Detailed data uncovers new stories, reveals gaps in CS access and participation
Note: In an effort to better identify who does and does not have access to computer science, we’re collecting and digging deeper into intersectional* data about students on Code.org and in computer science generally. See here for the full data report, which focuses on computer science participation from male and female students who identify as Hispanic, Latino, or Latina; African American or Black; Native Hawaiian or Pacific Islander; and Native American or Alaskan.
When it comes to who has access and who studies computer science, one statistic doesn’t tell the full story.
The computer science community knows that in order to close gaps, we need to be able to identify them. At Code.org, we have historically tracked participation by young women and students from underrepresented racial and ethnic groups (URG**), and this has allowed us to see the overall positive impact of the community’s collective efforts in broad strokes.
To take a step further, we’ve begun to disaggregate the race and ethnicity data we shared in the recent 2020 State of Computer Science Education: Illuminating Disparities report, and we’ve taken deep dives into intersectional data on male and female students from four underrepresented groups in computer science in this new intersectional data report.
This report, which includes data on student participation on Code.org’s platform and courses, in AP CS A and AP CS Principles exams, and in bachelor’s degrees earned in computer science, reveals a myriad of experiences and shows us a more complete picture of the K-12 CS education landscape.
Intersectionality data shows gaps in participation of older students
For example, in May, Code.org published data on trends in computer science degrees since 2000. This data showed that the number of CS bachelor’s degrees earned by women in 2018 finally reached the previous peak in 2003.
While seemingly promising, the upward trend obscures troubling gaps.
A more detailed look at the data shows that Black and African American women, Native American/Alaskan women, and Native Hawaiian/Pacific Islander women are becoming less represented. That’s because the percent of CS degrees earned by women from most racial and ethnic groups has actually remained fairly stable or declined, with the exception of Asian women. While the percent of degrees earned by white women have dropped, this group still earns the majority of CS degrees.
The percent of CS degrees earned by men also shows an increase in the representation of Asian and Hispanic/Latino men, but relatively stable representation by other racial and ethnic groups: namely, Black/African American men, Native American/Alaskan men, and Native Hawaiian/Pacific Islander men. Just like for white women, the percentage of degrees earned by white men have also dropped, but this group still earns the majority of CS degrees.
When we look at the disaggregated data on AP CS exam participation by young women, the growth is more distributed, with the exception of Native American/Alaskan female students and Native Hawaiian/Pacific Islander female students, whose representation is declining. While the trends show improvement, with 29% of participation in the AP CS exam by young women overall (up from 20% in 2014), there is still significant work to be done to spur growth and reach parity with young men.
Disaggregation of K-12 data uncovers gaps as well as encouraging trends
Recent data released in the 2020 State of Computer Science Education report shows that although 47% of public high schools teach a foundational CS course, and 73% of students attend one of these high schools, significant disparities in access exist between racial and ethnic groups. Native American/Alaskan students, Black/African American students, and Hispanic/Latino/Latina students are much less likely to attend a school that teaches CS when compared to their white and Asian peers.
Yet we are seeing some encouraging trends for younger students. When we disaggregated data on K-12 student participation on Code.org’s platform, we found:
- 1.3% of students are Native American or Alaskan (0.6% female and 0.7% male).
- 21.2% of students are Black or African American (10.2% are female, and 11% are male).
- 21% of students are Hispanic, Latino or Latina (10.1% are female and 10.8% are male).
- 0.9% of students are Native Hawaiian or Pacific Islander (0.42% are female, 0.48% are male).
- 42.7% of students are white (19.7% are female and 22.9% are male).
- 5.9% of students are Asian (2.7% are female and 3.2% are male).
According to NCES data compiled by Code.org, percentages for each racial/ethnic group are fairly close to their K-12 student population. Some groups — Black or African American, Native American/Alaskan, Native Hawaiian/Pacific Islander, and Asian students — are slightly overrepresented on Code.org than they are in the U.S. K-12 student population, while others — Hispanic, Latino or Latina, and white students — are slightly underrepresented on Code.org than they are in the general student population. Within each racial and ethic group, male and female students are almost equally represented as well.
If these students are supported in their CS studies, they can change the face of computing.
Now that we have this information, how can we act on it? Policymakers, legislators, district administrators, teachers and other CS advocates joined our state policy team and the Kapor Center for a webinar on improving equity in CS education and how to translate intention into equity-focused policy decisions. You can watch the webinar here.
A single stat can’t give us the full picture of computer science access and participation, but expanding our data collection and adopting a more holistic approach can help us more easily see where we can improve our efforts. We’ll continue to analyze the data and share more analyses and more of the story over time.
Together, we’ll bring computer science to every student in every classroom.
-Dr. Katie Hendrickson, Code.org
*”Intersectionality” refers to the way in which individuals are shaped by and identify with a vast array of cultural, structural, sociobiological, economic, and social contexts. See more information on APA.org.
**URG or underrepresented racial/ethnic groups refers to students from marginalized racial/ethnic groups underrepresented in computer science including students who are Black/African American, Hispanic/Latino/Latina/Latinx, Native American/Alaskan, and Native Hawaiian/Pacific Islander