Posts tagged ‘BPC’
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.
Insightful new report from ACCESS-CA on who is taking AP CS in California and on the challenges (quoted below):
Despite the strong outlook for the technology economy in California, there are major challenges in meeting the growing demand for skilled technology workers and preparing Californians to participate in the workforce of the future:
The lack of computer science standards, courses, and teachers and the lack of alignment between computing pathways and workforce needs. Roughly 65% of high schools in California offer no computing classes and the state has yet to develop a statewide plan for computing education.
The lack of diversity in the computing education pipeline and within the technology sector, particularly given the rapidly-increasing diversity of California’s population. 60% of California’s student population is Latinx or African American, yet these students comprise just 16% of students taking AP CS A and 15% of the technology workforce
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.
Nick Black, brilliant GT alum and (now former) Google engineer, says it like he sees it. His critique of Google and their efforts to improving diversity extend to most of Silicon Valley. If you really want a diverse workforce, open offices where there’s diversity.
Nick’s analysis (and I encourage you to read the whole post below) talks about the density of middle class Black workers. He doesn’t consider where there are Black workers who know computing. Computing education is still pretty rare in the US. Let’s use AP CS exam-taking as a measure of where there is CS education. In Michigan last year, there were 19 Black AP CS exam-takers. 11 in Missouri. None in Mississippi. There are middle class Black families in these states. They may not be getting access to CS education.
Google talks endlessly about diversity, and spends millions of dollars on the cause. My NYC office lends its prodigiously expensive square feet to Black Girls Code. We attempt to hook the recruiting pipeline up to HBCUs. We tweet about social justice and blog about the very real problem of racial inequality in America. Noble endeavors, all. It’s too bad that they’re not taking place where black people actually, you know, live.
According to census.gov’s data as of 2016, Mountain View is 2% black. In 2010, the Bay Area Census Project recorded 1,468 blacks in MTV. I saw more black people than that crossing Peachtree Street today. census.gov reports, as of 2010, blacks making up 25.1% of NYC, 9.6% of Los Angeles, and 6.1% of famously liberal San Francisco. census.gov does not provide data for Dublin or Zürich, but we can make some reasonable assumptions about those other largest Google offices, n’est-ce pas?
And let’s be honest — I doubt much of that 25.1% of NYC is centered around Chelsea.
Atlanta’s a bit down from 67% in 1990, but 54% ain’t so bad.
At the ECEP Summit, I sat with the team from North Carolina as they were reviewing data that our evaluation team from Sagefox had assembled. It was fascinating to work with them as they reviewed their state data. I realized in a new way the difficult choices that a state has to make when deciding how to make progress towards the CS for All goal. In the discussion that follows, I don’t mean to critique North Carolina in any way — every state has similar strengths and weaknesses, and has to make difficult choices. I just spent time working with the North Carolina team, so I have their numbers at-hand.
North Carolina has 5,000 students taking CS in the state right now. That was higher than some of the other states in the room. I had been sitting with the Georgia state team, and knew that Georgia was unsure if we have even one full-time CS teacher in a public high school in the whole state. The North Carolina team knew for a fact that they had at least 10 full-time high school CS teachers.
Some of the other statistics that Sagefox had gathered:
- In 2015, the only 18% of Blacks in North Carolina who took the AP CS exam passed it. (It rose to 28% in 2016, but we didn’t have those results at the summit.) The overall pass rate for AP CS in North Carolina is over 40%.
- Only 68 teachers in the state took any kind of CS Professional Development (that Sagefox could track). There are 727 high schools in the state.
- Knowing that there are 727 high schools in the state, we can put the 5,000 high school students in CS in perspective. We know that there at 10 full-time CS teachers in North Carolina, each teaching six classes of 20 students each. That accounts for 1,200 of those 5,000. 3,800 students divided by 717 high schools, with class sizes typically at 20 students, suggests that not all high schools in North Carolina have any CS at all.
Given all of this, if you wanted to achieve CS for All, where would you make a strategic investment?
- Maybe you’d want to raise that Black student pass rate. North Carolina is 22% African-American. If you can improve quality for those students, you can make a huge impact on the state and make big steps towards broadening participation in computing.
- Maybe you’d want to work towards all high schools having a CS teacher. Each teacher is only going to reach at most 120 students (that’s full-time), but that would go a long way towards more equitable access to CS education in the state.
- Maybe you’d want to have more full-time CS teachers — not just one class, but more teachers who just teach CS for the maximum six courses a year. Then, you reach more students, and you create an incentive for more pre-service education and a pipeline for CS teachers, since then you’d have jobs for them.
The problem is that you can’t do all of these things. Each of these is expensive. You can really only go after one goal at a time. Which one first? It’s a hard choice, and we don’t have enough evidence to advise which is likely to pay off the most in the long run. And you can’t achieve all of the goal all at once — as I described in Blog@CACM, you take incremental steps. These are all tough choices.