Periodic Reporting for period 2 - ENLIVEN (Encouraging Lifelong Learning for an Inclusive and Vibrant Europe)
Reporting period: 2017-10-01 to 2019-09-30
Nevertheless, not all has gone well. A target for participation was set in 2000, and raised under ‘Europe 2020’: it is clear that even the target originally set for 2010 will not be achieved by 2020. Early hopes that lifelong learning could ensure prosperity and social inclusion seem wide of the mark: for instance, people who participate in education and training as adults are overwhelmingly those who benefited most from education while at school. Those most in need get least. We know that lifelong learning to help the young – particularly badly affected post-2008 – have been a very qualified success. And with growing ‘Euroscepticism’, EU lifelong learning has clearly not succeeded in creating a universally-accepted European identity.
Of course, these are intractable problems. No single project can provide all the solutions – and even if we could, there is no simple link between what research demonstrates and what ‘policy-makers’ think realistic. With a focus on young adults (though we do not believe the young alone deserve education), Enliven has thrown light on what has gone wrong – and well – and why. In doing so, we have contributed to scientific and policy understanding.
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.
We have also made theoretical advances. We have explored and developed theories of bounded agency in adult learning. We have applied case-based reasoning in AI to policy-making. We have developed and operationalised the ‘policy trail’ method across a range of policy research issues. We have investigated EU governance using the notion of policy mixes. We have explored European datasets using a pseudo-panel approach, enabling better understanding of change, cause and effect. We have investigated the system characteristics of adult learning through analysis of regional level data. We have shown the value of learning biographies in understanding young adults’ learning, and developed new techniques for their analysis.