What drives working adults to be lifelong learners? Determinants and patterns of adult education participation in the United States

Abstract

With the recent shifts in the global economy, many scholars and policymakers are in broad agreement on the importance of lifelong learning practices in the occupational sphere. In response, there has been growing academic interest in adult education participation in which working adults acquire knowledge and skills to fulfill the ever-changing needs of the world of work. Many researchers have revealed that participating in adult education provides a wide array of benefits for individuals, organizations, and society. Yet, despite the increased research efforts, empirical findings are still inconclusive on what contextual factors most decisively or relatively importantly contribute to determining and patterning working adults’ participation in adult education. In this context, this study is conducted to present a holistic picture of adult education participation. To that end, the purpose of this study is to re-examine the determinants and patterns of adult education participation of working adults by leveraging emerging analytic techniques to capture population-level insights on (1) what drives participation in adult education and (2) how discrete patterns in adult education participation emerge. The data is drawn from the 2017 U.S. Program for the International Assessment of Adult Competencies (PIAAC). The total sample size was 1,283 respondents aged 25 to 65 years old who had work experience in the last 12 months. Outcome measures were formal adult education and training (AET), non-formal AET, and informal learning, all of which indicate three major pillars of adult education participation. The selected 19 independent variables represent working adults’ individual-level (i.e., demographic information, human capital, and learning-related socio-psychological states) and work-related contexts. Through the random forest classifiers (RFCs) technique, one of the machine learning algorithms, this study identified important factors associated with participation in adult education. In addition, latent class analysis (LCA) was applied to investigate discrete patterns of adult education participation among sub-groups of working adults that share similar profiles of individual-level and work-related characteristics. According to the results obtained from RFCs models, first, skills proficiency and/or utilization appeared to be the far most critical influencers across every type of adult education participation. Second, education level and monthly income were the common salient predictors across types of adult education participation. Third, predictors explaining adult education participation somewhat varied depending on the types of adult education. By applying the LCA approach, this study identified four latent classes of working adults in adult education participation: (1) low-participation learners, (2) high-participation learners, (3) informal learners, and (4) structured learners. Moreover, the results demonstrated that the broader separation of working adults’ participation in adult education itself was strongly affected by situational and institutional contexts, whereas individual preference or selection across types of adult education relies on personal demographics and human capital. Based on the findings, this study concluded with several discussions and implications for research, policy, and practice.