Determinants of Adult Education and Training Participation in the United States: A Machine Learning Approach.

This article examines the key determinants of working adults' participation in job-related adult education and training (AET) in the United States, using random forest classifiers (RFCs), a machine learning technique, to analyze data from the 2017 U.S. Program for the International Assessment of Adult Competencies (PIAAC). The study distinguishes between formal AET—structured learning leading to certifications—and nonformal AET, such as workshops and seminars without formal credentials. Findings indicate that age and skills use at work are the most important factors influencing formal AET participation, while skills use at work and organization size are most significant for nonformal AET participation. The results highlight the critical role of skill utilization and organizational context in adult learning engagement and suggest that policies promoting lifelong learning should focus on enhancing skills use and providing organizational support to foster continuous workforce development.