The career pathways approach to workforce development emerged to help workers with lower levels of formal education advance to better paying jobs by earning in-demand postsecondary credentials. The approach involves articulated steps of education, training, and jobs within an industry sector or occupational cluster, combined with other services and employer connections to support participant success. To advance the evidence base in the career pathways field, the Descriptive & Analytical Career Pathways Project (D&A CP Project) includes three sub-studies, each addressing different evidence gaps through distinct data sources and methods.

This D&A CP Project Machine Learning Study Brief, Machine Learning in Workforce Development Research: Lessons and Opportunities, presents findings from an exploratory study examining how machine learning can be used to synthesize a large body of data about the implementation of career pathways programs. Machine learning refers to a range of computer models and algorithms able to uncover patterns, create categories, and make predictions from large data sets without being given step-by-step directions from a human. The study: (1) explores how machine learning can be used synthesize and draw lessons from available text-based data to provide comprehensive information on implementation of career pathways programs, and (2) provides lessons on how machine learning could be used in future workforce development research.

The exploratory study suggests that machine learning:
• Can be a powerful research tool in the right context.
• Involves some risk and users should be cognizant of the limitations and expected results of this approach.
• May struggle to replicate the detail or nuance of human research in the context of implementation research.
• May require human researchers to dedicate substantial time and resources to define key concepts.
• May require substantial input from human researchers.
• May require a team with interdisciplinary skill sets to be completed successfully.
• Operates in an evolving legal, computing, and cost environment.

The other two sub-studies in the D&A CP Project include a Meta-Analysis Study and the Career Trajectories and Occupational Transitions (CTOT) Study.