Posts tagged ‘NCWIT’
One of my favorite papers is the analysis of Stayers vs Leavers in undergraduate CS by Maureen Biggers and colleagues. This new research published by the CRA explores similar issues.
We also looked at words associated (correlated) with these two sets of words to give us context for frequently cited words. When talking about thoughts about leaving, students were particularly likely to associate “weed-out” with “classes”. They were also likely to use words such as “pretty” and “extremely” alongside “hard” and “difficult”, which sheds light on computing students’ experiences in the major. When talking about staying in their major, students cited words such as “prospect”, “security”, “stable”, and “necessary” along with the top two most commonly used words: “job” and “degree”. For instance, one student said: “[I thought about changing to a non-computing major because of] the difficulty of computing. [But I stayed for] the security of the job market.” Yet another student noted: “The competitive culture [in my computing major] is overwhelming. [But] the salary [that] hopefully awaits me [helped me stay].” Furthermore, students used the words “friends”, “family”, and “support” in association with each other, suggesting that friends and family support played a role in students’ decision/ability to stay in their computing major. As a case in point, one student noted: “The material is hard to learn! I had to drop one of my core classes and must take it again. But with some support from friends, academic advisors, more interesting classes, and a more focused field in the major I have decided to continue.”
Thanks to Greg Wilson for sending this to me. It takes a while to get to the point about computing education, but it’s worthwhile. The notion is related to my post earlier in the month about engagement and motivation.
I’d been socialised out of using computers at high school, because there weren’t any girls in the computer classes, and it wasn’t cool, and I just wanted to fit in. I wound up becoming a lawyer, and spending the better part of twenty years masquerading as someone who wasn’t part of the “tech” industry, even though basically all of my time was spent online.
And I can’t begin to tell you how common it is. So what if your first experience of “code” is cutting and pasting something to bring back replies because Tumblr took them away and broke your experience of the site.
Is that any more or less valid than any dev cutting and pasting from Stack Exchange all day long?What if your first online experiences were places like Myspace and Geocities. Or if you started working with WordPress and then eventually moved into more complex themes and then eventually into plugin development? Is that more or less valid than the standard “hacker archetype”? Aurynn gave a great talk recently about the language we use to describe roles in tech. How “wizards” became “rockstars” and “ninjas”. But also, and crucially, how we make people who haven’t followed a traditional path feel excluded. Because they haven’t learnt the “right” programming language, or they haven’t been programming since they were four, or because, god forbid, they use the wrong text editor.
A really interesting set of proposals. I saw many that are applicable to improving diversity in higher-education CS, as well as the stated goal of improving workplace diversity.
Workplace diversity is probably the biggest factor inhibiting women in computing. We used to say that females avoided CS, not knowing what it is. I think we can now fairly say that many females avoid CS because they know what it is.
This is a great ending blog post of 2016. See you in January! Happy Holidays and a Great New Year!
Over the past few months, we and our colleagues at OSTP have had conversations with dozens of Federal agencies, companies, investors, and individuals about their science and technology workforces, and we have consistently heard people express a commitment to bringing more diversity, equity, and inclusion to their workplaces. They understand the strategic importance. Yet often we found that many of the same people who want to create high-performing, innovative teams and workforces do not know the steps and solutions that others are already effectively using to achieve their diversity, equity, and inclusion goals.
In order to help accelerate this work, we have compiled insights and tips into an Action Grid designed to be a resource for those striving to create more diverse, equitable, and inclusive science and technology teams and workforces, so that we can all learn from each other.
Diversity, equity, and inclusion work is not one size fits all. We hope this set of potential actions clustered by leadership engagement, retention and advancement, hiring, and ecosystem support provides ideas and a jumping off point for conversations within your team or organization on steps that you can take to increase diversity and to make your workforce more reflective of the communities you serve, customers you sell to, and talent pools you draw from.
I found these differences fascinating, though I’m not sure what to make of them. Once leaving computing, students head to different majors with a big gender difference. Only 5% of women go into an Engineering field after CS, while 32% of men go into some form of Engineering. Why is that?
As computing departments across the U.S. wrestle with increased enrollment, it is important to recognize that not everyone who becomes a computing major stays a computing major. In 2014, CERP collected data from a cohort of U.S. undergraduate students who agreed to be contacted for follow-up surveys in 2015. While most of the students surveyed remained computing majors (96%), some students changed to a non-computing major. As shown in the graphic above, students in our sample moved to a variety of majors, and the type of new major tended to differ by gender. Most men (69%) who left a computing major switched to engineering, math/statistics, or physical science majors. On the other hand, most women (53%) tended to move to social sciences, or humanities/arts. These data are consistent with existing social science research indicating women tend to choose fields that have clear social applications, such as the social sciences, arts, and humanities. CERP’s future analyses will explore why women, versus men, say they are leaving computing for other fields.
As usual, Barbara Ericson went heads-down, focused on the AP CS A data when the 2016 results were released. But now, I’m only one of many writing about it. Education Week is covering her analysis (see article here), and Hai Hong of Google did a much nicer summary than the one I usually put together. Barb’s work with Project Rise Up 4 CS and Sisters Rise Up have received funding from the Google Rise program, which Hai is part of. I’m including it here with his permission — thanks, Hai!
Every year, I’m super thankful that Barb Ericson at Georgia Tech grabs the AP CS A data from the College Board and puts it all into a couple of spreadsheets to share with the world. 🙂Here’s the 2016 data, downloadable as spreadsheets: Overall and By Race & Gender. For reference, you can find 2015 data here and here.Below is a round-up of the most salient findings, along with some comparison to last year’s. More detailed info is in the links above. Spoiler: Check out the 46% increase in Hispanic AP exam takers!
- Overall: Continued increases in test-taking, but a dip in pass rates.
- 54,379 test-takers in 2016. This reflects a 17.3% increase from 2015 — which, while impressive, is a slower increase than 24.2% in 2015 and 26.3% in 2014.
- Overall pass rate was 64% (same as last year; 61% in 2014)
- Female exam takers: 23% (upward trend from 22% in 2015, 20% in 2014)
- Female pass rate: 61% (same as last year; 57% in 2014)
- In 8 states fewer than 10 females took the exam: Alaska (9/60), Nebraska (8/88), North Dakota (6/35 ), Kansas (4/57), Wyoming (2/6 ), South Dakota (1/26 ), Mississippi (0/16), Montana(0/9). Two states had no females take the exam: Mississippi and Montana.
- Black exam takers: 2,027 (Increase of 13% from 1,784 in 2015; last year’s increase was 21% from 1,469 in 2014)
- Black pass rate: 33% (down from 38% in 2015, but close to 2014 pass rate of 33.4%).
- Twenty-four states had fewer than 10 African American students take the AP CS A exam. Nine states had no African American students take the AP CS A exam: Maine (0/165), Rhode Island (0/94), New Mexico (0/79), Vermont (0/70), Kansas (0/57), North Dakota (0/35), Mississippi (0/16), Montana (0/9), Wyoming (0/6)
- Hispanic exam takers: 6,256 (46% increase from 4,272 in 2015!)
- Hispanic pass rate: 41.5% (up from 40.5% in 2015)
- Fifteen states had fewer than 10 Hispanics take the exam: Delaware, Nebraska, Rhode Island, New Hampshire, Maine, Kansas, Idaho, West Virginia, Wyoming, Vermont, Mississippi, Alaska, North Dakota, Montana, and South Dakota. Three states had no Hispanics take the exam: North Dakota(0/35), Montana (0/9), South Dakota (0/26).And as a hat-tip to Barb Ericson (whose programs we’ve partnered with and helped grow through the RISE Awards these last 3 years) and the state of Georgia:
- 2,033 exam takers in 2016 (this represents something like a 410% increase in 12 years!)
- New record number of African Americans and females pass the exam in Georgia again this year!
- 47% increase (464 in 2016 vs. 315 in 2015) in girls taking the exam.
- Nationally, the African American pass rate dropped from 37% to 33%. In Georgia it increased from 32% to 34%.
- The pass rate for female students also increased in Georgia from 48% to 51%.
- Only one African American female scored a 5 on the AP CS A exam in Georgia in 2016 and she was in Sisters Rise Up 4 CS (RISE supported project).
Steps Teachers Can Take to Keep Girls and Minorities in Computer Science Education | Cynthia Lee in KQED News
So glad to see Cynthia Lee’s list (described in this blog post) get wider coverage.
Last summer, Cynthia Lee, a lecturer in the computer science department at Stanford University, created a widely-circulated document called, “What can I do today to create a more inclusive community in CS?” The list was developed during a summer workshop funded by the National Science Foundation for newly hired computer science faculty and was designed for busy educators. “I know the research behind these best practices,” said Lee, “but my passion comes from what I’ve experienced in tech spaces, and what students have told me about their experiences in computer science classrooms.”
Too often students from diverse backgrounds “feel that they simply aren’t wanted,” said Lee. “What I hear from students is that when they are working on their assignments, they love [computer science]. But when they look up and look around the classroom, they see that ‘there aren’t many people like me here.’ If anything is said or done to accentuate that, it can raise these doubts in their mind that cause them to questions their positive feelings about the subject matter.”
Google’s latest reports from their collaboration with Gallup lines up with Miranda Parker’s research interests in privilege in CS education (see preview of her RESPECT 2015 paper here). I invited her to write a guest blog post introducing the new reports. I’m grateful that she agreed.
Google, in collaboration with Gallup, has recently released new research about racial and gender gaps in computer science K-12 classrooms. A lot of the report confirms what we already knew: there are structural and social barriers that limit access to CS for black, Hispanic, and female students. I don’t mind the repeated results though–it helps form an even stronger argument that there is a dearth of diversity in computing classrooms across the country.
The report does highlight interesting tidbits that may not have been as obvious before. For example, black and Hispanic students are 1.5 and 1.7 times more likely than white students to be “very interested” in learning computer science. This knowledge, combined with the data that black and Hispanic students are less likely to have access to learning CS, creates a compelling argument for growing programs focused at these groups.
Research like this continues to push the envelope of what is known about racial and gender gaps in computer science. However, it may be time to dig deeper than visible identities and explore if there are other variables that, independently or together with the other traits, create a stronger argument for why the diversity gap exists. Does socioeconomic status better explain racial gaps? What about spatial ability? These are variables that we at Georgia Tech are looking at, as we hypothesize about what can be done to level the playing field in computing.
Today, we’re releasing new research from our partnership with Gallup that investigates the demographic inequities in K-12 computer science (CS) education in two reports, Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics and Trends in the State of Computer Science in U.S. K-12 Schools. We surveyed 16,000 nationally representative groups of students, parents, teachers, principals, and superintendents in the U.S. Our findings explore the CS learning gap between white students and their Black and Hispanic peers as well as between boys and girls and confirm just how much demographic differences matter. We’re excited to share this data to bring awareness to issues on the ground in order to help expand CS education in meaningful ways.