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In Event: Moscone Center West Roundtable Session 4
In Roundtable Session: Progress and Challenges in K–12 Computer Science Education: Evidence From Four States
This study informs research on computer science (CS) education for minority populations by analyzing student-level data in a way that teases apart the complexities inherent in quantifying inequities in CS education.
In research on equity in CS education, quantified measures of inequities are often presented in ways that oversimplify issues of equity and lead to misinterpretations of the data. Principal among errors of oversimplification is a failure to distinguish between disparities in access to CS education from disparities in participation in CS education. Additionally, intersectionality (Lopez et al. 2017; Schudde, 2018) is underexplored in CS equity research.
Demographic and course-taking data for all public high school students in Texas (N=1,537,073) were obtained through the Texas Education Research Center. To compare disparities in access and participation across different subpopulations of students, we computed a disparity index (DI) for each group, defined as the quotient of the rate (or proportion) for the target population over the rate for all other students:
For access to CS, the rates represent the proportions of students who attend a school that offers CS. For participation in CS, the rates represent the proportions of students who enrolled in one or more CS courses (only including those students whose school offers CS). The resulting index indicates the degree to which the target population is over or underrepresented in terms of access or participation relative to all other students (DI=1 signifies equal representation; DI<1 means underrepresentation; and DI>1 indicates overrepresentation).
Disparity indices for various subpopulations are displayed in Table 1. The natural logs of these numbers are displayed in Figure 1. Disparities in access and participation varied widely between subpopulations. For example, Asian students were four times as likely to take CS than non-Asian students, whereas Black students and Hispanic students were 1.5 times less likely to take CS than non-Blacks and non-Hispanics, respectively. Interestingly, Hispanic students were also less likely to have access to CS courses than non-Hispanic students, but Black students were equally likely as non-Black students to attend a school that offered CS.
Disparities were compounded for some students who were part of more than one minority subpopulation. For example, whereas Black students were 1.5 times less likely to take CS than non-Black students, Black females were 2.44 times less likely to take CS, and Black females in rural areas were 4.76 times less likely to take CS.
Most measures of inequities in CS education reported in the literature describe disparities in access rather than participation (e.g., Code.org Advocacy Coalition, 2018; Google Inc. & Gallup Inc., 2015; Stanton et al., 2017). This study demonstrates the importance of distinguishing between the two as most minority populations showed greater disparities in participation than in access. These findings are at odds with the relatively greater emphasis given to issues of access in equity research in CS education. Finally, this study underscores the importance of examining issues of intersectionality in order to identify areas of greater inequality.