Examining Equity From Multiple Perspectives in Computer Science

• In Event: The Multiple Dimensions of Equity: Exploring a Computer Science for All Initiative Through Different Lenses

Abstract

Objective
In this presentation, we examine the extent to which participation in CS is equitable for girls and Black and Latinx students in a districtwide CSforAll initiative. We describe the district context for the papers in this symposium, explain our framework and methods for assessing equity, and summarize findings by race, gender, and gradeband. We also discuss challenges and considerations related to measuring equity numerically.

Perspective and Methods
Data analysis can be a powerful step towards highlighting systemic inequality in education and improving student experiences (Gibb et al., 2008; Wayman et al., 2012). Our approach is informed by Fergus’s (2016) analytic framework of disproportionality, defined as “the overrepresentation… and/or the underrepresentation of a specific group in accessing… resources, programs, rigorous curriculum, and instruction relative to the presence of this group in the overall student population.”

In this study, we use three measures to determine the extent to which participation in CS is equitable for girls, Black, and Latinx students: participation rate (percentage of subgroup taking CS), composition index (participation rate compared with subgroups’ representation in the overall population), and relative participation ratio (odds of groups’ participation relative to all other groups) (see Table 1).

These metrics are common tools for measuring disproportionality in education, particularly in special education and disciplinary research (IDEA Data Center, 2014; Nishioka et al., 2017). Using these three measures allows us to look at proportionality through different lenses: since disproportionality can be hidden if only one metric is used (McIntosh et al., 2014), it is imperative that we use multiple quantifiers to form a more comprehensive statistical picture of equity (Fergus et al., n.d.).

Data sources
Our analyses and findings draw on student CS course-taking records and demographic characteristics from a large urban district for the 2016-2017 through 2020-2021 school years.

Results
Key findings include:
Participation rates for students in the three focal subgroups (girls, Black and Latinx students) were similar to their peers (boys, non-Black, and non-Latinx students), except at the high school level. 82% to 88% of schools had equitable participation rates in each subgroup.
Composition indices reveal greater disproportionality, girls being the most affected (63% to 73% of schools had equitable composition rates)
Relative participation ratios were even more disproportionate, with Black students most affected (54% to 59% of schools had equitable relative participation ratios) (see Table 2).

These findings show that generally girls, Black, and Latinx students took CS at rates similar to boys, non-Black, and non-Latinx students respectively, as exhibited by participation rates. At the same time, they were underrepresented in CS relative to their presence in the overall student population (illustrated by the composition index), demonstrating the importance of looking at equity from multiple perspectives.

Scholarly significance
Although strides have been made in recognizing the deep-seated inequities that produce gaps in CS opportunities and outcomes, these findings reflect a continuing need to address racial and gender disproportionality in CS education. Our results also illustrate the need for conversations surrounding how researchers define and quantify what is equitable—and why.

Authors

• Cheri Fancsali, Research Alliance for New York City Schools
  Presenting Author

• Janice Lee, Research Alliance for New York City Schools
  Presenting Author

• Symantha Clough, New York University
  Presenting Author