Artificial Intelligence and Machine Learning for Workforce Evaluation

AI and ML are powerful tools for translating large-scale data into actionable insights. This annotated bibliography offers uses for analytical capacity development and advanced analytical approaches in research and evaluation.

The first section of "Developing Analytical Capacity and Techniques for Large-Scale Workforce Data: An Annotated Bibliography" introduces methods for research and evaluation in workforce development, such as the use of cluster analysis, market basket analysis, predictive analytics, text mining, and topic modeling. The second section explains how workforce development agencies may use such methods to develop analytical capacity. Additional details describe how to build analytical capacity through technical infrastructure or human capital.

The annotated bibliography also includes references that may support evaluators when building a portfolio of evidence and conducting evaluations using large-scale data. The bibliographic citations, organized alphabetically by evaluation topic area, include the author's name(s), year published (with dates from 2002 to 2020), title, and a hyperlink to the online resource. The evaluation topics focus on analytical capacity development, cluster analysis, predictive analysis, text mining, and topic modeling.

• Last Updated:
• Created:
• Resource Publication Date: 2022
• Posted by: Alexander Fones
• Posted in: Evaluation and Research Hub