Reframing the representation of Black students in undergraduate computing.

The underrepresentation of Black students among undergraduate computing degree earners is a persistent problem that has received attention from a variety of groups, including educators, researchers, funders, and policymakers. However, awareness of the nature and extent of this underrepresentation is often limited, and in fact varies widely depending on which data source is consulted. Such discrepancies result from the fact that the method of reporting computing degree attainment can vary in terms of how computing is defined and which institutions are considered. This study uses data from the National Center for Education Statistics to examine how the representation of Black undergraduate computing degree earners is sensitive to the nuances of field definition, institutional type, and gender. Findings reveal that despite recent gains in the number of Black students earning computing degrees, their representation among all computing degree earners, and specifically in computer science and computer engineering subfields, is on the decline. Additionally, fewer computing degrees are being earned at Historically Black Colleges and Universities (HBCUs), while for-profit institutions have shown net gains in awarding computing degrees to Black students. This paper explores these and other findings that reveal important variations in computing degree attainment trends among Black students by gender, computing subfield, and institutional type. The paper concludes with implications for research and practice as well as a discussion of how equity considerations in computing might be reframed.

Reframing the representation of Black students in undergraduate computing.

  • Author Sax, Linda J.; George, Kari L.; Harris, Daniel; Payton, Fay C.
  • Publication Title Journal of Women and Minorities in Science and Engineering
  • Publication Year 2020
  • BPC Focus Underrepresented Racial/Ethnic Groups, Black/African American Students
  • Methodology Survey, Longitudinal, Multi-institution
  • Analytic Method Chi-square/Contingency Table
  • Institution Type NA
  • DOI 10.1615/JWomenMinorScienEng.2020028576
  • URL https://doi.org/10.1615/JWomenMinorScienEng.2020028577