From left to right: Dr. Chantra Nhien, Dr. Lara Perez-Felkner, Dr. Annie Wofford and Dr. Bret Staudt Willet

Aligning Graduate Education and Workforce Opportunities: A Robust, Equity-Focused Landscape Scan of Computing Terminology

About

In this mixed methods research project, our team is investigating the (mis)alignment of computing-related terminology used to describe master’s and doctoral training at Minority-Serving Institutions (MSIs) and that which is employed in computing-related job advertisements. With the goal of examining terminology as a potential barrier to racial equity in the computing and technological workforce, this mixed methods project will offer a robust, equity- focused landscape scan that establishes a typology of computing-related terminology.

Project Phases

This two-year project (2023-2025) will commence over two phases of research, with four research questions, and uses an exploratory sequential mixed methods design. Phase 1 (2023-2024) will qualitatively address the nature of computing-related terminology at MSIs, using document analysis of program information and semi-structured interviews with 50 faculty and administrators. Phase 2 (2024) will use latent Dirichlet allocation topic modeling and log odds ratio comparison with web scraped data to quantitatively explore terminology in computing-related job advertisements as well as the alignment between terminology used in job advertisements and graduate degree programs.

Funding Support

This research project, entitled “Aligning Graduate Education and Workforce Opportunities: A Robust, Equity-Focused Landscape Scan of Computing Terminology” is supported by the Alfred P. Sloan Foundation, grant #G-2022-19569

Sloan resarch team group shot
From left to right: Xinting Wu, Annie Wofford, Lara Perez-Felkner, Bret Staudt Willet, and Anum Fatima