SDG 4: Quality Education | SDG 8: Decent Work and Economic Growth | SDG 10: Reduced Inequalities
NITI Aayog | Ministry of Education | Ministry of Skill Development and Entrepreneurship
OECD policy paper, The many faces of adult learners: Who learns, why, and who is left behind argues that traditional models of front-loading skills development in initial education are becoming untenable due to rapid technological progress. Published in February 2026, this paper introduces a novel learner segmentation model to move beyond broad demographic classifications and understand the specific motivations and barriers that shape adult learning decisions. Using Latent Class Analysis (LCA), the model identifies distinct learner profiles across five regions—Flanders (Belgium), Bulgaria, Finland, Ireland, and Portugal—to enable more targeted resource allocation.
Comprehensive Adult Learner Profiles The research identifies nine stylised profiles across participating and non-participating populations:
Unmotivated Non-Participants: Includes “Disengaged Adults” (Profile 1), who perceive no need to learn and face no significant barriers, and “Unmotivated due to Age/Health” (Profile 2).
Motivated but Constrained Non-Participants: Comprises those “Motivated but Facing Time Obstacles” (Profile 3) and those facing “Multiple Obstacles” such as high cost and lack of suitable offers (Profile 4).
Extrinsically Motivated Participants: Groups like “Reluctant but Required” (Profile 5), often driven by law or employers, and those responding to “Work Pressures” (Profile 6) or seeking to “Strengthen Career Prospects” (Profile 7).
Intrinsically Motivated Participants: Includes individuals participating for “Personal Development” (Profile 8) or a mix of “Professional and Personal Development” (Profile 9).
What is “Latent Class Analysis” (LCA) in the context of adult learning policy? Latent Class Analysis (LCA) is a quantitative statistical clustering technique used to identify unobserved (latent) subgroups within a population based on shared patterns of observed behavior and attitudes. Unlike traditional demographic sorting—which might only look at age or education level—LCA groups individuals by how they interact with multiple variables simultaneously, such as high motivation coupled with severe time constraints. This allows policymakers to move beyond “one-size-fits-all” strategies and create precision-targeted interventions for specific profiles, such as those requiring confidence-building versus those needing financial subsidies.
Limitations and Risks of Profiling While powerful, the use of learner profiles carries inherent challenges that require careful management:
Oversimplification: Profiles represent analytical frameworks and patterns rather than fixed descriptions of individuals, risking the loss of nuances in real learning experiences.
Reinforcement of Stereotypes: Labels emphasizing low motivation or disengagement can lead to “victim-blaming” if structural barriers like unequal access or poor quality are ignored.
Static Nature: Profiles are snapshots in time and may become misaligned as learners’ needs and economic circumstances evolve.
Policy Relevance
The report provides a strategic framework for strengthening India’s skill development missions through precision-targeted interventions. Adopting a segmentation approach allows India to address the “Matthew effect,” where highly skilled individuals continue to accumulate advantages while those with the lowest education levels fall further behind.
Strategic Impact:
Optimizing Outreach: Identifying “Disengaged” profiles (Profile 1) allows India to shift from generic skill campaigns to community-based, confidence-building initiatives that highlight the immediate economic returns of upskilling.
Bridging the Participation Gap: For “Structurally Constrained” segments (Profile 4), policies can prioritize modular, flexible learning formats and digital infrastructure over centralized, time-intensive training.
Enhancing Institutional Accountability: Embedding profiling into digital platforms like the Skill India Digital Hub can track whether incentives are reaching underrepresented groups or merely subsidizing those who would have learned regardless.
Personalizing Guidance: Career and learning counselors can use diagnostic tools to compare individual client traits with established profiles, providing more tailored support and training pathways.
Follow the full report here: The many faces of adult learners | OECD