4) learner profiles, with all non-participating learner profiles receiving less information or guidance free of charge than all participating learner profiles. Particularly striking is that among non-participating learner profiles, especially Profile 2: “Unmotivated due to age and health obstacles” and Profile 4: “Motivated but facing multiple obstacles”, few receive free information and guidance (20% and 16%, respectively).

Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles” are characterised as having large shares of inactive adults – a group often beyond the reach of information provided by employers. The small shares of “unmotivated” adults receiving information and guidance may therefore be partly explained by the large share who are unemployed. This is supported by the fact that a comparatively large share of adults (29%) with Profile 1: “Disengaged from learning” receives information and guidance services from (VDAB), which targets those unemployed and other jobseekers.

The fact that “unmotivated” profiles are often not yet receiving information and guidance is somewhat surprising given that several initiatives target specific groups of adults that are overrepresented in these profiles (see Table 3.1). These target groups include adults with low levels of education (in both profiles), unemployed adults and non-native speakers (highly represented in Profile 1: “Disengaged from learning”), as well as older people and people with disabilities (highly represented in Profile 2: “Unmotivated due to age and health obstacles”). However, some targeted initiatives are relatively small in scope and appear to not always succeed in reaching their target groups. For instance, the four disadvantaged groups (the so-called kansengroepen, adults with low levels of education; those with an immigrant background; those with a work disability; and older adults) indirectly targeted by the career guidance vouchers (loopbaancheques) account for only 21.6% of total users, even though they represent 51.6% of the working population. A recent survey in Flanders also shows that older generations in particular are much less aware of the existence of these vouchers than younger generations (Meylemans and Verhoeven, 2021[10]).

Based on the assessment above, it can be concluded that Flanders could better target Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles” when disseminating information and providing guidance on adult learning. This finding is also supported by various reports in Flanders. For example, recent expert advice focusing on learning and career guidance indicated the presence of the “Matthew” effect, with high-educated adults reaping the benefits of current policies (De Vos et al., 2021[11]).

To better ensure that initiatives reach their intended target groups, more could also be done to design initiatives to better reflect the heterogeneity of these target groups. For example, the segmentation demonstrates that four target groups of career guidance vouchers (the kansengroepen) are in fact highly diverse, with different motivational profiles and very different obstacles (see Figure 3.3).

Adults with low levels of education, and who are generally most in need of learning, are highly concentrated in the “unmotivated” profiles, and are almost equally spread over Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles”. However, as will be explained in the next sections, these two profiles would require different types of information and guidance services, as well as different types of providers of these services, for optimal impact. These insights highlight the importance of understanding that each of the existing broadly defined target groups (e.g. low-educated adults) are actually diverse, and that encouraging their participation in learning will require offering a package of incentives and support that responds to their multiple needs.

Flanders could, for instance, consider further refining these target groups by making a clearer distinction between Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles” when reaching out to these adults with information and guidance. In practice, this could be achieved by considering age as an additional factor. As Profile 2 has a large share of older adults, using age as an additional criterion for target groups (i.e. targeting adults not only with low levels of education but also between the ages of 55 and 65) could help to more effectively reach these adults. Implementing such an approach could, for example, involve reaching out to the types of businesses and organisations where adults with these characteristics are overrepresented.

A large share of the adult learning information and guidance services in Flanders is available online (as described in Box 3.2). However, several stakeholders consulted for this project noted the limitations of online tools, especially with respect to some of the groups most in need of learning, such as the “unmotivated” profiles. These concerns are supported by analysis of the current use of online information and guidance based on the segmentation, which shows that “unmotivated” profiles are using online information and guidance services less frequently than other profiles, especially the most motivated participating profiles.

Using online information and guidance requires a certain level of digital skills, self-motivation and autonomy, as well as the required digital infrastructure (OECD, 2021[12]). However, 37% of Flemish adults have low levels of basic digital skills, and some groups in particular risk digital exclusion, including older generations, adults with low levels of education and adults with low incomes – all characteristics highly represented in “unmotivated” profiles (Statistiek Vlaanderen, 2020[13]).

To raise motivation and to encourage the participation of “unmotivated” profiles, more active outreach and the provision of in-person guidance will be important. This point was emphasised by various consulted stakeholders. Flanders has a number of options to reach out more actively and directly to these adults.

Access to in-person guidance could be expanded for “unmotivated” profiles. Given the characteristics of these profiles (e.g. low basic skills and education), in-person support to access and understand information and opportunities is likely necessary. For the relatively small share of adults with “unmotivated” profiles that receive information and guidance, a comparatively large share is already receiving it in-person. For example, 26% of adults with Profile 1: “Disengaged from learning” receives information and guidance via face-to-face interaction, which is the highest share of all profiles.

More active outreach can also involve encouraging the greater use of existing career support measures, such as career guidance vouchers, by “unmotivated” profiles (OECD, 2019[5]). Despite efforts to target these vouchers at underrepresented groups, they are not yet effectively reaching the groups most in need. For example, people with low education levels and older adults (both characteristics strongly associated with “unmotivated” profiles) are currently underrepresented among those applying for career guidance vouchers – only 5.4% of career guidance voucher users in 2019 were adults with low levels of education, while 70.1% were highly educated (VDAB, 2019[8]).

The low uptake of learning and career guidance vouchers by unmotivated adults could be related to certain strict eligibility criteria, such as the requirement of having worked for seven years – which means that most inactive workers are ineligible – and the required personal contribution (e.g. EUR 40 for career guidance vouchers). To make these services more accessible, Flanders could potentially relax some of these criteria. Flanders could consider improving access to the vouchers for adults with “unmotivated” profiles by, for example, increasing the number of eligible hours of career guidance covered by the voucher or increasing the frequency with which these career guidance vouchers can be used – i.e. more than twice every six years.

Stakeholders consulted for this project noted that career guidance vouchers are only one type of support measure, and that consideration should also be given to offering other measures potentially even more important for encouraging the participation of “unmotivated” learner profiles. It was noted that while expanding access to learning and career guidance might be sufficient to facilitate the participation of some adults in learning, it is not likely enough for “unmotivated” profiles, as taking advantage of these services generally implies that individuals take some initiative on their own. The current demand-driven provision of information and guidance therefore does not respond well to the characteristics of “unmotivated” profiles, who may not actively seek out these incentives and services on their own.

Consequently, a recent advisory document prepared by Flemish labour market experts (De Vos et al., 2021[11]) proposes a more proactive approach is taken to reach out to adults to encourage their greater use of learning and career guidance services (e.g. active outreach to specific groups). In addition, an action plan developed by the Partnership for Lifelong Learning (Partnerschap Levenslang Leren), “Set a course for a learning Flanders” (Actieplan levenslang leren: koers zetten naar een lerend Vlaanderen), has also noted the need for a targeted strategy (een gesegmenteerde mobiliseringsstrategie) for engaging hard-to-reach groups (Partnership for Lifelong Learning, 2021[14]). It was recommended that this strategy be built on this OECD segmentation study and the Flemish “customer journeys” study (see Box 3.6).

As mentioned previously, the overall lack of interest in learning for “unmotivated profiles” – and consequently their limited interest in information and guidance on this matter – are often the result of personal circumstances (e.g. socio-economic challenges, disabilities). To raise their motivation and overcome these obstacles, the Flemish Government could explore ways to mobilise the help and resources of the organisations and institutions already working closely with adults with these profiles, such as employers, trade unions, social partners and charities, and VDAB. For example, organisations and charities that work with the most vulnerable groups could become more involved in promoting learning for these adults. They are better positioned to know the individuals and their specific challenges, build some level of trust with them, and play a role in encouraging and guiding their participation in learning.

The segmentation provides insights into which stakeholders could be best placed to reach out to “unmotivated” profiles by indicating which socio-demographic and labour market characteristics are linked to low motivation to learn. For example, organisations with a mission to support the labour market and social participation of immigrants and non-native speakers – both overrepresented in Profile 1: “Disengaged from learning” – could be mobilised to encourage learning among these adults. Moreover, as Profile 1: “Disengaged from learning” is characterised by high rates of unemployment, VDAB could play an active role in reaching out to this profile. In addition, for Profile 2: “Unmotivated due to age and health obstacles”, Flanders could mobilise the help of organisations in direct contact with adults with permanent disabilities and older workers.

Consulted stakeholders noted that for the most vulnerable groups, who are most highly concentrated in Profile 1: “Disengaged from learning”, it could be beneficial to better integrate skills development and skills awareness initiatives in various social services, possibly at the local level (e.g. local centres for public welfare that support adults in poverty, health issues, and more). An example of such an initiative is the Learning Opportunities (Leerkansen) project, where Centres for Basic Education work with NGOs focusing on adults from low socio-economic backgrounds to integrate learning into the activities of these associations. Stakeholders also mentioned the potential to expand the role of Learning Shops (Leerwinkels) to promote learning among the most vulnerable groups. These one-stop-shops for potential learners specifically target adults without upper secondary education, prisoners and immigrants. However, they are not yet present in all Flemish regions, and do not have structural funding from the Flemish Government.

Employers could also play a more important role in encouraging participation in learning among employees with low motivation. Currently, “intrinsically motivated” learner profiles – such as Profile 8: “Participating for personal development” and Profile 9: “Participating for professional and personal development” – are most likely to be the recipients of information and guidance from their employers. The importance of involving employers was stressed by stakeholders, and analysis conducted for this project supports this, showing that information and guidance provided by employers and employer organisations is strongly associated with participation in learning. This is of particular importance for “unmotivated” learners, given that many will likely only learn if required to for their jobs. Promoting on-the-job learning by employers will therefore be key to boost their participation.

Given that for “unmotivated” profiles a majority are employed in small businesses (fewer than 50 employees), it would be helpful to mobilise the support of these small businesses in promoting awareness of the benefits of adult learning. However, small and medium-sized enterprises (SMEs) currently do not often take on such a role as they typically lack sufficient human resource management capacity (De Vos et al., 2021[11]). Networks of small businesses and organisations working with small enterprises could be encouraged to work together to promote better understanding of the benefits of upskilling workers. Furthermore, sectoral organisations could play an important role in promoting learning among adults and employers in sectors hard hit by the pandemic and/or where there are large shares of jobs at high risk of automation (e.g. specific manufacturing sectors).

Tailoring information in Flanders to the specific and diverse needs of different learners could help to raise the impact of information and guidance (OECD, 2019[5]). There is no one-size-fits-all information and guidance service that will meet the needs of all learners. As the nine adult learner profiles demonstrate, adults have diverse reasons for not participating in learning and it is therefore unlikely that general messaging will resonate with any particular group of adults.

Insights into the motivations and obstacles, and related socio-demographic and labour market characteristics, of the learner profiles can be used to better tailor information and guidance services. While tailored services are important for all learners, they are particularly important for “unmotivated” profiles, who are most in need of assistance to understand the benefits of learning and the incentives, support and learning opportunities available and how they might be accessed. In addition, for many “unmotivated” profiles it is very likely that their low motivation is the result of past negative learning experiences that influenced their self-confidence and attitudes towards learning. Therefore, sharing testimonials of learners with similar profiles and being informed about the many personal and professional benefits could help adults gain the confidence required to participate in learning.

Table 3.2 provides examples of the sorts of messages that might be most effective in increasing participation in learning among the two “unmotivated” profiles. These messages are directly linked to the unique characteristics of the profiles in terms of their motivations and obstacles, as well as their socio-demographic and labour market characteristics.

To increase the motivation of Profile 1: “Disengaged from learning”, messages should highlight the overall benefits of learning for both work and life, and share testimonials from disengaged learners who have had positive learning experiences. Moreover, as this profile is characterised by high levels of unemployment and inactivity, messages should emphasise how reskilling or upskilling can facilitate employability, and highlight the learning opportunities and support measures offered by VDAB. Given that many with this profile are also non-native speakers, messages could be made available in languages other than Flemish.

For Profile 2: “Unmotivated due to age and health obstacles”, messages should emphasise how participation in learning can benefit older individuals (e.g. learning basic digital skills, meeting new people) and those with disabilities. Messages should also aim to raise awareness of the incentives and support measures available to help overcome the obstacles faced by older people and people with health problems or disabilities, as well as highlight relevant learning opportunities.

Given that “unmotivated” working adults are often employed in jobs with a high risk of automation, they should also be made aware of these risks and how upskilling and reskilling opportunities could help them to transition to sectors and jobs with lower risk. They should also be informed about the availability of career guidance vouchers – which few “unmotivated” adults are currently making full use of (VDAB, 2019[8]) – as well as other guidance services that could help them better navigate their upskilling and reskilling options (e.g. as provided by VDAB, employers, unions and others). In addition, as these profiles have characteristics associated with weak digital skills (e.g. older generations, people with lower levels of education and people with low incomes), information could also highlight learning opportunities to develop these skills (Statistiek Vlaanderen, 2020[13]).

While information and guidance services are arguably the most important policy lever for promoting learning among “unmotivated” learning profiles, other measures should still be considered. The reason for this is that measures designed primarily to boost motivation, such as information and guidance, will not likely be sufficient to raise participation in learning. For “unmotivated” profiles, information and guidance services will be most effective when included as a package of support to raise participation and improve employability (OECD, 2019[15]).

Although adults with “unmotivated” profiles often do not report many obstacles to participation – apart from the age and health obstacles faced by adults with Profile 2: “Unmotivated due to age and health obstacles” – this is likely a reflection of their general lack of motivation and their perception that they have no need to learn, rather than the absence of any obstacles to learning. In other words, if they were motivated and saw the need to learn, they most likely would report facing obstacles to learning. Therefore, it would be important that these obstacles are addressed by ensuring that “unmotivated” adults have access to relevant learning opportunities and financial and non-financial incentives.

The Centres for Adult Education (Centre voor Volwassenonderwijs) and Centres for Adult Basic Education (Ligo – Centra voor Basiseducatie) are the main public providers of learning opportunities for adults with low motivation to learn (Department of Education and Training, 2021[16]). The Centres for Adult Education support the development of a wide range of skills, such as technical skills and languages, in modular and flexible formats (e.g. evening courses). Through these centres, adults can also obtain a secondary education degree through “second chance education” (Tweedekansonderwijs). Centres for Adult Basic Education provide courses in basic skills (e.g. numeracy, digital skills) and Dutch as a second language, which is the most popular course by a large margin.

As of 2019, Centres for Adult Basic Education and Centres for Adult Education have received a portion of their funding based on the profile of enrolled students, with a larger share of funding available when targets are met for the share of enrolled unemployed adults, jobseekers or those without an upper secondary education diploma. However, the impact of this legislative change is difficult to assess due to the COVID-19 pandemic, which resulted in a significant drop in enrolment. The programmes offered by the centres also offer registration fee exemptions for specific groups of adults. For instance, fees are waived for some vulnerable learners who would like to participate in Centre for Adult Basic Education courses, and for those without a secondary diploma who enrol in courses at a Centre for Adult Education.

There are also various other programmes for which specific groups of adults do not have to pay any fees. For example, non-native speakers are a key target group for Flanders and, as a result, language courses are compulsory and often free for many adults without a basic knowledge of Flemish. Adults learning Flemish are assigned to a Centre for Adult Basic Education, Centre for Adult Education or a University Language Centre, depending on their existing level of Flemish.

Based on this overview it appears that especially for adults with Profile 1: “Disengaged from learning”, there are already many accessible learning opportunities, as all the target groups of these education and training providers (adults with low levels of education, unemployed adults, non-native speakers and those with a low socio-economic backgrounds) are highly concentrated in this profile. However, there appear to be fewer providers that specifically target adults with Profile 2: “Unmotivated due to age and health obstacles” (e.g. older adults and those with health problems or disabilities).

Insights into the characteristics of “unmotivated” profiles can also provide valuable input into how to improve the design of programmes. For example, employed “unmotivated” adults are often found in occupations related to manufacturing and engineering (e.g. manufacturing and processing, engineering, using digital tools to control machinery). This insight could provide an indication of the sorts of skills they might need to develop.

However, many of these “unmotivated” adults are working in occupations facing a high risk of automation. Therefore, the identification of potential pathways between occupations in decline and those experiencing growth will be important, as will be assessments of the skills that will need to be developed to facilitate these transitions. Stakeholders consulted in this project indicated that current adult learning incentives in Flanders do not always effectively support these broader transitions. For example, as will be discussed in the next section, Flemish training incentives are currently available only for specific courses (i.e. listed in the training database for Flemish training incentives), and are limited in terms of course duration and funding, which may restrict adults in developing the sorts of skills they need to transition to new jobs.

The second category of motivational profile, “motivated, but facing obstacles”, represents adults not participating in learning because of the obstacles they face. This category of motivation profile can be further divided into Profile 3: “Motivated but facing time-related obstacles” and Profile 4: “Motivated but facing multiple obstacles”. Profile 3: “Motivated but facing time-related obstacles” represents adults who are not participating because they do not have enough time for learning due to either a busy schedule, family responsibilities, or both. Profile 4: “Motivated but facing multiple obstacles” represents adults who do not participate because they face of a range of obstacles, including the cost of participation, the absence of a suitable learning offer, and obstacles relating to their health and/or age.

Compared with “unmotivated” profiles, there are comparatively larger differences between the characteristics of the two “motivated, but facing obstacles” profiles. While Profile 3: “Motivated but facing time-related obstacles” is characterised by young adults, often women, with children, and those with high levels of education, Profile 4: “Motivated but facing multiple obstacles” is characterised by a comparatively large share of adults who are older, have low levels of education, and/or are working in jobs at high risk of automation. Together, these two profiles represent 15% of the adult population in Flanders.

For both profiles, reducing the obstacles to participation in learning activities is crucial to boost their participation. While obstacles to participation (especially time-related obstacles) are experienced by all profiles, they are the primary reason that these two profiles are not participating in learning. These profiles confront different obstacles related to their distinct characteristics and would, as a result, require very different policy responses to boost their participation.

Different types of financial and non-financial incentives are the most important policy levers for boosting the engagement in learning of these profiles. While incentives can play an important role in raising extrinsic motivations to learn, they primarily help by reducing the obstacles for both individuals and employers (OECD, 2017[17]). Financial and non-financial incentives could target individuals and employers, and aim to reduce both the direct costs (e.g. course registration fees, transportation costs) and indirect costs (e.g. lost wages, time off from work) associated with training.

The two “motivated, but facing obstacles” profiles should be priority target groups for the Flemish Government as they are already willing to learn and there are some clear policy actions that could be taken to translate this motivation into active engagement.

Time-related obstacles are the most frequently mentioned obstacles to participation in surveys of Flemish adults. Overall, depending on the survey, 22-26% of the Flemish adult population indicates that schedules (e.g. time constraints due to work) and competing family responsibilities are obstacles to their participation in learning activities (Eurostat, 2021[4]). Consulted stakeholders often mentioned the importance of addressing these obstacles in Flanders.

While time-related obstacles are relevant for most profiles, for Profile 3: “Motivated but facing time-related obstacles” it is their defining characteristic. Profile 3 can be characterised as being young, employed, having children and high educated. These adults are also comparatively often employed in jobs requiring computer programming skills and/or jobs that require the use of digital tools for collaboration and problem solving, which suggests that they are comparatively often working in high-skill occupations where training is often important to ensure that skillsets remain up to date.

To reduce time-related obstacles to learning there are already several measures in place (see Box 3.3). The Flemish Government introduced the incentive Flemish training leave (Vlaams opleidingsverlof), which allows employees to take paid leave from their employment for learning. At the federal level there is time credit for training (Tijdskrediet met motief opleiding), which allows employees in the private sector to take leave from work under certain conditions to pursue training. This measure is supplemented by Flemish training credit, which provides an extra financial benefit for employees who want to reskill or upskill. There are also several measures in place in the public sector, including the equivalent of the time credit for training. Finally, a law on Workable and Flexible Work (Werkbaar en Wendbaar Werk) requires employers in Belgium to provide full-time employees with five days of paid training each year.

Insights from the segmentation could help ensure that existing time-related measures reach the groups most in need, such as Profile 3: “Motivated but facing time-related obstacles”. For instance, to raise awareness of the incentives among adults with Profile 3, more active outreach to young adults with children could be considered. This would help to inform them about available incentives and support measures, including Flemish training leave and time credit for training. As Flemish training leave is the only Flemish training incentive not targeted at a specific group, Flanders could consider targeting it at adults with Profile 3, for example by increasing the number the eligible of hours of training leave to make it more attractive to those facing time-related obstacles.

Given that these time-related obstacles are a significant impediment to greater adult participation in learning for all profiles, there is a potential opportunity to expand and/or further promote the use of Flemish training leave and time credits for training.

A number of consulted stakeholders suggested that these incentives could be made more attractive, including by expanding the range of programmes for which individuals are eligible to take training leave and by increasing the number of hours of eligible leave per year. This suggestion contrasts sharply with elements of the 2019 reform of Flemish training incentives, which decreased the number of programmes for which Flemish training leave can be used, as well as the maximum number of hours of leave per year for which learners are eligible (from 180 hours to 125 hours). It should be noted, however, that in the academic year 2021/2022 an experiment was launched named “common right of initiative” (gemeenschappelijk initiatiefrecht), which doubles the maximum hours of Flemish training leave to 250 hours per year on the condition that the training is proposed by employers to strengthen their employees’ future opportunities in the sector or in the Flemish labour market (Vlaanderen, 2021[18]). Flanders could also examine whether the amount of financial compensation provided by the various measures (e.g. Flemish training leave, Flemish training credit) is sufficient to overcome the financial obstacles that learners also often face – for example, it may not be possible for some learners to work fewer hours to train if the financial compensation that adults receive is insufficient to cover basic needs.

Consulted stakeholders mentioned that the burden for employers should also be considered when discussing training leave and other measures that help adults participate in learning activities during working hours. Many of the young parents with Profile 3: “Motivated but facing time-related obstacles” already work reduced hours due to family responsibilities. Therefore promoting leave for training for these people could create challenges for employers, especially in smaller firms.

Other policies that Flanders could consider to overcome time-related obstacles include expanding on-the-job learning, as well as other initiatives that combine learning and work (e.g. dual learning, which has been extended to adult education). In addition, further promoting flexibility in adult learning provision could promote participation by adults with busy schedules. International evidence suggests that flexibility in the format (e.g. part-time, online) and design (e.g. modular, credit-based courses) of training helps to overcome time-related obstacles, especially for medium- to high-skilled workers (OECD, 2019[19]). Finally, Flanders should adopt policies that mitigate other types of time-related obstacles, such as family responsibilities. This would imply a different set of policies, such as affordable and accessible childcare.

While time-related obstacles are the most common reason why motivated adults do not participate in learning, there are various other obstacles that could prevent participation. Profile 4: “Motivated but facing multiple obstacles” represents this group of adults who are facing a more diverse range of obstacles, including the cost of participation, lack suitable courses, health/age-related obstacles, and, to a lesser extent, a lack of support by employers and public services, as well as other personal reasons (e.g. having had a negative learning experience in the past).

This profile has characteristics relatively comparable with those of the first two “unmotivated’’ profiles. But despite their comparable characteristics, Profile 4: “Motivated but facing multiple obstacles” is characterised by a willingness to participate. Therefore, their lack of participation is very unfortunate.

While cost is relatively often cited as an obstacle to participation in OECD countries overall, it is less commonly cited as an obstacle in Flanders (OECD, 2019[22]). This is likely largely the result of comparatively good access in Flanders to a range of financial incentives directed at both individuals and employers (see Box 3.4), and the generally very low registration fees for education and training in Flanders, compared to other OECD countries. However, despite these incentives, there are still many adults who report that cost and a lack of support by both employers and/or public services are a reason for not participating, and these adults are highly concentrated in Profile 4: “Motivated but facing multiple obstacles”. This highlights the potential to better direct these incentives to this group of adults.

To reduce financial obstacles, Flanders is already targeting some of these incentives at certain underrepresented groups of adults, many of which have characteristics similar to Profile 4: “Motivated but facing multiple obstacles” (e.g. low levels of education). For example, the training vouchers (Opleidingscheques) have been reformed in recent years to encourage their greater uptake by employees without tertiary education. Highly educated adults are now only able to use the vouchers for career-oriented education that is deemed necessary as part of a personal development plan (persoonlijk ontwikkelingsplan) drawn up with a career counsellor. Employees without tertiary education can use these vouchers for any course in the education database for Flemish training incentives. Moreover, as mentioned before, there are various registration fee exemptions in place for vulnerable learners, non-native speakers, and others.

Despite these efforts to target financial incentives at specific groups, there is evidence that they are not yet effectively reaching those most in need, with highly educated adults still representing a significant share of users (Department for Work and Social Economy, 2021[23]). Moreover, while the relative share of training vouchers used by underrepresented groups has been increasing, this has been largely offset by an overall decrease in the use of the vouchers. The share of training voucher requests made by highly educated employees decreased from 47% in 2003-2020 to only 6% in 2020/2021, with the number of requests in 2020/2021 at its lowest level since the introduction of the vouchers in 2003 (Department for Work and Social Economy, 2021[23]). Targeted adults, such as those with low levels of education, are also less aware than adults with higher levels of education of the available incentives, such as training vouchers (Meylemans and Verhoeven, 2021[10]).

These are indications that Flanders could do more to support Profile 4: “Motivated but facing multiple obstacles”, in particular to overcome financial obstacles. To start, Flanders could aim to expand the overall use of these financial incentives, including by countering the decreasing trend in the number of training voucher applications. To put the current levels of utilisation of the training vouchers in perspective, 11 358 employees benefited from the measure in 2020/2021, while Profile 4: “Motivated but facing multiple obstacles” alone represents around 200 000 employed adults.

Flanders could also possibly further improve the effectiveness of existing financial incentives by targeting measures not only based on education level, but also on other characteristics associated with low take up. For example, Flanders could combine the “no tertiary education” criteria for more benefits of training vouchers with additional indicators associated with Profile 4: “Motivated but facing multiple obstacles” (e.g. mid-skill level occupations). Alternatively, Flanders could explore the use of other criteria for incentives to reach these profiles. For example, financial incentives could potentially be targeted at adults with low incomes who are most likely to face cost barriers to participation. Flanders could also increase the value of the incentives for adults with Profile 4: “Motivated but facing multiple obstacles”.

Financial incentives for employers to invest in the skills development of their employees could also be better targeted to increase their impact. These incentives could help to increase their provision of on-the-job and other sorts of training. Given that employment in small businesses is associated with low participation in education and training, Flanders might consider introducing or expanding incentives that target SMEs (Eurostat, 2021[4]). Adults with Profile 4 “Motivated but facing multiple obstacles” are also predominantly employed in these small businesses. In Flanders, some incentives are in place that specifically target SMEs, such as the SME Wallet [KMO-portefeuille]), which potentially could be expanded.

The many obstacles to learning faced by otherwise motivated adults highlights the need for a diverse policy mix to support their participation. Financial incentives, non-financial incentives and a comprehensive and accessible education and training offer are all elements of an effective policy response for increasing the participation of motivated adults facing obstacles to learning. However, these policies need to be complemented with information and guidance to raise awareness of the availability of learning opportunities, as well as incentives and support measures to facilitate access.

Flanders could do more to improve access to information and guidance for “motivated, but facing obstacles” profiles. Figure 3.2 showed that these profiles are not often receiving adult learning information and guidance. In particular, only 16% of Profile 4: “Motivated but facing multiple obstacles” receives information and guidance, the lowest share of all profiles.

Targeted information and guidance could help to raise awareness and improve the accessibility of learning incentives. In Flanders, the broad range of incentives makes it difficult for some types of learners to navigate their options, thereby creating a need for clear information and guidance on these incentives (OECD, 2019[5]). To a large extent, the learning and career account in Flanders (leer- en loopbaanrekening) already has the objective to make information and guidance on learning incentives more accessible (see discussion later in this chapter and Box 3.7) (Department for Work and Social Economy, 2022[24]). A first phase of the development of the learning and career account involved the introduction of the Wizard Flemish training incentives (Wegwijzer Vlaamse Opleidingsincentives), which is a tool designed to help adults access information on the incentives available to them, with users responding to eight background questions resulting in a personalised overview of available training incentives (Flemish Government, 2021[25]). In a planned second phase, Flanders aims to develop a personal digital wallet with tailored information on training incentives and financial support (see Box 3.7). This digital wallet will be offered on the central citizen portal of the Flemish Government, Mijn Burgerprofiel. The wallet will be gradually linked to a personalised career platform from VDAB, where digital tools for career advice and career orientation are offered.

Targeted information and guidance could also help adults to identify the most suitable learning opportunities in Flanders. The OECD Skills Strategy Assessment and Recommendations report for Flanders (OECD, 2019[5]) noted that there are a wide range of providers of adult education and training in Flanders, and that the offer is therefore somewhat fragmented. As in the case of the two “unmotivated profiles” (Profile 1: “Disengaged from learning” and Profile 2: “Unmotivated due to age and health obstacles”), the “motivated, but facing obstacles” profiles require access to high-quality information to help them navigate the range of learning options available.

Information about incentives and the availability of learning opportunities should be tailored to the unique needs and characteristics of “motivated, but facing obstacles” profiles (see Table 3.3). For instance, to encourage the participation of Profile 3: “Motivated but facing time-related obstacles”, information on the availability of flexible courses (e.g. part-time, modular and online) would be important to help them overcome time-related obstacles. In addition, given that Profile 4: “Motivated but facing multiple obstacles” is characterised by employment in jobs with a high risk of automation, information could aim to raise awareness of these risks and demonstrate how upskilling and reskilling opportunities could support the transition to sectors and jobs with lower risks.

The third category of motivational profile represents those already learning and whose participation in learning is extrinsically motivated. The “extrinsically motivated” motivational profile can be further divided into Profile 5: “Reluctant but required to participate”, Profile 6: “Participating in response to work pressures” and Profile 7: “Participating to strengthen career prospects”. Together these profiles represent 37% of the Flemish adult population.

There are important differences in the reasons for participation and in the characteristics of the three “extrinsically motivated” motivational learner profiles. Profile 5: “Reluctant but required to participate” represents adults participating in learning, but only because they are required to do so by their employer or by law. This profile consists of adults who are relatively young, a large share of unemployed, as well as those with lower levels of education than the other extrinsically motivated profiles (although they are relatively better educated than any of the non-participating profiles – i.e. profiles 1 to 4). Profile 6: “Participating in response to work pressures” represents those participating in learning to adapt to organisational or technical changes in the workplace or to perform better in their current job. This profile is characterised by having better educational and employment outcomes than Profile 5: “Reluctant but required to participate”, but worse than Profile 7: “Participating to strengthen career prospects”. Profile 7 represents adults participating in learning to obtain a personal goal. This profile stands out the most among the extrinsically motivated profiles for being predominantly female, having comparatively higher levels of education and being relatively young.

Unlike the first two non-participating categories of motivational profiles (“unmotivated” and “motivated, but facing obstacles” profiles), policy interventions aimed at “extrinsically motivated” learners will not aim to nudge them towards participation, but rather to ensure that they continue to be motivated to participate. Many of these adults benefit from existing incentives and information and guidance services that encourage and enable their participation. Therefore, Flanders needs to ensure that in targeting more resources towards encouraging and supporting learning by those not already participating, it does not reduce the incentives for participation by those already learning due to external pressures.

The fact that participation by “extrinsically motivated” profiles is motivated by external factors makes their participation more vulnerable to changing circumstances. For example, the participation in learning of adults with Profile 5: “Reluctant but required to participate” is partly driven by their employers’ ambitions to upskill their workforce, which might weaken when skills shortages ease or economic conditions deteriorate.

To strengthen the commitment of extrinsically motivated adults to continuous learning, Flanders could take steps to bolster “intrinsic” motivation. Developing the intrinsic motivation of adults to learn was stressed as an important objective by stakeholders consulted in this project. In times of crisis, such as the COVID-19 pandemic, intrinsic motivation is becoming even more relevant, with intrinsic self-motivation important for engagement in online learning, for example. As described in Chapter 2, intrinsically motivated profiles (e.g. Profiles 8 to 9) are more likely than extrinsically motivated profiles to report positive outcomes from learning. However, some stakeholders consulted in this project warned against viewing intrinsically motivated learners as somehow superior to those extrinsically motivated. Indeed, it should be noted that a vast majority of those with Profile 5: “Reluctant but required to participate” reported positive outcomes from learning and, in particular, better performance in their current job and the achievement of personal objectives.

Strengthening intrinsic motivations to learn will likely be easiest for Profile 7: “Learners participating to strengthen career prospects”, which represents adults learning to obtain a personal goal but who are very much driven by the desire to improve career prospects rather than by an intrinsic interest in a given learning topic. The difference between these extrinsically motivated learners and intrinsically motivated learners is already quite subtle, and they share similar socio-demographic and labour market characteristics. They also participate intensively in learning activities (as measured by the number of hours in learning), even more so than adults with intrinsic profiles.

Improving the overall learning experience for extrinsically motivated learners is arguably the most effective way to strengthen their motivations to continue learning. This can be achieved by, for example, creating learning experiences that make people want to learn more and by raising awareness of the inherent benefits of learning for personal and professional development.

There are several factors involved with creating positive learning experiences. To start, it entails tailoring programmes to the specific needs of different types of learners. A flexible and responsive adult education and training system is needed to respond to the diverse needs of a broad spectrum of adult learners with different backgrounds, as well as to the continuously evolving skill needs of the labour market (OECD, 2019[5]). More specifically, this means ensuring that curriculum design is informed by current and future skills needs; offering training in flexible formats (e.g. part-time, online) and designs (modular, credit-based courses); creating education and training systems that can rapidly approve and introduce new courses in response to changing demands; and strengthening systems of skills recognition and validation to support the efficient acquisition of credentials and improve the visibility of skills acquired outside of formal education and training. While a flexible and responsive adult education and training system is important for all profiles, it is particularly important for extrinsically motivated adults as they are already participating in learning, and the quality and success of that experience could nudge them towards becoming intrinsically motivated learners.

As described above, the three extrinsically motivated profiles are very different, not only in their attitudes towards learning, but also in terms of their broader socio-demographic and labour market characteristics. These profile-specific characteristics should be considered when promoting or offering specific courses. For example, Profile 6: Participating in response to work pressures” is associated with employment in the health and social work sectors and, therefore, raising awareness of courses in these sectors could encourage continued participation in learning.

Expanding the range of contexts in which learning opportunities are made available could help to foster an ongoing commitment to learning (OECD, 2019[5]). The provision of learning opportunities in workplaces in particular helps to ensure that learning activities are relevant, and provides opportunities to apply skills in practical situations, all of which should increase motivation to learn, support the successful acquisition of knowledge and skills, and, by extension, strengthen commitment to continuous learning. Informal learning in workplaces, communities and homes is also a key component of a culture of lifelong learning. Informal learning is unstructured and often unintentional, and includes activities such as learning by doing or learning by observing others. Promoting this type of learning would be particularly important for Profile 6: Participating in response to work pressures”, which is characterised by comparatively low participation in informal learning. One way to increase engagement in informal learning is to promote the adoption of high-performance workplace practices (HPWP) and other management practices that emphasise the need for employees to learn and take on new responsibilities (OECD, 2019[5]).

“Extrinsically motivated” profiles are less likely than other profiles to report facing obstacles to participation in learning. This is not surprising given that they are already participating in learning – i.e. even if they did face obstacles, they managed to overcome them. To some extent their participation in learning could suggest the effectiveness of various incentives already in place to overcome obstacles relating to time, cost and other factors. However, some “extrinsically motivated” learners do report time-related obstacles (e.g. busy schedules, family responsibilities), suggesting that there is still room to improve awareness of existing incentives and support measures to overcome these obstacles.

Stakeholders consulted during this project warned against assuming that those currently participating in learning due to external pressures will continue to be motivated to learn if their access to incentives and support measures is withdrawn. At the same time, to free up resources to support learning by those currently not participating it is important for countries to minimise deadweight loss effects. One potential way to balance these pressures is to appeal further to both the extrinsic and intrinsic motivations to learn. In addition to providing high-quality and relevant learning opportunities (as discussed above), this would entail the development of information and guidance services that emphasise how people can benefit from continued participation. Table 3.4 presents an overview of possible messages tailored to the different learner profiles.

To encourage the continuous learning of Profile 5: “Reluctant but required to participate”, efforts could be taken to raise awareness of the benefits of continuous learning and upskilling throughout life, including in young adulthood (given the low average age of Profile 5). This could help to address this profile’s comparatively low motivation to participate further once having already participated. Given that Profile 5 is more likely than other participating profiles to be employed in occupations facing a high risk of automation, information could also emphasise opportunities to reskill to transition to more secure occupations.

To encourage the continuous learning of Profile 6: “Participating in response to work pressures”, information could highlight suitable learning options to increase career prospects, as well as the benefits of learning in terms of achieving certain desirable work outcomes or changing career paths. This could help make adults with this profile internalise the external factors that made them want to participate, and make them more aware of the long-term benefits of skills development.

To encourage the continuous learning of Profile 7: “Participating to strengthen career prospects”, information could emphasise what courses are available to strengthen their career prospects and, given the low average age of this profile, the benefits of learning for young adults. While Profile 7: “Participating to strengthen career prospects” is comparatively strongly associated with not wanting to learn more, this might be explained by the very high intensity of their existing learning activities – i.e. they may feel that they are already participating in all the learning they want or need.

The fourth category of motivational profile represents those already learning and whose participation in learning is intrinsically motivated – i.e. they participate in learning for its inherent pleasure and satisfaction. This motivational category can be further divided into Profile 8: “Participating for personal development” and Profile 9: “Participating for professional and personal development”. Both profiles are characterised by learning to increase knowledge and skills on a subject of interest. However, while Profile 8: “Participating for personal development” typically participates in learning to gain knowledge and skills useful in everyday life and/or for personal interests (e.g. personal development), Profile 9: “Participating for professional and personal development” typically participates to improve career prospects or to perform better in their job (e.g. professional development). The intrinsically motivated profiles represent 10% of the adult population.

These profiles more than others are characterised by high levels of education and employment in high-skilled occupations. Profile 8: “Participating for personal development” stands out for its large share of employment in occupations related to health, social work and education, as well as the lowest risk of automation of any profile. Profile 9: “Participating for professional and personal development” is characterised by the highest household income of all profiles and the large share in managerial positions.

For intrinsically motivated learners, incentives and information and guidance services are less crucial for ensuring their participation in learning than they are for extrinsically motivated learners. Their intrinsic drive to learn is often enough to ensure their continued participation.

Arguably, intrinsically motivated learners will participate in learning even without any incentives and support. However, there are indications that they are making use of these measures. Indeed, “intrinsically motivated” profiles share many socio-demographic and labour market characteristics (e.g. high-educated, professional occupations) known to be most strongly associated with the take up of incentives, including career guidance vouchers and Flemish training credit (VDAB, 2019[8]; Department for Work and Social Economy, 2021[20]).

This might suggest potential deadweight loss effects, with incentives and other support being provided to individuals who would have participated in learning even without this assistance. However, several stakeholders consulted in this project noted that the availability of this assistance might be a key factor driving motivation, and cautioned against assuming that these adults would continue to be committed to continuous learning if such assistance were restricted to only those profiles not currently participating in learning. Indeed, many motivated adults indicated that they still face obstacles to learning, especially time-related obstacles. Therefore, as noted in the section on “extrinsically motivated” profiles, Flanders will need to strike a balance between universal approaches on the one hand, which can improve administrative efficiency and support the objective of access for all, and more targeted and tailored approaches on the other hand, which can help ensure that more public support is available to those most in need.

Despite these considerations, it would seem that no additional measures need to be targeted at these learners. However, insights from the segmentation could help to make existing interventions more tailored to their needs, thereby further encouraging and perhaps increasing their ongoing participation. For example, given that many still face time-related obstacles, the flexible course and programme offerings discussed in the context of “extrinsically motivated” profiles would also be relevant for “intrinsically motivated” profiles.

The findings of this study also have more general policy implications beyond specific adult learner profiles, and affect the practical application of the nine profiles in policy making. These implications could involve the use of the nine profiles to strengthen the evaluation and monitoring of policies, and to support the design and implementation of adult learning policies.

Few countries use impact evaluations in relation to adult learning. Studies assessing the causal impact of participation in adult education and training on individual outcomes are typically limited to training programmes provided in the context of active labour market policy (ALMP). To the extent that evaluations of adult learning do take place in Flanders, there remains room for improvement (see Box 3.5 for examples of monitoring and evaluation of Flemish adult learning initiatives). The OECD Skills Strategy Assessment and Recommendations report for Flanders (OECD, 2019[5]) concluded that the impact and effectiveness of adult learning policy measures should be assessed more systematically through monitoring and evaluation practices. Insights from learner profiles could help to strengthen existing evaluation practices by highlighting what groups are reached by existing initiatives, and how new initiatives can be better targeted and tailored to the motivations and obstacles of different adult learner profiles.

Current practices for ex ante policy evaluation in Flanders are unlikely to ensure that adult learning policies are targeted and tailored to the distinct motivations and obstacles of the nine profiles. While evidence on the state of policy evaluation in Flanders specifically is limited, at the federal level in Belgium, regulatory impact assessment (RIA) is mandatory for all primary and for some subordinate legislation submitted to the Cabinet of Ministers, and is usually shared with social partners for consultation. RIAs for subordinate regulations, however, are no longer published, and Belgium currently does not systematically require the identification and assessment of alternatives to the preferred policy option (OECD, 2022[27]). One recent assessment of evidence-based policy making found that while RIAs are compulsory, they are generally treated only as a formality (Bertelsmann Stiftung, 2020[28]). Furthermore, general RIA procedures and processes do not appear to require evidence that adult learning policies are targeted and tailored to the distinct motivations and obstacles of different groups of adults.

The profiles could be used to strengthen these ex ante policy evaluation practices in Flanders to help ensure that adult learning policies are appropriately targeted and tailored to the distinct needs of different learners at the design phase. Some stakeholders consulted in this project suggested that when policy makers design new adult learning measures they should always assess the likely impacts on the nine learner profiles identified in this project. This could be achieved by introducing new guidelines and requirements into Flanders’ existing procedures for ex ante policy evaluation. Formal RIA requirements could be expanded to require that departments designing adult learning policies and programmes explicitly evaluate expected impacts on the different adult learner profiles. The departments involved in adult learning could also co-develop guidelines on how to utilise adult learner profiles in adult learning policy design. For example, such guidelines could define how eligibility/exclusions, types and levels of service provision, rates of funding, and expected outcomes will differ for the nine adult learner profiles.

In addition, current practices for the ex post evaluation of adult learning programmes in Flanders could be strengthened to ensure that policies are targeted and tailored to the distinct motivations and obstacles of different profiles. Currently, evaluation seems to be ad hoc and focused on participation and satisfaction measures, rather than outcomes for specific target groups. One assessment of ex post evaluation at the national level concluded that while such evaluations exist, they are often undertaken on the initiative of individual line ministries and not given serious consideration by ministerial cabinets making policy decisions (Bertelsmann Stiftung, 2020[28]). The comprehensiveness of evaluations and depth of insights for specific target groups appear to differ across programmes and time, and some consulted stakeholders cited a general lack of evidenced-based evaluation and insight into policy impacts in Flanders’ adult learning system. The profiles could be used to strengthen ex post evaluation in Flanders to reveal the extent to which targeted and tailored adult learning policies improve learning motivation and participation for different learner profiles. Targeting and tailoring policies in Flanders to consider adult learner profiles will require more detailed programme evaluations that are supported by new guidelines and requirements, which could require that departments responsible for adult learning policies and programmes explicitly evaluate the outcomes of policies for the different adult learner profiles. These more comprehensive programme evaluations could reveal whether certain adult learner profiles are not making use of the incentives, or whether there are others in less need who are receiving the support.

To strengthen both ex ante and ex post evaluation, Flanders could consider insights about learner profiles from other studies, especially the “customer journeys” study (see Box 3.6). Given that the studies highlighted in this box complement each other, Flanders should compare the results. By reviewing the findings from both studies, policy makers can obtain an even more comprehensive view of the different types of learners, thereby providing further insight into how to target and tailor information and guidance services and learning incentives.

Insights from the learner profiles could also inform the design and implementation of information, guidance and incentives more broadly. This could involve considering how to use the profiles to improve the provision of information and guidance services and to support the design of the Flemish individual learning account (ILA). This section will also consider how additional data and tools could support operationalising the profiles for policy making and implementation.

The specific policy insights gleaned from the adult learner profiles, as described above, demonstrate that the nine profiles can support strengthening the provision of information and guidance services. To ensure that the nine profiles are used to support the provision of information and guidance, Flanders could consider taking a number of actions, described below.

To start, those providing information and guidance to adults about learning should be made aware of the adult learner profiles identified through this study, and their implications for policy. This will likely require relevant departments developing a communications strategy to guide awareness-raising efforts among providers. The implicated departments could agree on key messages and audiences for these communications to ensure that all key stakeholders are well informed on the rationale and implementation modalities for targeting and tailoring information and guidance services to the needs of different adult learner profiles. Once familiar with the adult learner profiles, providers of information and guidance could tailor their communications to the different profiles. It is promising that the Partnership for Lifelong Learning’s action plan mentions that the profiles developed in this study, along with those identified in the Flemish “customer journeys” study, should be used to inform a strategy (een gesegmenteerde mobiliseringsstrategie) proposed to engage hard-to-reach groups in learning (Partnership for Lifelong Learning, 2021[14]).

The nine profiles could also be used to target tailored information at different learner profiles using digital advertising tools. The introduction of digital advertising on social media platforms has changed what is possible with regards to targeting and tailoring information and guidance. Content can be personalised to each user based on criteria such as location, age, gender, interests, relationship status, languages spoken, education, company size, skills, job seniority and device used. The socio-demographic characteristics and labour market status of each of the nine learner profiles could be used to identify and reach these adults on social media. Higher education institutions already use targeted advertising to a certain degree to appeal to different profiles of students – for example, the needs of mature students and younger students differ substantially, and universities tailor their messages accordingly (Hemsley-Brown, 2017[30]).

The learner profiles could also be used to help make information on online portals more targeted and tailored to the needs of different learners. Information on adult learning could be more centralised, which would make it easier to target and tailor information currently spread across multiple platforms to the needs of different adult learner profiles (OECD, 2019[5]). Stakeholders consulted in this project noted that a lot of information is available, but highly fragmented across various portals, for example the training database Flemish Training Incentives (Opleidingsdatabank Vlaamse Opleidingsincentives), Education Chooser (www.onderwijskiezer.be), and more. Several stakeholders referred to the need for a single portal that provides a one-stop-shop for information on skills needs assessments, education and training programmes, available incentives, as well as information on training quality, costs, and more. The learner profiles could help make such a portal more targeted and tailored to the needs of different learners. For example, visitors could respond to questions on their motivations, obstacles and characteristics, and then be assigned one of the nine learner profiles with information and messages tailored to their needs. Screening questions are already being used on the Wizard Flemish training incentives (Wegwijzer Vlaamse Opleidingsincentives) website (Flemish Government, 2021[25]).

The nine profiles could also be used directly by learning and career guidance services. Counsellors could potentially use the profiles when meeting an individual to provide insight into which learner profile they are closest to, and to gain a general insight into other characteristics this person might possess. This could then be used to probe the applicant for more information and make the intervention potentially more efficient. The counsellor could also find out what sorts of interventions might work for someone with this profile, which could also be a starting point for a more in-depth assessment.

Since the latest reform of Flemish training incentives in 2019, there has been significant emphasis on the development of a learning and career account (Leer- en loopbaanrekening) in Flanders (Flemish Government, 2021[31]; Vlaamse Regering and SERV, 2017[32]; IDEA Consult, 2021[21]; Department for Work and Social Economy, 2022[24]) (see Box 3.7). This type of individual learning account (ILA) should to some degree be targeted and/or tailored to those most in need of support.

ILAs are a type of individual learning scheme (ILS) that seek to provide universal access to training for all groups of individuals. As a result, their success at increasing training among underrepresented groups specifically is mixed. Highly skilled individuals make more use of ILS than lower-skilled individuals who are in greater need of training. For this reason, countries with ILS typically still seek to offer relatively more support to those in greatest need of learning, such as the least skilled, individuals with low-income, and employees in small and medium-sized enterprises (OECD, 2019[33]). The 2019 OECD Skills Strategy Assessment and Recommendations report recommended providing more training rights to low-skilled than high-skilled workers, and that the learning account should be accompanied by programmes to reach out to vulnerable groups with information, advice and guidance (OECD, 2019[5]).

Insights from the learner profiles could support the design of an ILA that is targeted and tailored to those most in need of training support. The recently published Vision note: Towards a learning and career account in Flanders”(see Box 3.7) also indicated how insights from the OECD study, as well as the customer journey study (see Box 3.6), could help to gain even better insight into barriers and levers for stimulating training participation, and what role an ILA can play in this (Department for Work and Social Economy, 2022[24]). Moreover, the vision mentions specifically the role segmentation could play in communication and support to specific target groups. The various policy insights for the nine profiles, as presented in the first half of the chapter, could be taken into consideration. For example, the tailored messages for “unmotivated” profiles (see Table 3.2), “motivated, but facing obstacles” (see Table 3.3) and “extrinsically motivated” profiles (see Table 3.4) could be used to support targeted and tailored communication linked to the learning and career account. In addition, the profiles could also potentially be used for evaluating and monitoring the learning and career account by providing insights on the reach of instruments.

Discussions with stakeholders on the policy implications of the nine profiles often focused on two different types of applications: 1) using them to inform policies by providing broad insights that could help policy makers reflect on existing and new policies; and 2) using them more directly as a tool to assess what profiles adults most resemble, and, by extension, to decide which learning opportunities and policy measures could be most helpful. Most of the analysis in this chapter has focused on the first type of application, primarily due to questions raised about how to operationalise the profiles in practice for the second type of application. The following section explores how better data and tools could support the use of the profiles as tools to support the assessment and referral of adults to appropriate learning opportunities and support services.

Stakeholders consulted in this project noted that it could be difficult in practice to identify which profile an individual most closely resembles. The capacity to assign individuals to profiles in this way would be very important for enabling the profiles to be used to better target and tailor services offered by public service providers to the needs (including motivations and obstacles) of different adult learners. Operationalising the profiles in this way would require service providers to have sufficient background data on their clients, as well as a tool that can identify an individuals’ likely learner profile based upon this data.

Adult learning service providers in Flanders appear to be supportive of collecting and using client data in general, but data limitations prevent them from understanding adults’ learner profiles. For example, in the context of the GOAL educational guidance project, providers used registration systems that they deemed important for saving background information about the client and their previous guidance sessions and follow-up activities. However, an evaluation found that the registration system required a large investment of time, was not user-friendly and did not support the easy extraction of data. This made it difficult to analyse data at an organisational level for the purpose of improving guidance processes (El Yahyaoui et al., 2018[26]). Service providers’ data collections would need to be made more comprehensive and user friendly to support providers in understanding and adjusting services according to clients’ learning profiles.

While some data required for understanding adults’ learner profiles – such as demographic and labour market data – are already collected and held by public adult learning service providers, there are some data gaps. For example, public adult learning service providers such as Centres for Adult Education (Centra voor Volwassenonderwijs) and the Flemish public employment service (VDAB) often hold the demographic, labour market and education history of their clients; however, they typically lack data on clients’ motivations to train, obstacles to training, the adult learning activities clients have participated in and the guidance services they have received.

Furthermore, there is limited scope for these different agencies to share data with each other, even when serving the same individuals, which means that each agency has only a partial view of adults’ training motivations and obstacles. For example, an evaluation of the GOAL educational guidance project found that GOAL counsellors are very dependent on the information in their registration system as there is currently no exchange of data between their own registration system and other systems (Ministry of Education, VDAB, Integration Service, etc.) due to privacy regulations and the lack of structural embedment. The evaluation found that the development of a professional back office for service providers could have several benefits for service provision and policy making (El Yahyaoui et al., 2018[26]).

Ideally, public adult learning service providers would have a tool to translate the data held on an individual into an assessment of that adult’s likely learner profile. VDAB comes closest to having these analytical capacities, for example with its digital matching tool that translates information about qualifications and work experience in CVs and job advertisements into skills requirements for jobs. Of course, assessing likely learner profiles of individuals would only be beneficial when service providers do not already have a clear picture of their clients’ learning motivations, obstacles and needs. Some providers of adult learning services in Flanders do lack this clear picture currently, for example because there is no data on these characteristics or because client data are difficult and time consuming to collect, extract, analyse and share, as in the case of the GOAL educational guidance project (El Yahyaoui et al., 2018[26]).

The data collections of adult learning service providers could be expanded to target and tailor services to different adult learner profiles in two ways. First, individual providers could collect missing data directly from clients. This could be done at the point of registration for new clients, but may require more proactive outreach and information requests to existing clients. Additionally, those agencies holding relatively more data on clients, such as VDAB, could share this with other public service providers. This would be of value when two providers serve the same client, but would be subject to privacy constraints and require consent from clients. Second, a simplified tool could be developed based on the segmentation model that enables providers to assess adults’ likely learner profile based on readily observable characteristics (such as age, gender, educational attainment, labour market status and other variables included in the model (see Chapter 2).

Adult learner profiles should be updated over time to ensure that they continue to help Flanders target and tailor policies and services. The profiles should be identified based on the most comprehensive data available about adults’ learning motivations, obstacles, needs and socio-economic backgrounds. While the segmentation model developed for this study is based on the best available data today, it does come with limitations (see Chapter 2), such as the fact that it is static (i.e. based on data about adults at a specific point in time and does not capture adults’ movements between profiles).

The segmentation model is also limited in its inability to assess skills gaps (i.e. the difference between the skills required for jobs and the skills adults actually possess). The topic of skills gaps was a recurring theme in conversations with Flemish stakeholders, and while the segmentation model presents insights into the skills required for the jobs where adults in each of the nine profiles are typically employed, it lacks information on the skills that adults already possess. This is one of the main areas for potential follow up research (see discussion in “Potential next steps” section).

To address these various data and information challenges, Flanders could identify opportunities for policy makers to collect and share more data on adults’ motivations and reasons for (not) training, guidance received, and learning needs, for example through the Labour Market & Social Protection Datawarehouse (AM&SB Datawarehouse) or other sources. In the future, Flanders could consider re-running the segmentation model using data from the 2022-2023 AES or the Survey of Adult Skills (PIAAC) 2022-2023 to explore how adults’ proficiency levels in literacy, numeracy and problem solving interact with training participation, motivations and obstacles (see also “Potential next steps”).

There are a number of studies and initiatives in Flanders whose findings can complement the insights provided by the nine learner profiles. To gain a better understanding of how adult learning policies could be better targeted and tailored to the needs of learners, the findings of these studies should be examined in tandem with those of this report. One recent study on the customer journeys of adult learners in Flanders stands out in this regard (Van Cauwenberghe et al., 2021[29]) (see description in Box 3.6). Like the segmentation model, this study aims to provide insights into learning needs, motivations and obstacles to adult learning, with eight personas identified. By reviewing the findings from both studies, policy makers can obtain an even more comprehensive view of the different types of learners.

A next step for Flanders could be to use findings from the profiles and these other studies to inform a more in-depth evaluation of how current information and guidance services and learning incentives reach adults with different profiles. The outcomes of this evaluation could help to further refine the design of initiatives by ensuring that they better reach the groups most in need of support. In particular, insights from the profiles and other studies on learner motivations and obstacles could inform ongoing discussions on the design and implementation of the learning and career account (leer- en loopbaanrekening) in Flanders, as already mentioned in the recently approved Vision note: Towards a learning and career account in Flanders, as well as the suggested strategy (een gesegmenteerde mobiliseringsstrategie) to engage hard-to-reach groups, which was proposed in the action plan of the Flemish Partnership for Lifelong Learning (Department for Work and Social Economy, 2022[24]; Partnership for Lifelong Learning, 2021[14]).

One particular area that requires further analysis is the skills profiles associated with each learner profile. By better understanding the skills profile of each learner profile, and comparing these profiles against assessments of the skills demands of the labour market, policy makers can obtain a better understanding of what types of training to support for adults with different profiles. In addition, these skills gaps also provide relevant insights on how to support learners transition to high-demand occupations, thereby reducing skills shortages and improving the labour market outcomes of learners (OECD, 2021[12]). To this end, the analysis in this report could also be rerun using data from the Programme for the International Assessment of Adult Competencies (PIAAC), which has data for Flanders, with new data available in 2024. Given that PIAAC includes indicators comparable to those used in AES on the motivations to learn, obstacles to participation in learning and reasons for learning, it would be possible to replicate the segmentation model with more recent PIAAC data once available. Using PIAAC data would allow for the consideration of proficiency and the use of foundational skills (i.e. literacy, numeracy and problem-solving), and by assessing these indicators for the nine profiles, it would be possible to better understand how motivation to learn and obstacles to learning are associated with skills.

Relying more on administrative data for carrying out further analysis should be also considered, which not only will increase the accuracy of characterisation of adult learners and thereby improve the identification of learners profiles, but which could also facilitate the operationalisation of the profiles as tools for assessing and referring learners thereby supporting better targeted and customised public policies and adult learning services. Using administrative data will allow to assess, evaluate and adjust in real time the adult learner profiles. This will require an important effort to integrate information systems already available in Flanders (e.g. VDBA administrave information).

Finally, it could be beneficial to update the segmentation model periodically in response to changes in the data source(s) used and to the availability of new data sources. To validate the current results, conducting related analysis using similar sources of information may be helpful. For example, a new round of the Adult Education Survey will take place in 2022-2023. The analysis contained in this report could be rerun with this updated AES data to see if it yields different profiles. However, considerations on the comparability of the outcomes between AES 2016 and AES 2022-2023 need to be taken into account, as changes may be explained by sampling differences between the two rounds (Eurostat, 2022[34]; Widany et al., 2019[35]).


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