Course-Skill Atlas: A National Longitudinal Dataset of Skills Taught in U.S. Higher Education Curricula

Authors

Alireza Javadian Sabet, University of Pittsburgh
Sarah H. Bana, Chapman UniversityFollow
Renzhe Yu, Columbia University
Morgan R. Frank, University of Pittsburgh

Document Type

Article

Publication Date

10-4-2024

Abstract

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity. Here, we fill this gap by presenting Course-Skill Atlas – a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education’s role in preparing students for the labor market. Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Comments

This article was originally published in Scientific Data, volume 11, in 2024. https://doi.org/10.1038/s41597-024-03931-8

Recommended Citation

Javadian Sabet, A., Bana, S. H., Yu, R., & Frank, M. R. (2024). Course-Skill Atlas: A national longitudinal dataset of skills taught in U.S. higher education curricula. Scientific Data, 11(1), 1086. https://doi.org/10.1038/s41597-024-03931-8

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.