1. Summary and key findings for Flanders, Belgium
This chapter describes the outcomes of the OECD Skills Strategy Implementation Guidance project on The Faces of Learners in Flanders. In this project, the OECD developed a population segmentation that resulted in nine profiles of adult learners based on their shared motivations and obstacles to participate in adult learning. The identification of these representative types of adult learners could help to facilitate the development of policies that are better targeted and tailored to the needs of different learners. The chapter provides an overview of the objectives of the project and the characteristics of the nine profiles. It also summarises the main findings of Chapter 2: The nine adult learner profiles in Flanders, Belgium, and Chapter 3: Policy implications of the nine adult learner profiles in Flanders, Belgium.
The vital role of developing skills through lifelong learning in the world of tomorrow
Skills are central to the capacity of Flanders to thrive in an increasingly interconnected and rapidly changing world (see Box 1.1 for the OECD definition of skills). Countries in which people develop strong skills, learn throughout their lives, and use their skills fully and effectively are more productive and innovative, and enjoy higher levels of trust, better health outcomes and a higher quality of life (OECD, 2019[1]).
Megatrends such as globalisation, digitalisation, climate change and demographic change are reshaping work and society in Flanders and across the OECD. At the same time, COVID-19 (coronavirus) has been uniquely disruptive and will likely have a long-lasting impact on societies. In this context, skills are becoming increasingly important. Strong foundational skills will make people more adaptable and resilient to changing skills demands, and digital, transversal, social and emotional, and job-specific skills will become increasingly essential for adults to succeed in both work and life. The OECD Skills Strategy 2019 provides a framework to support countries in strengthening their skills systems to respond to these trends (see Box 1.2).
To cope with these changes, it will be essential for Flanders to guarantee a lifelong learning system that is resilient and effective (in this report “lifelong learning” is used interchangeably with the concept of “adult learning”, but by definition also covers initial education). High-quality and inclusive learning opportunities in formal, non-formal and informal settings should be accessible for everyone to enable full participation in society and to successfully manage transitions in the labour market. Adults will need more opportunities to upskill and reskill to adapt to structurally different labour markets, which will likely require an overall expansion of lifelong learning systems. Learning providers will need to create more flexible and blended forms of learning. Lifelong learning will also be vital in addressing learning losses resulting from disruptions to on-the-job learning due to widespread interruptions of economic activity during COVID-19 (OECD, 2021[2]).
The OECD identifies a broad range of skills that matter for economic and social outcomes, including: 1) foundational skills (e.g. literacy, numeracy and digital literacy); 2) transversal cognitive and meta-cognitive skills (e.g. critical thinking, complex problem solving, creative thinking and self-regulation); 3) social and emotional skills (e.g. responsibility, empathy and self-efficacy); and 4) professional, technical, and specialised knowledge and skills needed to meet the demands of specific occupations.
Source: OECD (2019[1]), OECD Skills Strategy 2019: Skills to Shape a Better Future, https://dx.doi.org/10.1787/9789264313835-en.
A project to strengthen lifelong learning policies by applying a segmentation approach
In 2019, the OECD Skills Strategy Flanders: Assessment and Recommendations report (see Box 1.2) (OECD, 2019[3]) produced a series of recommendations for improving the skills system in Flanders (see Annex A for the full list of recommendations). Lifelong learning was a main theme in this report, with three out of five priority areas investigating related issues such as developing a culture of lifelong learning, strengthening the governance of adult learning and improving the financing of lifelong learning. Recommendations provided direction to Flanders on how to increase participation in adult learning, raise awareness of the need to learn, improve accessibility of learning and improve the effectiveness of financial mechanisms, among other topics.
OECD Skills Strategy projects
OECD Skills Strategy projects provide a strategic and comprehensive approach to assess countries’ skills challenges and opportunities, and build more effective skills systems. The OECD works collaboratively with countries to develop policy responses tailored to each country’s specific skills challenges and needs. The foundation of this approach is the OECD Skills Strategy framework (OECD, 2019[1]), the components of which are: 1) developing relevant skills; 2) using skills effectively; and 3) strengthening the skills system. So far, 28 OECD Skills Strategy projects have been completed or are underway in 20 diverse countries.
Source: OECD (2019[1]), OECD Skills Strategy 2019: Skills to Shape a Better Future, https://dx.doi.org/10.1787/9789264313835-en.
The OECD Skills Strategy Flanders: Assessment and Recommendations project
Flanders participated in an OECD Skills Strategy project between 2018 and 2019 to assess its overall skills performance and receive recommendations for strengthening its skills system (OECD, 2019[3]). The final report provided Flanders with tailored findings and recommendations from an international perspective in five priority areas: 1) developing a learning culture; 2) reducing skills imbalances; 3) strengthening skills use in workplaces; 4) strengthening the governance of adult learning; and 5) improving the financing of adult learning (see Annex A for an overview of recommendations).
Two workshops were held in May and September 2018 to collect input from stakeholders to shape the recommendations. The workshops convened a wide range of stakeholders, including unions, employers, sectoral training providers, education institutions, academics and government representatives. In addition, several bilateral meetings with stakeholders and experts, as well as site visits, were organised for the project. The final report was launched in January 2019.
Source: OECD (2021[4]), Website OECD Skills Strategies, https://www.oecd.org/skills/oecd-skills-strategies.htm.
The successful development and implementation of many of these recommendations depends on Flanders’ ability to effectively target and tailor policies to different groups of adults, especially those most in need of education and training. In this implementation guidance project, the OECD has developed a segmentation model to support this process. The model has enabled the identification of representative or “idealised” types of adult learners (i.e. the nine profiles – see Figure 1.2) based on their combinations of motivations and the obstacles they face, as measured by the Adult Education Survey (AES) (see Box 1.3).
Note: See the Annex 2.A. in Chapter 2 for a full description of the underlying methodology.
Source: Adapted from Eurostat (2021[5]), Adult Education Survey 2016, https://ec.europa.eu/eurostat/web/microdata/adult-education-survey.
The nine adult learner profiles provide unique insights into the diversity of factors that impact decisions to participate. While well-known indicators on an adult’s willingness to learn and the obstacles they face have been explored in many reports, the added value of segmentation is that it shows with greater granularity the different types of learners by looking at the combinations of motivations and obstacles, and by providing insights into the different types of motivations of participating adults based on reasons to learn (e.g. extrinsic vs. intrinsic motivations). These profiles and their characteristics are examined in more detail in Chapter 2, as well as in the section “The nine adult learner profiles in Flanders” in this chapter.
The adult learner profiles could help Flanders reflect on how to better target and tailor existing and new lifelong learning policies to the needs of learners. For example, they could support the identification of groups of adults that skills policies have previously overlooked, demonstrate the need for additional or different policies to address motivations and obstacles of specific profiles, and support efforts to further refine existing target groups of lifelong learning policies. The policy implications of the nine learner profiles are discussed in Chapter 3, as well as in the section “Policy implications of the nine adult learner profiles in Flanders” in this chapter.
The Adult Education Survey (AES) covers adult participation in education and training (formal, non-formal and informal learning) and is one of the main data sources for European Union (EU) lifelong learning statistics. The survey covers the resident population aged 25-64. The reference period for participation in education and training is the 12 months prior to the interview. The latest survey was carried out in 2016, with the sample for Flanders covering 2 782 observations.
While other surveys also present information on participation, motivations to learn and obstacles to participation – including the OECD Survey of Adult Skills, a product of the Programme for the International Assessment of Adult Competencies (PIAAC) – the AES was selected as the basis for the segmentation developed in this project as it is specifically designed to provide insights on adult learning, it has a comprehensive set of indicators on learning activities and learner characteristics, and it took place comparatively recently.
Source: Eurostat (2021[5]), Adult Education Survey 2016, https://ec.europa.eu/eurostat/web/microdata/adult-education-survey.
To develop a segmentation of the adult population in Flanders, the OECD combined a data-driven approach (using Latent Class Analysis [LCA]) with extensive input from stakeholders drawn from across the Flemish Government and society. The project has been supported by a multi-disciplinary project team comprising 11 different organisations (see Annex B), which functioned as a steering committee for the project through active participation. The project facilitated stakeholder engagement through interactive workshops, group discussions and meetings with stakeholders and national experts (see Annex B). Over 100 representatives from different organisations participated in the project.
Lifelong learning is still not a reality for all adults in Flanders
Adults most in need of learning are not sufficiently participating in lifelong learning
It is well-documented in the OECD Skills Strategy Flanders: Assessment and Recommendations and various Flemish studies and strategic documents that Flanders has a comparatively low participation rate in adult education and training (OECD, 2021[2]; Van Langenhove et al., 2020[6]). According to the AES in 2016, 47.7% of adults in Flanders participated in formal and/or non-formal learning in the last 12 months, which is far below the share found in top-performing countries such as Switzerland (69.1%) and the Netherlands (64.1%) (Eurostat, 2021[5]). It is also still far below Flanders’ ambition of a 60% participation rate by 2030. When analysing participation in the last four weeks of 2020, according to the EU Labour Force Survey (LFS), Flanders only had a participation rate of 7.7%, which is below the average of 12.4% across OECD-EU countries (Eurostat, 2021[7]). Even when different data sources or measures of learning activity (e.g. informal learning, continued vocational training) are used, participation rates are generally below that of top-performing countries.
There are significant differences in participation across socio-demographic groups in Flanders, with the gap in participation between high- and low-skilled adults particularly striking. In all countries, low-skilled adults are significantly less likely than high-skilled adults to participate in adult education, but in Flanders the gap is especially large. Taking education levels as an indication for skill levels, only 21% of adults with less than upper secondary education participates in education or training, compared to 68% of adults with tertiary education (Figure 1.3) (Eurostat, 2021[5]). This gap of 47 percentage points between low- and high-educated individuals is also larger than the EU average of 42%. As a result, adult learning may be further reinforcing the skills gap between low- and high-skilled adults. During consultations, the “Matthew effect” was frequently mentioned by government representatives and stakeholders to describe the phenomenon in which the high-skilled can accumulate further advantages, while those who are low-skilled are left behind (Merton, 1968[8]). The Matthew effect highlights the importance of increasing efforts to ensure that those who are low-skilled, who have most to gain from adult learning, are actually participating (OECD, 2019[3]).
Source: Eurostat (2021[5]), Adult Education Survey 2016, https://ec.europa.eu/eurostat/web/microdata/adult-education-survey.
There is also a strong association between the skill requirements of jobs and of participation in learning. While 70% of adults in occupations requiring high levels of skills (e.g. professionals) are participating in education and training, this figure is only 32% for adults in occupations requiring low levels of skills (e.g. elementary occupations) (Eurostat, 2021[5]). This participation gap is particularly problematic because low-skilled jobs are more vulnerable to economic and societal shocks resulting from megatrends and COVID-19. For instance, adults in occupations with the highest risk of automation (e.g. service and sales workers, craft and related trades, plant and machine operators) have the lowest participation rates – 40% compared with 72% for adults in occupations with a very low risk of automation. Calculations are based on automation probability for occupations (2-digit ISCO08) by Nedelkoska and Quintini (2018[9]).
Age is also strongly associated with participation in learning. Older adults are much less likely than younger adults to participate in adult learning – 62% of 25-34 year-olds participate in adult education and training, compared to only 32% of 55-65 year-olds. In the context of an ageing population, it is increasingly important that adults of all age groups remain engaged. The engagement of older generations is also important because of the health and social benefits of remaining active in learning, and for their active participation in a more digitalised society.
Another important factor is migration status. Immigrants are less likely to participate in adult education and training than native-born Flemish adults – the participation rate of adults born in Belgium is 11 percentage points higher than for adults not born in Belgium. This is a particular concern as immigrants in Flanders are three times more likely to have very low literacy levels (level 1 or below in PIAAC) than natives (38% vs. 13%) (OECD, 2019[3]). However, when immigrants participate in non-formal education, they spend 60% more hours on the learning activity than native adults, which is likely the result of the intensive Dutch as a second language courses (Nederlands als Tweede Taal, NT2) that immigrants often enrol on. Dutch as a second language is, by a large margin, the most popular course in Centres for Adult Education (Centra voor Volwassenonderwijs) and Centres for Adult Basic Education (Ligo – Centra voor Basiseducatie) (Department of Education and Training, 2021[10]).
COVID-19 has deepened existing inequalities in lifelong learning
COVID-19 provided impetus to expand access to online learning (OECD, 2020[11]). Online and distance training was already expanding rapidly before the pandemic (e.g. with the rise of massive open online courses – MOOCs) (OECD, 2019[12]), but lockdowns due to COVID-19 helped to accelerate this expansion. A Flemish survey among education and training providers on e-learning in 2020 showed that 80% of providers either moved programmes online during the pandemic or developed new online programmes (Department for Work and Social Economy, 2020[13]). Many adults were using their time during lockdowns to participate in online and distance learning – for example, Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding (VDAB), the Flemish public employment service, noted an increase in participation in its free online courses, and LinkedIn found that the number of enterprise learners more than doubled between 2019 and 2020 (LinkedIn, 2021[14]). This expansion underscores the potential of online learning to become a more permanent feature of adult learning systems (OECD, 2020[11]).
This expansion of online learning has predominantly benefited more high-skilled adults. Recent LFS data show that for high-educated adults there was an increase in learning between 2019 and 2020 in Flanders (from 41% to 42.7%), while adults with low levels of education experienced a decrease in participation (from 18.4% to 17.7%) (Eurostat, 2021[7]). This finding is supported by various studies in Flanders. For example, a recent COVID-19 survey shows that around 27% of Flemish adults participated in some form of education or training during the COVID-19 pandemic, but that this figure disguises large disparities (see Figure 1.4) (Statistiek Vlaanderen, 2021[15]). Participation rates were only 4% and 16% for low- and mid-educated adults, respectively, compared with 32% for high-educated adults. The participation rate of the oldest age cohort (55-64) is half that of young adults (25-34) (15% vs. 30%, respectively). Within the Centres for Adult Education and Centres for Adult Basic Education, enrolment dropped 13% and 30%, respectively. Online learning has not worked well for many low-skilled and non-native speakers – the main target groups for these centres – and their programmes are not well-suited to adapt to online or blended formats. While some new groups were reached through online learning, these gains did not outweigh the losses in enrolment of existing target groups.
Note: Participation rate is covering both work–related and not work-related participation.
Source: Statistiek Vlaanderen (2021[15]), Results COVID-19 Survey: Work, Income and Lifelong Learning [Resultaten COVID-19-bevraging: Werk, Inkomen en Levenslang Leren], https://www.statistiekvlaanderen.be/nl/resultaten-covid-19-bevraging.
A lack of motivation to participate in learning is a significant challenge
The OECD Skills Strategy Flanders: Assessment and Recommendations report from 2019, as well as various other Flemish studies, all point to a lack of willingness to participate in learning (both formal and non-formal) as a main driver behind the low participation rate in Flanders (OECD, 2019[16]; Van Langenhove and Vansteenkiste, 2020[17]; Van Langenhove et al., 2020[6]). About 42% of the population does not participate and does not want to participate, which is far higher than the share in OECD countries with similar economic, social and educational outcomes (e.g. the Netherlands with 27%) (Eurostat, 2021[5]). For many of these adults, the lack of motivation is a result of not seeing a need to learn, and this is often particularly the case for adults who are most in need of upskilling and/or reskilling. For example, for adults in jobs with a significant or high risk of automation, the share reporting no need to learn is double that of adults in jobs with a very low risk of automation – calculations based on Nedelkoska and Quintini (2018[9]) and the AES (Eurostat, 2021[5]).
In discussions with stakeholders, strengthening motivations to participate was considered the most important challenge for Flanders’ lifelong learning system, and viewed as an issue underpinning many of the other challenges that Flanders faces. Participants in consultations indicated a general need to better understand the drivers behind motivation, especially for the most unmotivated groups.
Motivations to learn are arguably becoming even more important in the context of COVID-19. For instance, for the successful participation in online and blended learning, higher levels of autonomy and self-motivation are required (OECD, 2021[18]). This is again particularly relevant for the most vulnerable groups in Flanders, who generally exhibit a lower willingness to learn (Eurostat, 2021[5]). This low willingness, combined with higher obstacles to participation in online and blended learning (e.g. limited digital skills, no access to computer/internet) – which also partly drives the lower motivation to participate – risks widening existing participation gaps in Flanders when online and blended learning becomes a more integral part of lifelong learning. Low-skilled adults already have lower completion rates for online learning (as low as 10% in MOOCs) than high-skilled adults (OECD, 2019[1]).
When discussing the motivations of learners, it is important to consider the large heterogeneity in motivational profiles. Adults can have very diverse reasons for participating, or not participating, in learning activities, reflecting a variety of motivations. Studies that examine the motivations of learners often apply self-determination theory, which distinguishes three main profiles: intrinsically motivated (driven by inherent pleasure and satisfaction), extrinsically motivated (driven by external rewards or punishments) and unmotivated learners (not feeling a connection between their actions and expected results) (Deci and Ryan, 2000[19]). This theory has regularly been applied to compulsory education, but for adult education few studies differentiate between different motivational profiles. In consultations with Flemish stakeholders, the different types of motivation were a recurring topic of discussion. For example, stakeholders noted the relevance of developing intrinsic motivations to strengthen a culture of lifelong learning, which is also highlighted as an objective in several Flemish strategic documents, such as the 2020 “learning society" report (Conceptnota de lerende samenleving) (Tindemans and Dekocker, 2020[20]).
Finally, obstacles to learning need to be considered when evaluating motivational profiles, as they can directly affect motivation. Some obstacles (especially health and family responsibilities) are important drivers behind an overall low motivation to participate in adult education and training in Flanders. Obstacles are experienced by most adults in Flanders, even the most motivated adults who do manage to participate. Even though many adults manage to overcome these obstacles, 22-26% of the 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[5]).
Lifelong learning is vital for reducing skills gaps in a post-COVID world
Since the OECD Skills Strategy Flanders: Assessment and Recommendations report was published, COVID-19 has reshaped economies and societies. It has helped to accelerate existing megatrends and is expected to drive a broader shift in the economy – creating jobs in some sectors, while destroying jobs in others. These trends will have major implications for the types of skills required in the labour market, thereby creating potential skills gaps that lifelong learning could help to address.
The impact of COVID-19 on skills demand is already evident. Trends in the skills, knowledge and abilities identified in online job postings before and after COVID-19 show that there has been a major acceleration in the importance of certain abilities, such as the ability to adapt to change (Burning Glass Technologies, 2022[21]). In addition, various digital and soft skills have become much more important, including skills related to communication, collaboration and creativity (e.g. liaising, negotiating with other people, developing solutions to problem), as well as skills related to working with computers.
In a post-COVID world, people will also need stronger foundational skills, and to develop attitudes and mind-sets that will help them adjust positively to change and manage uncertainty (OECD, 2020[22]). Moreover, digital skills are fast becoming a prerequisite to actively participate in more complex, interconnected societies. However, 37% of Flemish adults had low basic digital skills in 2019 – as indicated by reporting that they had not used the Internet in recent months or that they have no or low digital skills (see Figure 1.5) (Statistiek Vlaanderen, 2020[23]). While this share is below the EU-average, it is far above that of the best-performing countries, and some groups in particular risk digital exclusion, including older generations, adults with lower levels of education and those on low incomes.
Source: Statistiek Vlaanderen (2020[23]), Digital skills of Flemish adults [Digitale vaardigheden bij burgers], https://www.statistiekvlaanderen.be/nl/digitale-vaardigheden-bij-burgers#sources.
A variety of specific skills are projected to become more relevant for the future Flemish labour market. Findings from Flemish projections at the sectoral level show that 11 types of skills will become increasingly important, including interdisciplinary teamwork, complex problem solving, use of digital tools, adaptability, innovation capabilities and taking responsibility (Departement for Work and Social Economy, 2021[24]). Projections of how the green transition will impact on skills needs suggest that in the future there will be greater need for technical knowledge, technical skills and soft skills (e.g. self-management, planning, communication) (Department for Work and Social Economy, 2021[25]). Flanders has several initiatives underway to map future skills needs at the macro-level, and has plans to integrate these efforts into a single skills projection model.
The role of targeted and tailored approaches in improving lifelong learning outcomes
Existing initiatives are not yet sufficiently reaching the groups most in need of learning
Based on the assessment of lifelong learning in Flanders, three overarching challenges can be identified: 1) raising participation of the groups most in need of learning (e.g. adults with low levels of education); 2) addressing an overall low motivation to participate; and 3) ensuring that adults develop the skills needed in current and future labour markets and societies.
Flanders is aware of these challenges and has introduced many initiatives to address them. For example, adults are provided with information on various portals, and career guidance is subsidised through career guidance vouchers (Loopbaancheques). Moreover, the Flemish Government has a range of measures, including Flemish training incentives comprised of Flemish education leave (Vlaams opleidingsverlof), training vouchers (Opleidingscheques) and Flemish training credit (Vlaams opleidingskrediet).
There are, however, indications that the impact of these lifelong learning initiatives could be enhanced as they are not yet effectively reaching the groups most in need of learning. For example, while training credit (opleidingskrediet) targets low- and mid-educated adults, 29% of training credit users in 2020/2021 had completed tertiary education, and adults who have not completed upper secondary education are most under-represented (Department for Work and Social Economy, 2021[26]). Moreover, consulted stakeholders often referred to an information gap, indicating that more vulnerable groups often have less access to information than other groups in Flanders. Data support this finding – 55% of adults with tertiary education received free information or help on learning possibilities, compared to only 19% of adults with less than upper secondary education received (Eurostat, 2021[5]).
Flanders could raise the impact of policies through better targeting and tailoring
Targeting and tailoring lifelong learning policies to specific cohorts can be an effective way to raise the impact of policies and address the challenge of raising the participation of groups most in need of learning. When policies are targeted and tailored to groups with specific profiles their contribution to raising the participation of these groups is likely higher than if a more universal approach is taken (OECD, 2020[27]).
In Flanders, lifelong learning initiatives are already targeted and tailored to some specific groups, including adults with low levels of education, unemployed adults, non-native speakers, prisoners, adults in rural areas, and small and medium-sized enterprises (SMEs). For example, for training vouchers (opleidingscheques), adults without upper secondary are exempted from a personal contribution (usually 50% of total costs) when they want to participate in specific types of training, and adults without tertiary education who want to study for a graduate or bachelor degree can receive additional funding (EUR 500 instead of EUR 250) (Department for Work and Social Economy, 2021[26]).
Despite these efforts, the participation rates of these groups have not improved in recent years. This could be explained by a variety of factors, including a focus on the costs of learning in existing initiatives, even though financial obstacles to participation are not considered among the most important in Flanders; current incentives could be too restrictive (e.g. eligibility criteria are too strict); some initiatives have been only recently implemented and their impact might not yet be felt; and the overall availability of funding for incentives has been decreasing (Department for Work and Social Economy, 2021[26]). In addition, the groups of adults targeted are often highly diverse. For example, those with low levels of education may also be early school leavers, long-term unemployed and/or older generations, all of which are characteristics associated with different obstacles to taking up learning opportunities (OECD, 2019[1]). Finally, current targeted and tailored initiatives do not consider the varying motivational profiles of adults, even though lack of willingness to participate is a main driver behind the low participation rate in Flanders.
Segmentation of the adult learning population could help to better target and tailor lifelong learning policies
Population segmentation facilitates the creation of more insightful profiles of potential learners, allowing policy makers to better understand how a constellation of factors influence the likelihood that adults will participate in learning. Separating the adult learning population into smaller segments can enable more effective policy design and implementation, and improve the allocation of resources.
Applying a population segmentation approach to adult learning allows Flanders to better understand how to develop lifelong learning policies, programmes and courses that are better targeted and tailored to the needs of adults with different profiles. The resulting representative types of learners are not be defined by their possession of a single characteristic (as for existing target groups of lifelong learning policies – e.g. education levels), but rather by their possession of multiple characteristics known to have an impact on people’s decisions to learn. Specifically, the segmentation approach allowed for the examination of the different motivations to learn, combinations of obstacles to learning, as well as the inter-relationship between these motivations and obstacles (e.g. age and health barriers could be causes of low motivation to learn).
Segmentation could help Flanders identify gaps in the current policy response for lifelong learning, including by helping to identify groups of adults that skills policies have previously overlooked, and by demonstrating the need for additional or different and more tailored policies to strengthen the motivations and overcome the obstacles of specific profiles. The nine profiles identified in the segmentation exercise need not replace existing target groups, but do provide new insights into how multiple factors combine to influence the decisions of people in these target groups to participate, which can support policy design, implementation and evaluation.
Some other OECD countries have already started using segmentation strategies to improve the development of lifelong learning policies. For example, in 2018 the Government of the United Kingdom commissioned research on adult learner profiles, which resulted in the development of an attitudinal typology with six types of learners identified (Kantar Public and Learning and Work Institute, 2018[28]). Nonetheless, the implementation of segmentation strategies to improve lifelong learning is still relatively new among OECD countries, meaning that with this OECD Skills Strategy project, Flanders is at the forefront of developments using these methods. Developing segmentation based on the characteristics of adult learners could also be considered a good first step towards more tailored interventions directed at individuals, as well as more targeted approaches directed at employers.
Policy context
Flanders has a long history of developing lifelong learning strategies and policies to confront the challenges and seize the opportunities inherent in societal and economic changes. Accordingly, Flanders has already taken several steps to address many of the lifelong challenges identified in this chapter.
Since 2019, the Flemish Government has taken important steps to improve the adult learning system. The government approved the concept note “the learning society" (Conceptnota de lerende samenleving) to promote a culture of lifelong learning (Tindemans and Dekocker, 2020[20]). The concept note was based on extensive consultations and identified ten assignments for lifelong learning, with one (“a data driven segmentation to identify skills needs and to ensure that resources for learning can be better targeted at these specific needs”) providing a starting point for discussions for this project on the segmentation of the adult learning population. The paper also marked the beginning of the establishment of a Partnership for Lifelong Learning (Partnerschap Levenslang Leren), which is also mentioned in the Flemish Coalition Agreement 2019-2024. The partnership aims to strengthen synergies and co-operation on lifelong learning in the Flemish Government, and to further develop the ambitions and objectives through a clear vision, which is reflected in an action plan (Partnership for Lifelong Learning, 2021[29]) (see Box 1.4).
The concept note, “the learning society" (Conceptnota de lerende samenleving), described four main challenges and ten assignments to ensure that people can deal with transitions proactively and become more intrinsically motivated. Several organisations were asked to provide their views on the document, including the Social and Economic Council of Flanders (Sociaal-Economische Raad van Vlaanderen – SERV), the Flemish Education Council (Vlaamse Onderwijsraad – VLOR), the Flemish Advisory Council for Innovation and Enterprise (Vlaamse Adviesraad voor Innoveren & Ondernemen – VARIO), and the Economic Restart Committee (Economische Relance-comité). Based on advice by these bodies, the Partnership for Lifelong Learning was established. Chaired by Ans De Vos from the Antwerp Management School, the Partnership has 18 members representing SERV, the education landscape and education providers, private learning providers, sectoral funds, Syntra, the local level, business schools, and the VLOR Commission Lifelong Learning.
In December 2021, an action plan developed by the Partnership for Lifelong Learning, Set a course for a learning Flanders (Actieplan levenslang leren: koers zetten naar een lerend Vlaanderen), was approved by the Flemish Government. This action plan formulates the partnership's ambitions and actions to make Flanders a learning society, and describes the functioning of the partnership and the implementation of the action plan. The partnership has listed ten ambitions with more than 16 actions. It has also prioritised seven flagship directions with a set of actions. These flagship directions indicate a move to: 1) a knowledge agenda for lifelong learning; 2) a strategy to mobilise the segments of the adult learning population; 3) an assessment of personalised approaches to lifelong learning incentives; 4) one Flemish strategic competency programme; 5) guidance and support to elaborate flanking policies; 6) guidance and support of providers of education and training; and 7) strengthening futureproof partnerships for learning.
Source: Tindemans and Dekocker (2020[20]), The Learning Society, https://www.oecd.org/skills/centre-for-skills/The_Learning_Society.pdf.; Partnership for Lifelong Learning (2021[29]), Action plan lifelong learning: Set a course for a learning Flanders [Actieplan levenslang leren: koers zetten naar een lerend Vlaanderen], https://www.vlaanderen.be/publicaties/actieplan-levenslang-leren-koers-zetten-naar-een-lerend-vlaanderen.
Other recent strategies aim to support the recovery of education systems and labour markets following COVID-19, with lifelong learning often a central topic. In 2020, the Flemish Government and social partners concluded a VESOC agreement (Flemish Economic and Social Consultative Committee [Vlaams Economisch Sociaal Overlegcomité]) on the recovery of the Flemish labour market (Flemish Government and Social Partners, 2020[30]). The resulting plan, “All-hands-on-deck” (Alle hens aan dek), allocates EUR 190 million to support the development of a strong, sustainable recovery and the transformation of the labour market, with EUR 120 million reserved for a training offensive – the largest pillar of the plan. Flanders also launched a “restart-plan” (“Flemish resilience” [Vlaamse veerkracht]) in 2020, with one of its seven ambitions to strengthen human capital by investing in training and lifelong learning (Flemish Government, 2020[31]). Several plans were launched to support this ambition, including Edusprong (for adult education), Voorsprongfonds (for higher education) and Digisprong (to support digitalisation). EUR 50 million has been made available for hubs providing guidance and access to digital tools and to strengthen digital skills (Digibanken). In 2020, the Flemish Minister for Work also requested that six labour market experts draw up expert advice for the recovery of the Flemish labour market during and after COVID-19 (Department for Work and Social Economy, 2021[32]). Two reports have been published so far, containing a comprehensive set of recommendations for strengthening the labour market and emphasising the role of lifelong learning and skills. The latest report from October 2021 presented an assessment of how a learning and career strategy can help to better align what people know and can do with what employers are looking for (De Vos et al., 2021[33]).
The Flemish Government has also launched several tailored plans on more specific topics in recent years. In the Flemish Coalition Agreement for 2019-2024, the government committed to work towards the development of individual learning accounts (ILA) to make existing training incentives simpler, clearer and more transferable (Flemish Government, 2019[34]). To launch work on the development of the ILA, the Flemish Government approved Vision note: Towards a learning and career account in Flanders in 2022, which identifies the different steps of the development of this learning and career account (leer- en loopbaanrekening) (Department for Work and Social Economy, 2022[35]). In addition, the Flemish Government has committed to extend dual learning to higher education and adult education – the related decree has been approved and will be implemented in September 2022 – and several strategic policy papers have considered the learning of specific groups of adults, including prisoners, internees and the elderly (Flemish Government, 2020[36]; 2020[37]; 2020[38]).
Since 2019, several major studies have been published on lifelong learning. The Centre of Expertise for Labour Market Monitoring (CELM) (Steunpunt Werk), in collaboration with the Department for Work and Social Economy (WSE), publishes a yearly monitor of training efforts (Van Langenhove et al., 2020[6]; Penders et al., 2021[39]). WSE also recently published a report on the customer journeys of adult learners (Van Cauwenberghe et al., 2021[40]), as well as annual reports on Flemish training incentives (Jaarrapport Vlaamse opleidingsincentives) (Department for Work and Social Economy, 2021[26]). A VIONA-study by IDEA Consult for the Flemish Government reviewed the road towards the Flemish learning and career account (IDEA Consult, 2021[41]), and SERV has published several relevant reports on lifelong learning, including one on the possible benefits of an individual learning account in Flanders and advice on how to make learning in higher education more attractive during people’s careers (SERV, 2020[42]; 2021[43]).
The Flemish Government recently reformed the funding arrangements of the adult education sector. A new financing decree for adult education (Financieringsdecreet volwassenenonderwijs) aims to provide a more stable and predictable financing system for institutions (Flemish Government, 2019[44]). The decree includes a “qualification bonus” for centres when participants finish a certain programme; more funding to increase the supply of courses, especially for basic skills courses; a cap on the number of seats in fields of study other than Dutch as a second language and basic skills; and more financial support for vulnerable groups. A larger share of funding is allocated to certain training programmes, including training for bottleneck professions and training in areas of social importance (e.g. the Dutch language), partly at the expense of funding for other training programmes (e.g. foreign languages). Centres for Adult Education also receive a larger share of funding, which can be considered an incentive to maintain learning provision in more rural areas. However, despite the new financing decree, there are still concerns about whether funding for adult education is stable and adequate; for example, in 2020 an educational bridging loan was required to address a budget shortfall.
In 2019, the three reformed Flemish training incentives were introduced: educational leave, training vouchers and training credit. In addition to giving these incentives a more labour market and future-oriented focus, the reforms introduced a single recognised training database, uniform monitoring and evaluation, and more. One of the main points of the reform was to target specific groups (Flemish Government, 2021[45]). Since 2019, another decree has transferred responsibility for higher vocational education (Graduaatsopleidingen, HBO5) – including, for example, informatics and orthopaedics – from Centres for Adult Education to university colleges (Flemish Government, 2019[46]). Moreover, while still in the process of being implemented, a decree from 2019 proposed a more integrated policy for the recognition of prior learning (erkenning van Verworven Competenties – EVC), which includes more means for the validation of these skills acquired through non-formal and informal learning (Flemish Government, 2019[47]). In new sectoral agreements (Sectorconvenants), sectoral social partners agreed to engage in lifelong learning, in addition to improving connections between education and labour markets, and promoting diversity (Department for Work and Social Economy, 2021[48]).
The OECD has developed a model to segment the adult learning population based on adults’ motivations to participate or not participate in learning, and on the obstacles that adults face to participation. The OECD’s segmentation approach employs an LCA methodology to identify sub-groups in the adult population with similar profiles using data from the AES (see Box 1.5) for a high-level description of the methodology; for a full description see Annex 2.A. of Chapter 2) (Eurostat, 2021[5]).
Latent Class Analysis (LCA)
LCA is a statistical method for identifying population subgroups based on multivariate categorical data. Similar to other clustering methods, LCA identifies mutually exclusive and exhaustive latent (or unobserved) classes based on patterns in observed data. LCA estimates class membership probabilities and uses iterative numerical methods to find the model that best fits the data (based on a statistical criteria). LCA is extensively used in several applications, such as to classify patterns of behaviour or attitudes, identify consumer preferences, and examine subpopulations based on their responses to survey or test items.
The segmentation models based on motivations and obstacles
For the segmentation of the Flemish population based on the AES 2016, the LCA estimates two different baseline models for two different groups of adults: 1) adults not participating in non-formal or formal learning activities; and 2) adults participating in non-formal or formal learning activities. The reason for this is that the most effective policies for these two groups are considerably different, with likely different policy objectives (e.g. for non-participating adults, policies will mainly aim to ensure that adults participate, while for participating adults policies will mainly aim to ensure that adults participate more or in more relevant learning activities). These models include the indicators that best describe the main drivers behind not participating (i.e. a lack of motivation and obstacles to participation) and participating (i.e. the different reasons for participating and motivations to participate more).
For the first group of profiles (non-participating), the model includes indicators on both motivation and obstacles to participation (see first column in Table 1.1). In the AES, all adults who did not participate in learning activities were asked whether they would have liked to participate, thereby indicating their motivation. If they did not want to participate, they were asked if it is because they do not see a need for learning. Regardless of whether adults want to participate, they were also asked about the obstacles that they face. For some cases, the obstacles are grouped to increase the statistical representativeness of the sample (e.g. time related obstacles include variables on schedule constraints and family responsibilities).
For the second group of profiles (participating), the model relies primarily on indicators of their reasons for participating in learning, including both job-related and not job-related factors (see second column in Table 1.1). These indicators provide insights into their attitudes towards learning, which could be linked to different types of motivational profiles (e.g. extrinsic and intrinsic motivations to learn). In addition, to have a more comprehensive view of these motivational profiles, indicators in AES of the willingness to participate more – i.e. in addition to the learning activities they already participate in – are included (see Table 1.1), thereby providing insights into motivations ex post the learning activity (in contrast to the ex ante reasons to learn).
Covariates for identifying additional characteristics
After having identified the nine adult learner profiles through estimating two baseline models, additional variables have been included in the model to identify the associated characteristics of the profiles. Following a three-step approach, more detailed information on the nine profiles were examined, including socio-demographic characteristics (e.g. level of education, age, income), labour market characteristics (e.g. occupation, labour market status), skills requirements of their occupations, and learning patterns and outcomes (see Chapter 2 for a more detailed description).
The resulting nine adult learner profiles in Flanders (as presented in Figure 1.2) are comprised of four profiles of adults currently not participating in non-formal or formal learning activities (referred to as “non-participating” adults), and five profiles of adults participating in non-formal or formal learning activities (referred to as “participating” adults). These profiles provide unique insights into the shared motivations and obstacles of those not yet participating and those participating. The non-participating group can be further broken down into two sub-groups, specifically those “unmotivated” and those “motivated but facing obstacles”. The participating group can be further broken down into those “extrinsically motivated” and those “intrinsically motivated”.
After identifying the nine profiles based on their motivations and obstacles, additional quantitative analysis was undertaken to identify which characteristics (e.g. socio-demographic, labour market status, skills required, learning outcomes and patterns) are most associated with each of the nine profiles. This has involved using other data sources, such as Burning Glass job postings data. The characteristics of each of the nine profiles are described in the box below.
It should be noted that the descriptions of the profiles are based on generalisations derived from a long list of characteristics – the LCA method does not allocate individuals and characteristics to profiles in a deterministic way, but instead presents probabilities. As a result, while specific characteristics may have strong associations with the specific profiles (and indeed all presented results are statistically significant), they generally are not linked in a 1-to-1 relationship.
Adults not participating in non-formal or formal learning activities
A. Unmotivated
1. Disengaged from learning: These adults are unmotivated to participate in learning and do not see a need to participate.
This profile is characterised by having the lowest education levels of all profiles (41% educated below the upper secondary level), the largest share of adults not in employment (13% unemployed and 34% inactive) and the lowest average income. These characteristics partly explain the low motivation of the profile (e.g. socio-economic challenges, such as poverty and inadequate housing may mean that learning is a lower priority). Adults with this profile are comparatively old, and non-native speakers represent a significant minority. Working adults with this profile tend to be employed in jobs requiring low- to mid-level skills, have a high likelihood of working in manufacturing, and tend to be in jobs facing a high risk of automation. Despite not participating in non-formal or formal learning, 61% participates in informal learning, such as learning on-the-job or visiting learning centres (e.g. libraries). Profile 1 represents 19% of the adult population in Flanders.
2. Unmotivated due to age and health obstacles: This profile consists of adults who are unmotivated to learn and perceive no need to participate in learning opportunities. However, this low motivation is largely the result of the age and health related obstacles they face (e.g. adults feeling too old to learn new things).
Adults with this profile have the highest average age (42% of individuals aged between 55 and 65), education levels that are comparatively low, and a high likelihood of inactivity due to early retirement and/or disability (together representing 25% of adults with this profile). Compared to Profile 1, adults with Profile 2 are more likely to be employed and work predominantly in small businesses in jobs requiring low or medium levels of skills. In addition, adults with this profile are more likely than others to be working in a job at high risk of automation. 50% of adults participates in informal learning, which is one of the lowest shares of all profiles. Profile 2 represents 18% of the adult population in Flanders.
B. Motivated, but facing obstacles
3. Motivated but facing time-related obstacles: The majority of adults with this profile are motivated to participate in learning, but do not have enough time due to either a busy schedule (37% of adults), family responsibilities (29% of adults), or both.
This profile is characterised by having the largest shares of both adults in a relationship with children (69%) and single parents (9%). Moreover, non-native speakers represent a significant minority for this profile (22%). Among non-participating profiles, this is the youngest (59% of adults are below 45 years of age), the highest educated (41% has a tertiary degree) and has the highest proportion of females (55%). Almost 80% of adults with this profile are working, with most employed in full time jobs. Some 67% of adults participates in informal learning, the highest share among non-participating profiles. Profile 3 represents 6% of the adult population in Flanders.
4. Motivated but facing multiple obstacles: Adults with this profile are motivated to engage in learning but face a range of obstacles, including high cost, the absence of suitable learning offers, and health and age related obstacles.
Adults with this profile are relatively old, but not as old as Profile 2, and have relatively low levels of education, but higher than adults in Profile 1. The income of adults with this profile is comparatively low, partly because a comparatively large share are inactive. Looking at different job characteristics, there is a large share of adults in medium-skilled occupations (57%), with jobs in small businesses (55%) and/or with jobs that tend to have a very high risk of automation (63% – the highest share of all profiles). This profile stands out as having the lowest share of adults participating in informal learning (45%). Profile 4 represents 9% of the adult population in Flanders.
Adults participating in non-formal or formal learning activities
C. Extrinsically motivated
5. Reluctant but required to participate: These adults are participating in learning, but only because they are required to do so by the employer or by law.
This is a very young profile (47% of adults under 35). While it is one the lowest educated participating profiles (only 49% of adults completed tertiary education), the profile is still more highly educated than any non-participating profile. This is also the profile with the second highest proportion of unemployed adults (9%). Working adults with this profile are typically employed in jobs requiring mid- to high-level skills, and these jobs face a relatively high risk of automation compared to those held by other participating profiles. While Profile 5 stands out with a comparatively low to medium intensity of learning (as measured by the number of hours in learning), a relatively large share participates in informal learning (76%). Profile 5 represents 16% of the adult population in Flanders.
6. Participating in response to work pressures: The majority of adults with this profile are extrinsically motivated learners who are participating in learning to adapt to organisational or technical changes in the workplace, or are participating to perform better in their current job.
Compared to Profile 5, adults with this profile are older and more often employed, and their jobs have a lower risk of automation. Non-formal learning is often provided by the employer, and the participation rate in informal learning (69%) is the lowest among the participating profiles. Profile 6 represents 17% of the adult population in Flanders.
7. Participating to strengthen career prospects: Adults with this profile are participating to improve their career prospects, to improve their professional opportunities by gaining formal certification, or to perform their jobs better. Their motivation to learn could be characterised as “identified regulation”, which is a type of extrinsic motivation characterised by the ambition to attain a personally valuable goal. This type of motivation is more self-determined and personal than the extrinsic motivations of adults with Profile 5 and 6, and not far removed from intrinsic motivation.
Looking at their socio-demographic characteristics, adults in this profile are comparatively often female (63%), highly educated (62%) and/or very young (47% of adults are under 35 years of age). When analysing labour characteristics, many adults with this profile are employed in jobs requiring high levels of skills (66%) and/or are typically employed in medium-large enterprises (63%). This profile also has the largest proportion of part time workers (22%). Learning by adults with this profile is characterised by a comparatively high intensity (i.e. learning for many hours), as well as by participation in informal learning (87%). Profile 7 represents 5% of the adult population in Flanders.
D. Intrinsically motivated
8. Participating for personal development: Adults with this profile are intrinsically motivated and participating in learning for non-work related reasons, such as to gain knowledge/skills that are useful for everyday life or to explore their personal interests and passions.
This profile has the highest share of adults employed in occupations requiring high levels of skills (74%). They are found in professional occupations, with a large share employed in health and social work and education. Working adults with this profile have the lowest risk of automation of any profile. Adults are typically highly educated and have comparatively high household incomes. Most adults with this profile participate in informal learning (84%). Profile 8 is the smallest profile, representing 3% of the adult population in Flanders.
9. Participating for professional and personal development: As with Profile 8, these are intrinsically motivated adults who want to participate in learning. Unlike Profile 8, their primary motivation for learning is to achieve work related objectives. For example, many adults with this profile participate to perform better in their current job or to improve career prospects.
Looking at their socio-demographic characteristics, this profile has adults who are highly educated, work predominantly in high-skilled occupations (often managerial positions), and/or are generally employed in medium- to large-sized firms. This profile has the highest household income of all profiles. Adults with this profile have the longest tenure and the majority train with the support of their employers. A large share (82%) also participates in informal learning. Profile 9 represents 7% of the adult population in Flanders.
The insights generated from these profiles can help make policy makers and stakeholders more aware of the different motivations of learners and the different obstacles they face. Furthermore, a 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.
Specific policy insights for each adult learner profile
Insights from the nine profiles can help Flanders to 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 nine underlying learner profiles. An overview of the key insights is presented in Figure 1.6.
Key insights: “Unmotivated” profiles
The “unmotivated” profiles represent the least motivated learners, and include Profile 1: “Disengaged from learning”; and Profile 2: “Unmotivated due to age and health obstacles”. Together, these unmotivated adults represent 37% of the adult population. These profiles are still far from being lifelong learners. Profile 1: “Disengaged from learning” is a particular concern since they are not learning, and either are not working or are at risk of losing their jobs. For adults with Profile 2: “Unmotivated due to age and health obstacles” it will be even more difficult to convince them of the merits of becoming lifelong learners.
Adults with these profiles are arguably most in need of both upskilling and reskilling opportunities, and a broad package of policies is needed to raise their participation, including information and guidance services, accessible learning opportunities, and incentives to participate in learning. The following are key insights for policy making that have emerged from the analysis of the unmotivated profiles:
Existing information and guidance services often do not respond effectively to the diverse and multiple needs of “unmotivated” adults. High-quality learning information and guidance services are particularly important for engaging “unmotivated” adults. However, the current offer of information and guidance is not yet effectively reaching these adults. A more targeted approach is needed that considers the diversity of these two profiles. For example, Profile 1: “Disengaged from learning” should receive information and guidance that considers the socio-economic challenges stemming from high-levels of unemployment and inactivity, while Profile 2: “Unmotivated due to age and health obstacles” should receive information and guidance tailored to a context of early, or close to, retirement, or having permanent disabilities. The segmentation demonstrates that the existing broadly defined target groups (e.g. adults with low levels of education) are quite diverse in terms of motivations, obstacles and characteristics, and that encouraging their participation in learning will require a package of incentives and support measures that responds to these diverse and multiple needs.
Active outreach by stakeholders in the most direct contact with “unmotivated” adults could help to increase their participation in learning. The take up of in-person information and guidance services by “unmotivated” profiles could be increased by encouraging the greater use of existing support measures, such as career guidance vouchers (loopbaancheques). Stakeholders who are in the closest contact with these profiles could also play a more important role in raising awareness of the importance and availability of learning incentives and support services. For example, local organisations and charities that work closely with vulnerable groups could play a part in promoting learning among adults with Profile 1: “Disengaged from learning”. 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. For employed “unmotivated” adults who are working, the involvement of employers, trade unions and sectoral organisation is vital for promoting and facilitating their participation in learning. Small businesses in particular could play an important role as they are comparatively often the employers of workers with “unmotivated” profiles, especially as on-the-job learning will be particularly important for these adults. Finally, one-stop learning resources, such as “learning Shops” (leerwinkels), could be expanded to provide relevant advice to “unmotivated” adults.
Insights into the motivations and obstacles faced by “unmotivated” adults can be used to better tailor information and guidance services. For the two “unmotivated” profiles, these tailored messages should be substantially different. To encourage and support participation by Profile 1: “Disengaged from learning”, it would be important that information is provided about the benefits of learning, as well as what incentives, support services and learning opportunities are available and how they might be accessed. For Profile 2: “Unmotivated due to age and health obstacles”, it would be particularly important that information is made available on the benefits of learning and opportunities to learn for older learners or adults with health problems or disabilities. In addition, 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. Information targeted at “unmotivated” profiles could also highlight the availability of upskilling and reskilling opportunities that can help them transition to sectors and jobs with a lower risk of automation, as well as the availability of career guidance vouchers.
Information and guidance alone is insufficient to boost learning among “unmotivated” adults – they also require access to relevant adult learning opportunities and incentives to learn. While information and guidance services are arguably the most important policy levers for promoting learning among “unmotivated” profiles, access to relevant learning opportunities and incentives to learn are also important. There are already large numbers of learning providers and incentives that specifically target those “unmotivated” to learn, especially for Profile 1: “Disengaged from learning”. These include registration fee exemptions and targeted courses in adult education centres. The profiles can also provide valuable input into how to improve the design of programmes, including the type of courses to offer to different profiles of learners and their content. For example, employed “unmotivated” adults are often found in occupations related to manufacturing and engineering, which could provide an indication of the sorts of skills they might need. However, many of these adults work in occupations facing a high risk of automation, and potential pathways between occupations in decline and those experiencing growth should be identified, as should the skills that need to be developed to facilitate these transitions. Stakeholders indicated that current adult learning incentives in Flanders do not always effectively support these broader transitions.
Key insights: “Motivated, but facing obstacles” profiles
The “motivated but facing obstacles” profiles represent those willing to learn but impeded from doing so. They include Profile 3: “Motivated but facing time-related obstacles” and Profile 4: “Motivated but facing multiple obstacles”. Together, these two profiles represent 15% of the adult population in Flanders. Since these profiles are already motivated to learn, if their barriers could be overcome, they could more easily become lifelong learners than the “unmotivated” learners.
Both financial and non-financial incentives are considered important policy levers for boosting the participation of these profiles. Incentives can both raise extrinsic motivations to learn and respond to specific obstacles faced by adults and employers. The following are key insights for policy making that emerged from the analysis of “motivated, but facing obstacles” profiles:
Raising awareness of training leave and encouraging firms to provide more on-the-job learning opportunities would help to raise participation in learning of adults facing time-related obstacles. Time-related obstacles, such as being too busy at work and family responsibilities, are the most frequently mentioned obstacles to participation in learning in Flanders, and are the defining characteristic of Profile 3: “Motivated but facing time-related obstacles”. Flanders could increase take up of existing initiatives designed to overcome time-related obstacles by more actively reaching out to adults with this profile, who are comparatively often young adults with children, to inform them about available incentives and support measures. In addition, given that time-related obstacles are an important 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, while considering how to offset the additional burden this may place on employers. Furthermore, Flanders could encourage more on-the-job learning and other initiatives that combine learning and work (e.g. dual learning), improve access to training with flexible formats (e.g. part-time, online) and designs (modular, credit-based courses), as well as provide affordable and accessible childcare.
Financial incentives for individuals and employers could be better designed to encourage the participation in learning of adults facing a lack of support and cost obstacles. Despite the wide range of financial incentives directed at both individuals and employers, many adults still report cost and a lack of support (by both employers and public services) as their main reason for not participating in learning, especially in Profile 4: “Motivated but facing multiple obstacles”. This highlights the potential to improve the design of these incentives. Flanders could encourage greater take up of existing financial incentives by individuals for whom cost is an important consideration, such as those with low levels of income. This could entail reducing the amount of required personal contribution to training vouchers (which is currently 50% of the total voucher). In addition, Flanders could encourage small businesses, where workers facing cost-related obstacles are highly concentrated, to increase their provision of on-the-job and other sorts of training, with financial incentives targeting SMEs.
Incentives should be complemented with tailored and targeted information and guidance on the availability of incentives and learning opportunities. 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 these adults. However, these policies need be complemented with information and guidance to raise awareness of available learning opportunities, incentives and support measures to facilitate access. This information and guidance should be tailored to the unique needs and characteristics of adults. For example, to encourage the participation of Profile 3: “Motivated but facing time-related obstacles”, Flanders should raise awareness of training leave, flexible course offerings (e.g. part-time, modular, online courses) and other initiatives designed to overcome time-related obstacles. Similarly, to encourage the participation of Profile 4: “Motivated but facing multiple obstacles”, Flanders should raise awareness of the wide range of financial incentives currently available and how they might be accessed.
Key insights: “Extrinsically motivated” profiles
The “extrinsically motivated” profiles represent those already learning and whose participation in learning is driven by external factors, such as being required to learn by an employer or by law, the need to adapt to organisational or technical changes in the workplace, etc. This category of 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 adult population in Flanders.
Since these profiles are young and, therefore, will be in the workforce for many years to come, they are important profiles to consider in policy-making. In particular Profile 5: “Reluctant but required to participate” is an important target for policy as they lack commitment to further learning. Profile 6: “Participating in response to work pressures” underscores that new technologies and other workplace innovations that demand the greater use of skills or, indeed, new skills, play an important role in encouraging lifelong learning. Adults with Profile 7: “Participating to strengthen career prospects” exhibit a stronger commitment to learning and can be considered borderline with respect to whether their motivation is extrinsic or intrinsic.
Many with these profiles are already benefitting from existing incentives and from information and guidance services. Flanders needs to ensure that in targeting more resources at encouraging and supporting learning by those not already participating (e.g. “unmotivated” and “motivated, but facing obstacles” profiles), it does not reduce incentives for participation by those already learning. The following are key insights for policy making that emerged from the analysis of “extrinsically motivated” profiles:
The motivation to continue learning can be boosted through the provision of high-quality and relevant learning opportunities. The fact that participation by extrinsically motivated adults is motivated by external factors makes their participation more vulnerable to changing circumstances. To strengthen their commitment to learning, Flanders could aim to improve the learning experience. First and foremost, this entails ensuring access to a high-quality and relevant education and training offer. Ensuring that course offerings and programmes are responsive to the specific needs of different types of learners could also help to improve learning experiences. 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. In addition, making learning opportunities available in a wide range of contexts, but especially in the workplace where people apply their skills, should increase motivation to learn, support the successful acquisition of knowledge and skills, and, by extension, strengthen commitment to continuous learning.
Information and guidance is important to build a culture of lifelong learning, even among those who already have a strong motivation to learn. 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 redirected to adults not yet participating. 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 – i.e. the financing of learning activities that would have occurred even without public support. One potential way to balance these pressures is to appeal further to extrinsic and intrinsic motivations to learn. This would entail the development of information and guidance services that emphasise how adults benefit from continued participation. For example, Profile 5: “Reluctant but required to participate”, should be informed about the benefits of continuous learning and upskilling throughout life to overcome this profile’s comparatively low motivation to participate further once having already participated. Profile 6: “Participating in response to work pressures” and Profile 7: “Participating to strengthen career prospects”, should be informed about learning options that could increase career prospects or facilitate new career paths.
Key insights: “Intrinsically motivated” profiles
The “intrinsically motivated” profiles represent those already learning and whose participation in learning is driven by its inherent pleasure and rewards. This category of motivational profile can be further divided into Profile 8: “Participating for personal development” and Profile 9: “Participating for professional and personal development”. Together these profiles represent 10% of the Flemish adult population.
For intrinsically motivated learners, incentives and information and guidance services are less crucial for ensuring participation, as their intrinsic drive to learn is often enough. Still, many learners with these profiles are benefitting from existing incentives and support, and continued access to this assistance may be important for their sustained engagement. A key insight for policy making that emerged from the analysis of “intrinsically motivated” profiles is provided below:
Even the most motivated learners may need incentives and support to maintain their strong engagement in learning. There are various indications that intrinsically motivated adults are actively using available incentives and support measures for learning. While Flanders may wish to target more resources at those not currently engaging in learning, it will be important not to undermine the motivation of those already engaged in learning by withdrawing certain financial and non-financial benefits that may underpin that motivation. 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.
General policy implications of the adult learner profiles
The findings of this study also have more general policy implications beyond specific adult learner profiles. 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 measures.
Using the nine profiles to strengthen the evaluation and monitoring of policies
The learner profiles could help strengthen existing evaluation practices by providing insights into 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.
The profiles could be used to strengthen ex ante policy evaluation practices in Flanders. Flanders could introduce new guidelines and requirements, including the requirement that departments designing adult learning policies and programmes explicitly evaluate the 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.
The profiles could also be used to strengthen ex post evaluation in Flanders to reveal the extent to which existing adult learning policies improve learning motivation and participation for different learner profiles. Evaluation currently seems to be ad hoc and focused on participation and satisfaction measures rather than outcomes for target groups. More comprehensive programme evaluations could be applied that also seek to uncover 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. This could be achieved through new guidelines and requirements for evaluations that could require departments responsible for adult learning policies and programmes explicitly evaluate the outcomes of the policies for adult learner profiles.
To strengthen both ex ante and ex post evaluation, Flanders could also consider insights into the learner profiles identified in other studies, especially Flanders’ “customer journeys” study. By doing so, 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.
Using the nine profiles for the design and implementation of adult learning policies
Insights from 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). Consideration should be given to additional data and tools that could support operationalising the profiles for policy making and implementation. These opportunities are discussed in more detail below:
Using the nine profiles to strengthen the provision of information and guidance services. The adult learner profiles identified in this project can be used to better target and tailor information and guidance services to adults in various ways. Flanders could develop a communications strategy to raise awareness of these profiles and their potential uses. Insights from the profiles are also expected to inform the Partnership for Lifelong Learning’s strategy (“een gesegmenteerde mobiliseringsstrategie”) for engaging hard-to-reach groups in learning. The profiles can also be used as input for digital marketing tools on social media that provide tailored information to different learner profiles. They could also be used to make information provided on centralised online portals more targeted and tailored to the needs of different learners, for instance with several questions for visitors to direct them to tailored information. Insights from the nine learner profiles could support learning and career guidance. For example, by comparing the characteristics of clients against learner profiles, career and learning counsellors can obtain valuable insights into the sorts of learning opportunities, incentives and support measures that might be most suitable.
Using the nine profiles to support the design of the Flemish ILA. The development of the ILA (Leer- en loopbaanrekening) is a priority for the government of Flanders. ILAs provide universal access to training for all groups of individuals, but evidence of their success in increasing training among under-represented groups is limited. A more targeted and tailored scheme would help to better reach those most in need of training. 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 noted that this OECD study, as well as the Flemish “customer journey” study, provide important insights into barriers faced by learners and effective policies for overcoming them, which could be helpful in the design of the ILA and complementary support measures. In addition, the nine profiles could be used to tailor communications about the learning and career account to different learners, and as a tool to evaluate whether this account is meeting the needs of different types of learners.
Providing insights into how better data and tools could support operationalising the profiles as tools for assessing and referring learners. Most of the analysis in this chapter has focused on using the profiles to inform policies by providing broad insights; however, they could also be used 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. This application has not been extensively discussed, primarily due to the question of how to operationalise the profiles in practice. Operationalising the profiles as a tool for assessment and referral 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, which would require addressing gaps in data and improving data sharing between the various actors that collect information on adult learners (e.g. through new protocols to facilitate information sharing). Alternatively, Flanders could develop a tool that enables providers to assess adults’ likely learner profiles based on a more limited range of readily observable characteristics (e.g. age, education level and occupation). However, the outcomes of such an exercise should be interpreted with caution as the characteristics of profiles are based on probabilities. Learner profiles could be updated over time based on newly available or updated sources (e.g. the next round of AES) to ensure that they continue to accurately represent the main types of learners in Flanders.
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