Date of Submission

Fall 2024

Academic Program

Computer Science

Project Advisor 1

Valerie Barr

Abstract/Artist's Statement

This study investigates the potential impact of predictive algorithms in higher education on the disparity between students' intent to major in STEM fields and their actual graduation rates, focusing on variations across racial groups and genders. By analyzing data from two distinct periods—before and after the widespread adoption of predictive algorithms—the study examines how these tools have influenced these disparities. Using statistical analyses and visualizations generated in R, the findings highlight significant effects, particularly in Biological Sciences, where the possible impact of predictive algorithms varies by race and gender. The research also highlights the potential for greater disparities due to the limited availability of post-predictive algorithm era data on STEM intent rates. If such data were accessible—and synthetic data had not been used in its place—the analysis might have revealed even larger differences between intent and actual graduation rates in STEM fields.

Open Access Agreement

On-Campus only

Creative Commons License

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

This work is protected by a Creative Commons license. Any use not permitted under that license is prohibited.

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