in the work force. Examining the data in this way indicates which
student groups may need to be better prepared for the industry
careers and graduate study that can follow their undergraduate
CS degree, and the tech industry also must examine hiring practices. In addition, new research directions are suggested by the
by-cohort analysis. As mentioned above, there must be a thorough examination of the gender demographics and race and ethnicity demographics of college preparation in various high school
programs. To the extent that data are available, there should be
an examination of the intersection of race and ethnicity with socio-economic standing. One element of this could be a study of
trends along race and ethnicity lines in attendance at two-year
institutions and at the transfer rate of students from two-year
institutions into 4-year CS programs. In general, as cohort sizes
continue to shift within the U.S., longitudinal by-cohort analysis
will enable both identification of changes in long term participation in computing and improved understanding of factors that
impact student success after graduation.
I would like to thank Virginia Valian, Distinguished Professor, Department of Psychology,
Hunter College. Dr. Valian’s book Why So Slow? The Advancement of Women [ 12] led to the
‘aha moment’ that prompted me to start thinking differently about degree data. A surprise
message from Dr. Valian years later encouraged me to extend this work to non-STEM
fields, a project in which I am still engaged. I also thank Becky Packard, Mount Holyoke
College, and Revi Sterling, U.S. AID, as well as the anonymous reviewers, all of whom
provided thoughtful feedback. Aaron Cass, Union College, provided key R help. Finally, I
am also grateful to Eleanor Harris, Mount Holyoke College Class of 2020, who painstakingly
updated and double checked all the data, wading unafraid into the vagaries of WebCASPAR.
1. Boryga, A. The Complex Disadvantages Underlying New York City’s Specialized-High-School Dilemma. The New Yorker, June 15, 2018.
2. Computing Research Association (2017). Generation CS: Computer Science
Undergraduate Enrollments Surge Since 2006; http://cra.org/data/Generation-CS/.
Accessed 2018 July 5.
3. Corbett, C., and Hill, C. Solving the Equation: The Variables for Women’s Success in
Engineering and Computing. (Association of University Women, Washington, DC,
4. Goldin, Claudia. A Grand Gender Convergence: Its Last Chapter. American
Economic Review, 104, 5 (2014), 1–30; http://dx.doi.org/10.1257/aer.104.4.1.
5. Google Annual Diversity Report; https://diversity.google/static/pdf/Google_
Diversity_annual_report_2018.pdf. Accessed 2018 July 5.
6. National Academies of Sciences, Engineering, and Medicine. Assessing and
Responding to the Growth of Computer Science Undergraduate Enrollments. (The
National Academies Press, Washington, DC, 2018); https://doi.org/10.17226/24926.
7. National Center for Educational Statistics WebCASPAR; https://ncsesdata.nsf.gov/
webcaspar/. Accessed 2018 July 5.
8. National Science Board, Science and Engineering Indicators: Multiple years; see, for
example, https://nsf.gov/statistics/topics.cfm#t2&t2s5. Accessed 2018 July 5.
9. Summit, J. and Vermeule, B. The ‘Two Cultures’ Fallacy. The Chronicle of Higher
Education, July 1, 2018.
10. United States Census Bureau American FactFinder, ACS Demographic and Housing
xhtml?pid=ACS_ 16_ 5 YR_DP05&src=p. Accessed 2018 June 3.
11. United States Equal Employment Opportunity Commission. Diversity in High Tech.
(EEOC, Washington, DC, 2016).
12. Valian, Virginia. Why So Slow? The Advancement of Women. (MI T Press,
13. Zweben, S. and Bizot, B. 2015 CRA Taulbee Survey. Computing Research News, 28,
5 (May 2016).
Department of Computer Science
Mount Holyoke College
50 College Street
South Hadley, MA USA
DOI: 10.1145/3239261 ©2018 ACM 2153-2184/18/09 $15.00
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