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The brief summarizes lessons learned from using machine learning to study the implementation of career pathways programs. First, this brief describes the research questions that guided the study and summarizes the machine learning methods designed for the data collection and analysis activities, including study limitations and challenges encountered. It then provides lessons learned on using machine learning methods for social science research. Finally, the brief discusses strategies for using these methods in future workforce development projects and other areas, particularly federally funded efforts.

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Key Takeaways

• Machine learning can be a powerful tool in the right context.
• Machine learning involves some risk and users should be cognizant of the limitations and expected results of this approach.
• Machine learning may struggle to replicate the detail or nuance of human research in the context of implementation research.
• Machine learning may require human researchers to dedicate substantial time and resources to define key concepts.
• Machine learning may require substantial input from human researchers.
• Machine learning may require a team with interdisciplinary skill sets to be completed successfully.
• Machine learning operates in an evolving legal, computing, and cost environment.

Citation

De La Rosa, S. M., Greenstein, N., Schwartz, D., Lloyd, C. (2021). Abt Associates. Machine Learning in Workforce Development Research: Lessons and Opportunities. Chief Evaluation Office, U.S. Department of Labor.

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The Department of Labor’s (DOL) Chief Evaluation Office (CEO) sponsors independent evaluations and research, primarily conducted by external, third-party contractors in accordance with the Department of Labor Evaluation Policy and CEO’s research development process.