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Computer Science Achievement Gap in the United States: Gender, Race, Ethnicity, and Ability

This article explores the achievement gap in computer science education in the United States, focusing on factors such as gender, race, ethnicity, and ability. It examines enrollment trends, AP exam participation, bachelor's and master's degrees awarded, as well as the challenges faced by underrepresented groups. It also highlights the unique equity challenges in the field of computer science in the USA.

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Computer Science Achievement Gap in the United States: Gender, Race, Ethnicity, and Ability

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  1. Computer Science in the United States: Achievement Gap based on gender, race, ethnicity, and ability Betsy DiSalvo Assistant Professor School of Interactive Computing

  2. Example of Booming Enrollment Majors applied to among University of Washington Freshmen https://www.geekwire.com/2014/analysis-examining-computer-science-education-explosion/

  3. Computer science unique equity challenges in the USA 1986 Robert E. Lee Jr. High School, Florida 1981 Hobart High School, Indiana 1979 Dobie High School, Texas 1986, Clifton High School, New Jersey

  4. Overview • Intro • High School Advanced Placement Test • Bachelor’s Degree • Master’s Degree • PhD • Tech sector • Moving Forward

  5. Graph of AP CS A Exam-takers over time

  6. Exam takers of AP CS A 2012 • 24,782 examtakers totalwith an overall pass rate was 63.2%. Females • The number of female exam takers was 4,635 up from 4,000 the year before. • However, the percentage of exam takers who were female (18.7%) was lower than the previous year (18.9%) . • The female pass rate was 56.4%. • Ericson, B. and Guzdial, M. "Measuring demographics and performance in computer science education at a nationwide scale using AP CS data." Proceedings of the 45th ACM technical symposium on Computer science education, 2014.

  7. Exam takers of AP CS A 2012 • 24,782 examtakers totalwith an overall pass rate was 63.2%. Females • The number of female exam takers in 2012 was 4,635 up from 4,000 the year before. • However, the percentage of exam takers who were female (18.7%) was lower than the previous year (18.9%) . • The female pass rate was 56.4%. Blacks • The number of Blacks was 1,014 (4.09%) up from 893 the previous year. • The Black pass rate was 27.3%. In contrast, the white pass rate was 66.4%. • Ericson, B. and Guzdial, M. "Measuring demographics and performance in computer science education at a nationwide scale using AP CS data." Proceedings of the 45th ACM technical symposium on Computer science education, 2014.

  8. Exam takers of AP CS A 2012 • 24,782 examtakers totalwith an overall pass rate was 63.2%. Females • The number of female exam takers in 2012 was 4,635 up from 4,000 the year before. • However, the percentage of exam takers who were female (18.7%) was lower than the previous year (18.9%) . • The female pass rate was 56.4%. Blacks • The number of Blacks was 1,014 (4.09%) up from 893 the previous year. • The Black pass rate was 27.3%. In contrast, the white pass rate was 66.4%. Hispanic • The number of Hispanics was 1,919 (8.03%) up from 1,752 the previous year. • The overall pass rate was 63.2%. The Hispanic pass rate was 39.8%. • Ericson, B. and Guzdial, M. "Measuring demographics and performance in computer science education at a nationwide scale using AP CS data." Proceedings of the 45th ACM technical symposium on Computer science education, 2014.

  9. Bachelor’s Degrees Awarded by Gender 2014 • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  10. Bachelor’s Degrees Awarded by Ethnicity • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  11. Master’s Degrees Awarded by Gender • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  12. Master’s Degrees Awarded by Ethnicity • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  13. PhDs awarded by gender • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  14. PhDs awarded by ethnicity • Zweben, Stuart, and Betsy Bizot. "2015 Taulbee Survey." Computing 28.5 (2016).

  15. Women Leaving the Tech Sector Women are more than twice as likely to quit the tech industry as men (41% vs 17%) Estimated 52% eventually leave tech sector St. Fleur, N. (2014). Many Women Leave Engineering, Blame The Work Culture. National Public Radio. Retrieved from www.npr.org/sections/alltechconsidered/2014/08/12/339638726/many- women-leave-engineering-blame-the-work-culture Hewlett, S.A., Sherbin, L., Dieudonné, F., Fargnoli, C., & Fredman, C. (2014). Athena factor 2.0: Accelerating female talent in science, engineering & technology. Retrieved from http://www.talentinnovation.org/assets/Athena-2-ExecSummFINAL-CTI.pdf

  16. Women Leaving the Tech Sector Workplace bias (30%)* • Macroaggressions • Openly hostel environments • Unicorn effect • Sexual Harassment Stereotype threat Isolation Balancing Work and Life (30%)* *Joseph, Jeffrey. "What are the Top Factors that Prevent Women and Racial/Ethnic Minority Employees From Leaving Engineering Professions or the Tech Industry?." (2016). Randomthroawayacount. 2014. [Serious] Women of reddit, what is a sexist thing guys do that they don’t notice? And vice versa. Retrieved from http://www.reddit.com/r/AskReddit/comments/2entqn/

  17. African Americans in CS and CE High percentage of African American males earn a BS in CS in relationship to those attending college – slightly hire than white peers • Predictors seem to be a degree program that isn’t a stepping stone to graduate school or professional degree • High earning potential of CS and CE • Potential to be a way to encourage more African American males to persist in school Lower percentage of African American females earn a BS in CS in relationship to those who attend college. • Double bind of race and gender • Lack of peers • All the same issues that face women in tech • Ong, Maria, et al. "Inside the double bind: A synthesis of empirical research on undergraduate and graduate women of color in science, technology, engineering, and mathematics." Harvard Educational Review 81.2 (2011): 172-209.

  18. Moving Forward Increase access to computer science at an earlier age • Informal Learning • Code.org • Parents • Formal Education - CS4All • Create interventions that consider intersectionality • Create intervention that have sustainable, longitudinal impact Retention • Create Community • Change the White and Asian American men in computing • Remove cultural acceptance of macroaggressions, racism, sexism and ableism • Require teachers to reflect on practices that privilege dominate groups • More active learning • Less lecture • More socially contextualized projects

  19. Betsy DiSalvo, Assistant Professor Bdisalvo@cc.gatech.edu Questions?

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