This Annex presents the theoretical foundations of the LLL conceptual framework. It builds on and combines a variety of approaches to LLL.
It is grounded in an integrated body of theories that address human agency in learning, systemic interconnections between actors and societies, and the evolving role of (digital) technology in education and learning. These theories provide analytical lenses for the framework’s three pillars – demand (capability gaps and needs), supply (diverse provisions), and coordination (alignment mechanisms) – to understand how learning needs emerge, how different types of provisions are structured across institutional clusters, and how they are aligned within multi-level ecosystems. Drawing from diverse disciplines, this foundation ensures a holistic approach.
Its conceptual foundations draw from:
1. Capability Theory (Sen, 1999[1]; Nussbaum, 2011[2]; Boyadjieva and Ilieva-Trichkova, 2021[3]) views learning as the expansion of substantive freedoms, enabling valued functioning like personal growth, social inclusion, and civic participation beyond economic productivity. It informs the demand pillar of our model by framing learning needs as capability gaps and guides equitable policy design for diverse life domains, aligning with RA3’s focus on inclusive data strategies.
2. Social Capital Theory (Putnam, 2000[4]) emphasises the role of social networks in fostering resilience, civic engagement, and collective outcomes. It connects to the provision of LLL within the civic cluster (Section 4.3.4) and the coordination pillar (Section 2), highlighting how relational ties amplify learning impacts and mitigate barriers for disadvantaged groups, supporting RA2’s analysis of national variations in community-driven learning.
3. Life-Course Theory (Elder, 1994[5]) illustrates how early experiences shape long-term learning trajectories, emphasising timely interventions to prevent compounding inequalities in the form of the “Matthew effect” (Boeren, 2016[6]). It links to demand (Section 3) and the coordination pillar (Section 2.1), underscoring the cumulative effects of educational biographies across life transitions.
4. Ecological Systems Theory (Bronfenbrenner, 1979[7]) provides a multi-layered model – spanning micro (individual), meso (organisational/community), and macro (national) levels – to analyse how nested environments shape learning opportunities. It informs innovative funding, credentialing, and institutional support within the supply pillar’s clusters (Section 4.3), supporting RA2’s comparative analysis.
5. Complexity Theory (Prigogine and Stengers, 1984[8]) explains LLL systems' adaptation to unpredictable, non-linear dynamics, such as technological disruptions or demographic shifts. It supports RA3’s data strategies through adaptive models (e.g. agent-based simulations) to predict emergent behaviours, enhancing coordination resilience (Section 2.4).
6. Structuration Theory (Giddens, 1984[9]) situates individual and organisational behaviours within structural constraints, framing the recursive interplay of agency and structure. It guides our discussion of multi-level coordination (Section 2), emphasising how meso-level actors (e.g. employers, NGOs) mediate between individual needs and national policies.
7. Actor–Network Theory (Latour, 2005[10]) conceives people, technologies, and policies as interconnected actors co-shaping LLL ecosystems. It enables mapping networks of formal qualifications, informal self-study practice, and digital platforms, ensuring diverse provisions are visible and accessible for RA1’s taxonomy and RA3’s data integration (Sections 4.2, 2.5).
8. Platformisation Theory (van Dijck, Poell and De Waal, 2018[11]; Poell, Nieborg and van Dijck, 2019[12]) examines how digital platforms transform learning into data-driven processes through algorithmic curation and datafication. It informs the supply pillar (Section 4.2) by analysing platforms like LinkedIn, YouTube, or Coursera as expanding sites of learning, raising equity and governance challenges.
This integrated theoretical foundation mirrors the framework’s open, adaptive design, as outlined above. It remains responsive to emerging challenges, such as recent advances in AI (Suleyman and Bhaskar, 2024[13]) and demographic shifts (Scott, 2024[14]), enabling continuous refinement across research agendas. This adaptability reflects Clarke’s (2005[15]) approach in her situational analysis, which underscores that new theoretical and methodological frameworks must remain flexible and context-sensitive if they are to grasp the complexity of contemporary social life. By conceptualising LLL as an ecology of networks, our approach facilitates integration of diverse data sources (e.g. Programme for International Student Assessment (PISA), Programme for the International Assessment Competencies (PIAAC), platform analytics, and administrative data) and theoretical perspectives, addressing gaps identified in Sections 2–4.
These theoretical traditions have enabled the formulation of eight foundational principles, which inform the framework’s components and their interrelationships, integrating the demand, supply, and coordination pillars:
1. Life-wide and lifelong scope: Encompasses all life stages, from childhood to retirement, across diverse contexts like work, family, and civic life, ensuring a holistic approach to learning needs and provisions (Jarvis, 2004[16]); see Figure 1.1.
2. Bounded agency: Recognises that individual and collective learning choices are shaped by constraints such as socioeconomic barriers or institutional structures, applicable at micro (individual) and meso (organisational/community) levels (Salling Olesen, 2001[17]; Evans, 2007[18]; Rubenson and Desjardins, 2009[19]; Boeren, 2016[6]; West and Michie, 2020[20]); Section 2.2 and 3.
3. Early foundations: Draws on life-course research to underscore the long-term effects of early interventions. For instance, research has shown that exposure to preschool programmes is associated with higher annual earnings among children from low-income backgrounds (World Bank, 2016[21]; Chetty et al., 2024[22]).
4. Coordinated transitions: Emphasises enabling context-specific transitions across the life-course (e.g. career changes, retirement), ensuring LLL systems support unique needs through tailored pathways and coordination, as highlighted in Section 2.3 (Elder, 1994[5]; Desjardins, 2017[23]).
5. Beyond economic returns: Values capabilities linked to well-being, social inclusion, and civic participation, aligning with capability theory and broadening LLL’s scope to include social outcomes like community cohesion and wider benefits like health literacy (Schuller et al., 2004[24]; Field, 2011[25]; Boyadjieva and Ilieva-Trichkova, 2021[3]).
6. Institutional embeddedness: Positions LLL within diverse institutional ecosystems, spanning formal education, labour, welfare, and civic clusters, as outlined in Section 4.3. It emphasises that provisions must reflect contextual realities through coordinated action, open communication, and stakeholder negotiation. In many countries, governance networks remain fragmented due to disconnected institutional boundaries and the absence of a coherent, shared vocabulary regarding the scope of LLL (Rothstein and Stolle, 2008[26]; Kalenda and Desjardins, 2025[27]).
7. Negotiated settlements for effective institutions: Leverages the institutional dimension of social capital to foster stakeholder collaborations (e.g. tripartite agreements among governments, employers, unions), creating governance structures that align diverse interests, resolve coordination failures, and enhance system coherence and equity through effective communication and negotiations (Putnam, 2000[4]; Rothstein and Stolle, 2008[26]; Desjardins, 2017[23]).
8. Resilient systems: Supports adaptive feedback loops and data-driven responses to global challenges like automation or ageing populations, leveraging complexity theory to ensure systemic adaptability (Folke, 2006[28]).
This integration ensures the framework remains holistic, multi-domain, and adaptable, aligning theory, policy, and practice to foster inclusive LLL ecosystems.