Archive for February 17, 2020

Importance of considering race in CS education research and discussion

I was talking with one of my colleagues here at Michigan about the fascinating recent journal article from Tim Weston and collaborators based on NCWIT Aspirations award applicants, which I blogged about here. I was telling him about the results — what correlated with women’s persistence in technology and computing, and what didn’t or was negatively correlated.

He said that he was dubious. I asked why. He said, “What about the Black girls?”

His argument that the NCWIT Aspirations awards tends to be white and tends to be in wealthy, privileged school districts. Would those correlations be the same if you looked at Black women, or Latina women?

I went back to the Weston et al. paper. They write:

Although all respondents were female, they were diverse in race and ethnicity. Because we know that there are differentiated experiences for students of color in secondary and post-secondary education in the US, and especially women of color, we wanted to make sure we captured any differences in outcomes in our analysis. To do so, we created a variable called Under-represented Minority in Computing (URMC) status that grouped students by race/ethnicity. URMC indicated persons from groups historically under-represented in computing–African-American, Hispanic, or Native American. White, Asian and students of two or more races were coded as “Majority” in this variable. Unfortunately, further disaggregation by specific race/ethnicity was not possible due to low numbers. Thus, even though the numbers in the respondent pool were not high enough to disaggregate by specific race/ethnicity, we could still identify trends by over-representation and under-representation.

18% of their population was tagged URMC. URMC was included as a variable in their analyses, and their results suggest that being in the URMC group did not influence persistence significantly. If I understand their regressions right, that doesn’t tell us if the correlations were different by race/ethnicity. URMC wasn’t a significant factor in the outcomes, but that is not the same as thinking that those other variables differ by race and ethnicity. Do Black females have a different relationship with video games or with community than white females, for example? Or with Latina students?

While the analysis did not leave race out of the analysis entirely, there was not enough diversity there to answer my colleague’s question. I do agree with the authors that we would expect differentiated experiences. If our analysis does not include race, can we account for the differentiated experiences?

It’s hard to include race in many of our post-secondary CS ed analyses simply because the number of non-white and non-Asian students is so small. We couldn’t say that Media Computation was successful with a diverse student body until University of Illinois Chicago published their results. Georgia Tech has few students from under-served groups in the CS classes we were studying.

There’s a real danger that we’re going to make strong claims about what works and doesn’t work in computer science based only on what works for students in the majority groups. We need to make sure that we include race in our CS education discussions, that we’re taking into account these differentiated experiences. If we don’t, we risk that any improvements or optimizations we make on the basis of these results will only work with the privileged students, or worse yet, may even exacerbate the differentiated experiences.

February 17, 2020 at 7:00 am 7 comments


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