This chapter examines learning needs, aspirations, and demand as a core pillar of the lifelong learning (LLL) framework, defining learning needs as the gap between existing capabilities and those required to achieve personal goals or participate effectively in social, civic, and economic life. It argues that learning needs and aspirations evolve across the life course, remain unevenly expressed among different groups, and often fail to translate into actual demand for learning. The chapter considers learning needs at the individual, organisational, and state levels, highlighting the tensions and interactions between these actors. It emphasises the importance of collecting richer and more inclusive data on learning needs, particularly for disadvantaged groups, children, and older adults, and of recognising the complexity, diversity, and interconnectedness of learning needs across society.
A Conceptual Foundation for the Development of a Lifelong Learning Measurement Framework
Abstract
The second of the three fundamental pillars of the LLL framework is the concept of learning needs, aspirations, and demands. Learning needs refer to the gap between an individual’s existing capabilities, encompassing knowledge, skills, and attitudes, and those required to either meet personal learning aspirations or to function more effectively within a given social context. These contexts extend beyond the workplace, including civic and personal life, and evolve throughout the life course. They reflect how individuals interact with social structures and respond to social change at different stages of life.
Despite their critical role in understanding engagement with LLL (e.g. Baert, De Rick and Van Valckenborgh (2006[1]); Kyndt et al. (2011[2]); Kyndt et al. (2014[3])), learning needs and interests are rarely examined in depth through large-scale comparative surveys, such as the Programme for International Student Assessment (PISA), the Programme for the International Assessment of Adult Competencies (PIAAC) or the Adult Education Survey (AES). Moreover, particularly among disadvantaged groups, such as migrants or individuals with low skill levels, learning needs may remain latent or only partially articulated (Karger, Kalenda and Kočvarová, 2022[4]; van Nieuwenhove and De Wever, 2022[5]; Van Nieuwenhove and De Wever, 2023[6]; Broek et al., 2025[7]), thereby restricting the impact of policy. This is particularly relevant for children from disadvantaged backgrounds, where the expression of learning needs or interests may be more difficult than for their peers from more advantaged backgrounds (Lareau, 2011[8]; Lareau and Goyette, 2014[9]).
For children and adolescents under 18, learning needs extend beyond formal schooling attainment. Informal and civic contexts – youth associations, digital spaces, volunteering, cultural and sports clubs – provide critical arenas where dispositions, social capital, and digital literacies develop. These experiences are often unequally distributed, with disadvantaged youth having less structured access. Capturing those needs requires data collection focused on traditional PISA respondents (15-year-olds) to map their learning needs and aspirations beyond the boundaries of formal schooling.
For older adults above 64, needs and aspirations are often latent or under-reported, partly because participation in organised learning provision is lower and partly because existing surveys frequently cut off at age 65. Their learning trajectories are tightly linked to well-being, social inclusion, and health literacy, rather than to narrow productivity metrics. Measuring such needs and aspirations requires expanded longitudinal coverage (e.g. modules in ageing surveys).
Learning needs and aspirations are closely linked to the demand for LLL, which signifies the expressed intention of individuals, groups, or organisations to participate in LLL; they usually include perceived reasons and motivations (e.g. career advancement), or perceived barriers (e.g. lack of time for training, or money resources for development of skills among the elderly) to engage in LLL (Baert, De Rick and Van Valckenborgh, 2006[1]; Kyndt et al., 2014[3]; Kalenda and Kočvarová, 2022[10]). Since not all learning needs and aspirations translate directly into demand, we distinguish between them analytically. Individuals may be unaware of their own skill gaps or may deprioritise learning due to competing life pressures and options, including those related to their family.
Consequently, an effective LLL policy must gather data on unmet learning needs to address them, as well as on the factors that influence whether these needs translate into actual engagement with LLL, thereby fostering genuine demand. In the next two sections, we dive much deeper into both concepts.
The recognition that individuals have diverse learning needs is well established (e.g. Knowles (1980[11]); Cross (1981[12]); Jarvis (2004[13])) and has been acknowledged in various LLL policy frameworks, including those of UNESCO (2019[14]), the OECD (2023[15]), and the European Commission (2016[16]).
However, we contend that for both contemporary and forward-looking LLL policies to deliver meaningful and systemic impact, they must move beyond addressing the needs and demands of a generic group of individuals: “adults.” A new data framework is needed to provide a more nuanced and comprehensive portrait of the diverse needs of social groups, age cohorts, and stakeholders. Learning needs do not emerge in a vacuum; rather, they form part of a broader ecology of stakeholders, including organisations, communities, and nation-states. These actors operate at different scales and often express divergent or even conflicting learning needs and consequently demands for LLL.
To address this complexity, LLL policy should be reframed through a multi-level analytical perspective, as recommended by several other authors (see e.g. Blossfeld et al. (2014[17]); Boeren (2016[18]; 2017[19]; 2017[20]); Cabus, Ilieva-Trichkova and Štefánik (2020[21]) and Grotlüschen et al. (2023[22])). We argue that learning needs and aspirations (but analogically also demands) operate across three interrelated levels: (1) the individual (micro), (2) organisational (meso), and (3) state (macro) levels. These levels differ not only in scale but also in how gaps in skills, knowledge, and attitudes are perceived and articulated, which, in turn, influences both the sources of data and the most appropriate data-collection techniques.
Moreover, needs and demands at these levels are not always congruent and may sometimes be in direct tension with one another. For instance, individuals from different social classes and sociocultural backgrounds often have learning priorities that diverge sharply from those of employers, who typically focus on organisational performance and support only a subset of their workforce (Erola, Mills and Solga, 2023[23]; Hornberg, Heisig and Solga, 2024[24]). Similarly, governmental strategies – such as addressing the implications of automation of work or an ageing population through large-scale upskilling and reskilling programs – may not align with the immediate aims of all employers or employer groups from specific industries or economic sectors (Busemeyer, Carstensen and Emmenegger, 2022[25]; Milana et al., 2025[26]; Hodge et al., 2025[27]). Finally, migrant families' educational aspirations for their children may not always align with local school priorities or national education policies.
For the sake of analytical clarity, we have categorised learning needs and demands across three distinct levels.
At the individual level (micro), learning needs refer to the specific learning requirements of individual actors. In shaping LLL policy, it is essential to account for the differing needs and demands between advantaged adults, typically those with higher educational attainment, stable integration into the labour market, and higher occupational status, and disadvantaged adults, who often display the opposite characteristics: low levels of education and or literacy, limited/precarious or no participation in the labour market, and lower occupational status (Desjardins, Rubenson and Milana, 2006[28]; Boeren, 2016[18]). A similar analytical approach can be applied to households, particularly in the context of children’s educational needs. To date, scholarly research has mainly focused not on needs but demand of individual actors, and it demonstrated that adults from disadvantaged backgrounds differ not only in their motivations for engaging in organised adult learning, advantaged individuals tend to be driven more by intrinsic motives (Kalenda et al., 2026 (forthcoming)[29]), but also in the barriers they face (Roosmaa and Saar, 2017[30]; Kalenda, Vaculíková and Kočvarová, 2022[31]).
At the organisational level (meso), these learning needs are the skills and knowledge needs of the diverse organisations and communities operating within society. Despite their central role in implementing LLL, meso-level actors remain under-conceptualised (and under-measured) in current policy frameworks (Boeren, 2017[19]; 2017[20]; Broek et al., 2024[32]). At this level, a distinction emerges especially between the learning requirements of private enterprises and those of public organisations and non-governmental organisations (NGOs). Private enterprises, as central actors within late-modern capitalist economies, usually seek to bridge the gap related to skills and knowledge associated with utilitarian business objectives (Hall and Soskice, 2001[33]). Therefore, their investment in human capital is directed along this line (Becker, 1964[34]; Hornberg, Heisig and Solga, 2024[24]). Public organisations and NGOs pursue more frequently missions that are not solely dictated by performance indicators or utilitarian metrics, but reflect wider social, ethical, or public service goals. Additionally, digital platforms (e.g. LinkedIn, YouTube) and tools (e.g. chatbots and conversational agents like Claude and ChatGPT) emerge as key meso-level actors, platformising learning (van Dijck, Poell and De Waal, 2018[35]) by aggregating user data to personalise and mediate access, thereby influencing organisational demands. As the present generation of children grows up in an increasingly digital world, this environment is shaping their learning patterns more than ever, with potentially profound effects on their education and development throughout life (Haidt, 2024[36]).
At the state level (macro), countries are more than a mere aggregation of individuals, families and organisations; they function as policy actors with distinct learning imperatives. These include fostering the skills formation of their population for both national economic performance and the promotion of social inclusion throughout the life course. However, national contexts vary significantly, influenced by degrees of post-industrialisation, types of initial formal education system, demographic of ageing, and a multitude of other factors (Busemeyer and Trampusch, 2012[37]; Solga and van de Werfhorst, 2023[38]; Desjardins and Kalenda, 2025[39]). Such diversity gives rise to distinct challenges and priorities, which shape learning needs and demands on the macro level.
Note: This figure illustrates the multi-level configuration of LLL needs, spanning the micro (individual), meso (organisational), and macro (state) levels. The arrows represent reciprocal and dynamic influences: individuals’ learning needs are shaped by organisational and national provisions, while institutions and states are affected by evolving individual and collective demands. The meso level functions as a crucial mediating layer, translating state policy into practice and shaping the accessibility and quality of individual learning opportunities.
The existence of these three levels carries two fundamental implications for the LLL framework.
First, the assessment of learning needs and demands should be systematically conducted at the individual, organisational, and state levels, with policies designed to ensure coherence and alignment across these scales or at least minimise their incoherence and divergence. This multi-level approach necessitates comprehensive data collection: capturing micro-level information from individuals, meso-level data from organisations, and macro-level aggregates to inform national policy design and strategic planning.
Second, these levels are embedded within a process of double structuration (Giddens, 1984[40]), wherein agency and structure mutually constitute one another. Individual needs and demands are influenced by the communities where they live and/or organisations where they work, and these organisations and communities are shaped by national institutions. The educational provisions and expressed demands for specific skills of these institutions affect the learning trajectories available to individuals. Simultaneously, organisations and the state are not merely passive providers but are themselves responsive to individual agency. They are affected by both individuals’ demand for learning and the emergent supply of adult skills, which recursively shape organisational strategies and national policy priorities. This reciprocal dynamic illustrates the complex interdependencies that characterise the governance of LLL within multi-level institutional contexts, which have direct implications for both the supply of learning opportunities (see Section 4) and the coordination of LLL systems (see Section 2).
While this mechanism has been widely recognised within the field of LLL (Rubenson and Desjardins, 2009[41]), we would like to emphasise the role of the meso level (see also Figure 3.1), which has hitherto been less explicitly conceptualised. State policy (macro level) seldom influences individual actors directly. Instead, its effects are typically mediated through meso-level organisations and communities (Clarke, 2005[42]) that deliver learning services. In the context of organisational learning, the highest level of such provision is often found within enterprises and publicly funded institutions (Desjardins, 2020[43]).
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