Women and people of color are underrepresented in the U.S. computing workforce5,6,8 and in computing majors and coursework in higher education and K–12.1 Addressing this lack of diversity requires interventions in both the culture and practice of the computing industry as well as earlier in the education pipeline. The National Science Foundation has made significant investment over the past decade to broaden participation in computing (BPC) through programs such as CS10K, RPP for CS, and BPC Alliances like Expanding Computing Education Pathways (ECEP). The release in 2017 of a new high school course in the U.S. called, AP CS Principles, has resulted in some improvements in diverse participation in high school CS,3 and the Computing Research Association has reported modest improvements in the enrollment of women and students of color in introductory CS major courses.2 However, it remains to be seen whether this limited progress will result in substantive improvements to diversity in the computing industry.
Moving the needle on diverse representation in computing coursework is often the de facto, end-of-the-line measure of success in these various efforts. Less attention has been paid, however, to the entire ecosystem of CS education and the precursors or root causes of underrepresentation. The CAPE framework is a lens for assessing equity not simply as an end product, but as an integral component to each element of the systems that support computing education. The framework addresses four key components of CS education: Capacity for, Access to, Participation in, and Experience of equitable CS education (CAPE). The CAPE pyramid shown in the figure in this column is meant to illustrate how the four components of the framework interact progressively, building and relying on the previous component. For example, if students are to have equitable experiences learning CS, they must first participate in CS courses and programs. If students are to choose to participate in CS, they must first have equitable access to CS courses and programs. If schools and universities are to provide students access to CS, they must first have the capacity to offer inclusive CS instruction for all students, not just a privileged few. We posit that until we begin to address the root causes of underrepresentation in CS at each of these levels, the U.S. will continue to struggle in developing a CS education system and workforce that fully leverages the contributions of our diverse national populace.
The CAPE framework is a lens for assessing equity not simply as an end product, but as an integral component to each element of the systems that support computing education.
Equity research often examines disparities in student outcomes such Advanced Placement (AP) CS passing rates or degree completion. But these types of disparities are lagging indicators of inequity and focusing solely on such metrics ignores the varied systemic barriers to equitable outcomes that were put in place long before students enrolled in courses or completed a degree. The CAPE framework can help instructors, researchers, practitioners, and policymakers to examine the ecosystem in which K–16 CS education is embedded and create a deeper understanding of the precursor conditions and leading indicators of systemic inequities in the experience of CS for historically underrepresented populations, including women, students of color, students from families with limited financial resources, students with disabilities, and students who live in rural communities. Each of the four levels of the framework carries important implications for how we think about, measure, and ultimately impact equity in CS education.
Capacity for CS Education
Capacity for CS education refers to the availability of resources to support and maintain high-quality CS instruction. These resources may include faculty, funding, and policies that make implementing CS instruction possible and inclusive. At this level, researchers can examine equity through questions such as:
- High School
- Are there differences based on student socioeconomics in the proportion of schools that employ certified CS teachers?
- How does a shortage of CS faculty impact opportunity for underrepresented students to major in CS?
- How does faculty capacity impact policies around access to CS course-work for non-majors?
Each of these questions around capacity to deliver CS instruction has implications for the eventual equitable access to and participation in CS both in K–12 and in higher education. For example, if trained and certified teachers are disproportionately employed by wealthier school districts, the capacity of schools serving primarily low-income students to provide high quality CS courses will be severely constrained. Regarding undergraduate computing, if an institution is struggling to serve all students who hope to major in CS due to a shortage of faculty, are admission filters in place that only accept students with prior experience in CS into the major? If so, how does that impact students from low-income households who are less likely to attend high schools that offer CS or have access to CS experiences outside of school? How do these capacity issues exacerbate the challenges of diversifying undergraduate CS opportunities? Each of these questions about capacity address issues that can impact, at a very early stage, whether traditionally marginalized students have opportunities to engage in CS education.
Access to CS Education
At the high school level, access can be operationalized as attending a school that offers CS courses. At the undergraduate level, access can address both access to CS as a CS major as well as access to CS coursework for non-majors. Equity in access to CS can be explored by examining questions such as:
- High School
- How do rural and urban/suburban schools differ in terms of offering CS courses?
- What are the barriers and facilitators for community college transfers into undergraduate CS majors at four-year institutions? How do these barriers/facilitators impact diversity at four-year institutions?
- How do majors in other STEM fields access CS courses? How does access to CS coursework for non-majors impact learning opportunities in CS for males and females differentially?
As of 2020, only 47% of U.S. high schools offered a single computer science course. Moreover, this limited access is not equitably distributed across diverse populations.1 Even when CS courses are offered, access to multiple courses or more advanced CS course-work is often highly correlated with affluence. How does this lack of access ultimately impact diversity in CS majors in college and industry?
With respect to undergraduates, low-income students and students of color are disproportionately enrolled in community colleges (as opposed to four-year universities). In the U.S., 31% of undergraduate students from families with the lowest income (lowest quartile) enrolled in community colleges compared to only 17% of undergraduates in the highest income quartile.7 Similarly, 41% of Black undergraduates and 48% of Hispanic/Latino undergraduates enrolled in community colleges first compared to 34% of White undergraduate students.4 Because of this, policies at four-year institutions that effectively prohibit community college transfers into CS majors are likely to exacerbate existing disparities in CS enrollment.
Participation in CS Education
We operationalize participation as enrolling in CS courses when offered by the school, either at the high school or college level. Examples of questions that address participation in CS education include:
- High School
- Are there enrollment disparities in advanced CS courses based on gender, geography, socioeconomic status, or ethnicity?
- Are there disparities in CS majors based on student gender or ethnicity?
- Are male non-CS majors more likely than female non-CS majors to enroll in CS courses?
Undergraduate STEM majors in fields such as biological sciences are dominated by females. Given the increasing expectation that competency in these fields requires experience with computational and analytical tools grounded in computer science, the need to provide access for non-CS STEM majors in particular has implications for gender equity that should be examined.
Experience of CS Education
Experience of CS education encompasses the various outcomes of participating in CS. The overarching questions here are: When students participate in CS, do they have equitable learning experiences? What have they learned? Are their experiences culturally and personally relevant? Are students successful academically? Do all students feel welcome in the class?
Policies at four-year institutions that effectively prohibit community college transfers into CS majors are likely to exacerbate existing disparities in CS enrollment.
Additional questions to assess student experiences of CS education include:
- High School
- Do course curricula explicitly address issues of equity?
- What is the relationship between AP test outcomes and gender and ethnicity?
- Do passing rates or grades differ between student subpopulations based on demographics that should not be correlated with academic achievement?
- Do all students feel included and accepted in CS courses? Are females and students of color more likely to drop out of CS majors?
Student performance measures such as course grades, degree attainment, and AP test outcomes are one way to measure equitable outcomes for students, but providing truly equitable experiences must go beyond these simple outcome measures as it is possible to have parity in these types of outcomes while still failing to create an environment where all students feel they belong, instruction is inclusive, and diverse perspectives are valued explicitly. To achieve this inclusivity, instructors must attend to the explicit and implicit policies, classroom culture, and instructional strategies that either support or discourage under-represented students in CS courses.
We argue that efforts to diversify the computing profession must use an ecosystems approach to account for the myriad contextual factors, institutional polices, and unexamined practices that influence the entire CS education pipeline. The CAPE framework can be a useful tool for examining some of the root causes that lead to a lack of diversity in computing. "If you build it, they will come" may work well for baseball movies, but diversifying computing education and the computing profession will require a more comprehensive examination of all levels of the CS ecosystem and the ways in which issues of equity, diversity, and inclusion play out to either exacerbate or mitigate existing disparities. The CAPE framework can be a road map for examining both the leading indicators of equity in CS, such as capacity and access, and the lagging indicators of student outcomes. Individuals committed to broadening participation in the computing field must be prepared to address each of these interrelated levels if we hope to build a more diverse computing profession.
1. Code.org, CSTA, and ECEP Alliance 2020 State of Computer Science Education: Illuminating Disparities. (2020); https://bit.ly/3m6hHMh
3. Ericson, B. AP CS A and CSP Data. Computing for Everyone. (Jan. 13, 2020); https://bit.ly/3a3lGqA
4. Ginder, S.A., Kelly-Reid, J.E., and Mann, F.B. Enrollment and Employees in Postsecondary Institutions, Fall 2017; and Financial Statistics and Academic Libraries, Fiscal Year 2017: First Look (Provisional Data) (NCES 2019-021rev). U.S. Department of Education. (2018); https://bit.ly/2W8D7xK
5. Google Inc. and Gallup Inc. Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics. (2016); http://goo.gl/PG34aH
6. Hill, C., Corbett, C., and St. Rose, A. Why So Few?: Women in Science, Technology, Engineering, and Mathematics. American Association of University Women, (2010); https://bit.ly/2KhNrRD
7. Ma, J. and Baum, S. Trends in Community Colleges: Enrollment, Prices, Student Debt, and Completion. College Board, 2016; https://bit.ly/3oJ8tr3
8. Nelson, B. The data on diversity. Commun. ACM 57, 11 (Nov. 2014), 86–95; https://doi.org/10.1145/2597886
The development of the CAPE Framework was supported in part by a Google CS-ER Grant to The University of Texas at Austin.
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