The participation of adults in learning and education (ALE) has been subject to research for many years, and attempts to make it more prevalent have been going on throughout the world due to its value in the labour market and the empowering role of ALE in individuals’ social relationships, social mobility, job prospects, finances, health, and wellbeing. These innumerable benefits of ALE require the genuine dedication of countries to making participation in ALE more prevalent and accessible for all adults, regardless of their educational or socio-economic background. Inevitably, this dedication necessitates effective policymaking that aims to involve more adults in learning, especially those with lower educational attainment and from lower social classes who are traditionally less prone to participate in or access ALE opportunities (Boeren, 2009; Kersh & Laczik, 2021). One of the most crucial factors influencing the effectiveness of policymaking is taking scientific evidence into account during the decision-making process concerning ALE. The consistent decrease in ALE participation rates in the United Kingdom since 2010s makes evidence-based policymaking more indispensable and urgent than ever. Through the use of scientific evidence, policymakers can better target the groups who don’t participate in ALE or those who are deprived of ALE opportunities, which can result in an increase in total participation rates and more equitable proportions of learners within those rates based on their economic and social backgrounds. However, the effectiveness of evidence-based policymaking is partially bound to the amount and quality of the scientific evidence available. It is ideally expected that the data on ALE should provide a good measurement of the people who participate, for what reasons, and in what type of learning activities as well as the benefits of ALE (Boeren, 2016). It is equally important that the data on ALE should depict a very accurate picture of who does not participate and why.

This contribution will present findings from an ongoing research project funded by the UK’s Economic and Social Research Council (ESRC). The project aims to investigate the statistical evidence-base in ALE in the UK, reveal the potential reasons behind the decreasing participation rates, and unpack how policymakers benefit from the available evidence-base during their decision-making process. In this paper, we aim to explore how participation in ALE is measured by large-scale surveys that collect data from the UK. We also aim to investigate how major determinants of participation (motivations for ALE, barriers to ALE, and the benefits of ALE) have been encompassed by these surveys. While approaching the participation questions in the surveys, we will adopt the Total Survey Error paradigm to reveal potential sources for varying participation rates. In terms of motivations and barriers, we will mainly rely on the Bounded Agency model (Evans, 2007) and Boeren’s (2017) layered model of participation, along with other theoretical frameworks such as Cross's (1981) typology of barriers and Houle’s (1961) typology of adult learners’ motivations. 

Employing a qualitative approach, we conducted an extensive text-based content analysis on the questionnaires of 16 national and European surveys that collect data on participation in ALE from the UK context. The surveys under our scrutiny were the Adult Participation in Learning (APiL) survey, the Survey of Adult Skills (PIAAC), Adult Education Survey (AES), Labour Force Survey (LFS), European Social Survey (ESS), European Quality of Life Survey (EQLS), European Company Survey (ECS), European Working Conditions Surveys (EWC), Continuing Vocational Training Survey (CVTS), National Child Development Study (NCDS), British Cohort Study (BCS), Next Steps (NS), Understanding the Society (UtS), and the UK Time Use Survey (UK-TUS). The documents for these surveys were downloaded from their websites, as most of them were already available for public use. The questionnaires that were not publicly available were shared with us by the relevant institutions. During the data analysis, we systematically coded and categorised the questions for participation, motivations, and barriers to reveal their compatibility with the theoretical frameworks mentioned above by following the steps proposed by Zhang and Wildemuth (2009).

The findings indicate that the surveys differ from each other in terms of their methodology, their ways of measuring participation, and how they approach to motivations and barriers, which can complicate further secondary statistical analyses. Most of the surveys in our sample do not collect detailed data about ALE as it is not their primary focus. Another major finding is the scarcity of consistent and comprehensive longitudinal data underpinning ALE. It is also revealed that data on motivations and barriers are rare and the scope of them vastly differs across surveys and from the theoretical models. Most surveys do not adopt a comprehensive understanding of barriers, failing to acknowledge the layered nature of the factors affecting participation behaviour (Boeren, 2017) since the surveys usually focus on individual (micro) level factors rather than meso-/macro-level factors and seem to fail to depict the intertwined relationships between them. Therefore, they may create the illusion that nonparticipation is largely caused by individuals’ time constraints and family commitments by ignoring the role of how economic volatility may require individuals to work for longer hours or how insufficient childcare policies may impede participation. In conclusion, the data underpinning ALE is mostly piecemeal and divergent in nature, which undermines future secondary analyses and comparisons across different surveys. Although the available ALE data do tell us which groups of people tend to participate, we are still in the dark when it comes to answering more intricate questions: When do former non-participants switch to the state of participation? When do former participants stop learning? How are the switching states of (non)participation affected by micro, meso-, and macro-level determinants? The lack of answers to these questions may jeopardise effective policymaking by preventing policymakers from addressing the most relevant factors and cause ALE policies to be tautologous, generic, or deflective.

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