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Degree Attainment in Computing: Intersectional Switching Trends

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2023 ASEE Annual Conference & Exposition


Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Computing and Information Technology Division (CIT) Technical Session 8

Tagged Division

Computing and Information Technology Division (CIT)

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Paper Authors


Jia Zhu Florida International University Orcid 16x16

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Jia Zhu is a Ph.D. student in the Knight Foundation School of Computing and Information Science at Florida International University (FIU). Her research interests include computer science education, educational data mining, and data science, focusing on broadening participation in engineering and computing.

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Stephanie Jill Lunn Florida International University

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Stephanie Lunn is an Assistant Professor in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) and the STEM Transformation Institute at Florida International University (FIU). She also has a secondary appointment in the Knight Foundation School of Computing and Information Sciences (KFSCIS). Previously, Dr. Lunn served as a postdoctoral fellow in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology, with a focus on engineering education. She earned her doctoral degree in computer science from the KFSCIS at FIU, in addition to B.S. and M.S. degrees. She also holds B.S. and M.S. degrees in neuroscience from the University of Miami. Her research interests span the fields of computing and engineering education, human-computer interaction, data science, and machine learning.

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George D. Ricco University of Indianapolis

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George D. Ricco is an engineering education educator who focuses on advanced analytical models applied to student progression, and teaching first-year engineering, engineering design principles, and project management.

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Although efforts have been made to broaden participation in computing, ongoing reports and counts in the field continue to illustrate the need to improve engagement and retention. There remains a minoritization of Black or African American men and women, Hispanic or Latinx men and women, Indigenous men and women, White women, and Asian women. As such, it is vital to explore trends over time and find new potential avenues to attract students to computing. Developing a better understanding of students’ trajectories, and potentially the variable ways they may enter the major before obtaining their degrees, can offer avenues for recruitment. We conducted a quantitative analysis of switching behaviors using the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). The theoretical framework of intersectionality guided the inquiry as we examined patterns and disaggregated them by gender, race, and ethnicity. We sought to explore trends in switching behaviors for those entering computing, including potential variations in: 1) the major in which students earn their first undergraduate degree; and 2) different intersectional groups. We focused specifically on students from CIP6 11, Computer and Information Sciences and Support Services. Our analysis of MIDFIELD illustrated that many intersectional populations of students who transferred into computing came from another engineering field. However, several racial and ethnic groups of women primarily entered through non-STEM fields. Among these women, those who identified as Black and those who identified as Hispanic or Latinx most often switched from a Business major, at 31.5% and 29.4%, respectively. Meanwhile, women classified as International and those who identified as White most often transferred from the Liberal Arts/Humanities, at 41.7% and 32.7%, respectively. The findings of this work suggest that women who enter computing may do so through distinctive pathways. Going forward, this provides new opportunities for educators and administrators to consider what may appeal to different intersectional populations of students, particularly for women that identify as Black, Hispanic or Latinx, International, and/or White. This study offers information that could shape what topics may attract students to a computing field and how they could be incorporated into computing lessons or examples.

Zhu, J., & Lunn, S. J., & Ricco, G. D. (2023, June), Degree Attainment in Computing: Intersectional Switching Trends Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44638

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