1. We have examined programmes, governance and policies in EU adult learning: how adult learning ‘markets’ – public and private – reverse or reproduce inequalities. We found that, unfettered, markets strengthen inequalities: providers must invest heavily, both financially and in time, to provide enough personalised support for those most in need. Most programmes fall short. A particular risk in more market-oriented measures is ‘parking and creaming’: when providers focus on ‘low-hanging fruit’, and leave those most in need still in need.
Focussing on low-hanging fruit is common to most systems based on quantitative targets or ‘payment by results’. We also explored how policy is shaped, and how policies (and policy-makers) have formulated issues related to inequality: the changing ways in which social inclusion, vulnerability, and similar ideas are deployed. These have been reshaped over time to imply that responsibility for poor circumstances, and the route to a better life, are matters more of individual than collective responsibility. One profoundly important feature of the EU is its complex multi-level governance: nation states, local government, private companies, social movements and other actors combine to generate policies and carry them into effect. Using the policy trail method, we have thrown new light on policy formation, the role of particular policy measures and instruments (and mixes of them), in giving weight to particular actors and interests.
2. We examined variation in adult learning participation rates. We analysed how far ‘system characteristics’ – such as structural features of educational systems and labour markets – explain variations in participation rates, especially among disadvantaged young people. We used large-scale datasets, multilevel regression analysis, and a ‘pseudo-panel’ to understand change over time. A particularly innovative feature was to ‘drill down’ to compare not only countries but regions. Despite ‘globalisation’, labour markets, educational provision, demography etc often differ at the regional level. Our analysis shows how labour markets shape the need for skills and the opportunities for learning. The growing need for personal care skills seems ill-served by labour markets and policy aims which prioritise high-technology skills such as ICT. EU adult learning participation targets should be differentiated to take account of unequal social and geographical distribution of educational participation.
3. We looked at what and how young adults learn at work, using case studies of their organisational environments, based on extended interviews, documentation and observation. The quality of young adults' workplaces varies. We looked at three sectors (metals, retail and adult education) with contrasting features. We distinguished the impact on an individual’s learning of their workplace from particular features of their lifecourse. We showed that good workplace learning plays a vital role in creating occupational and professional identities and motivation to learn. We discovered managers are often unaware of their key role in shaping workplace learning potential.
4. We developed and piloted an Intelligent Decision Support System (IDSS), using case-based reasoning. With ‘big data’ and Artificial Intelligence playing an ever-greater part in people’s lives, influential voices suggest an IDSS can provide a more ‘scientific’ basis for policy. An IDSS needs to be focussed on specific areas: we focused on young adults Not in Employment, Education or Training (NEET); but our model could be applied elsewhere. We show the potential of an IDSS, but also that data currently collected in the EU needs to be strengthened to be a reliable base for such developments. We also point out that, in diverse democracies, specifying desired outcomes too precisely brings risks.
