Posts tagged ‘NCWIT’
It’s great to hold this woman up as a role model, but isn’t it a shame that she is so unusual. Only girl in AP CS? One of only five women in CS at Iowa State?
Cassidy Williams was the only girl in her AP computer science class at Downers Grove South High School.
Now, she is one of only five women majoring in computer science, along with 57 men, in the 2014 graduating class at Iowa State University.
It’s a trend the 21-year-old Downers Grove native hopes to help change for future girls studying computer science.
“If we don’t have women in computer science, we’re only seeing half the picture,” Cassidy said. “We need to have women in the computing workforce to bring their diverse perspectives to a development team, thus creating the best products.”
The Economist has an article in a recent issue that’s leading to lots of discussion: Are we making mistakes with science? Can scientists really tell the good stuff from the bad stuff? Are we really making sure that our key results are replicable?
One of the topics that they explore is “priming” research.
“I SEE a train wreck looming,” warned Daniel Kahneman, an eminent psychologist, in an open letter last year. The premonition concerned research on a phenomenon known as “priming”. Priming studies suggest that decisions can be influenced by apparently irrelevant actions or events that took place just before the cusp of choice. They have been a boom area in psychology over the past decade, and some of their insights have already made it out of the lab and into the toolkits of policy wonks keen on “nudging” the populace.Dr Kahneman and a growing number of his colleagues fear that a lot of this priming research is poorly founded. Over the past few years various researchers have made systematic attempts to replicate some of the more widely cited priming experiments. Many of these replications have failed. In April, for instance, a paper in PLoS ONE, a journal, reported that nine separate experiments had not managed to reproduce the results of a famous study from 1998 purporting to show that thinking about a professor before taking an intelligence test leads to a higher score than imagining a football hooligan.
Stereotype threat is a kind of priming effect. Stereotype threat is where you remind someone of a negative stereotype associated with a group that the person belongs to, and that reminding impacts performance. The argument is that stereotype threat might be leading to the gaps between races and genders.
A common situation of stereotype threat for girls and women is when they are tested on their knowledge of math or science. The Educational Testing Services performed an experiment to see if girls performed better or worse on a math exam if they were asked their gender either before or after the exam. Researchers found that the group of girls who were asked their gender before the exam scored several points lower than the boys, while girls who were asked their gender after the exam scored on par with the boys.
If there are questions being raised about “priming” research, I got to wondering about whether anyone was checking the reliability of the stereotype threat research. They are, and it’s not promising.
Men and women score similarly in most areas of mathematics, but a gap favoring men is consistently found at the high end of performance. One explanation for this gap, stereotype threat, was first proposed by Spencer, Steele, and Quinn 1999 and has received much attention. We discuss merits and shortcomings of this study and review replication attempts. Only 55% of the articles with experimental designs that could have replicated the original results did so. But half of these were confounded by statistical adjustment of preexisting mathematics exam scores. Of the unconfounded experiments, only 30% replicated the original. A meta-analysis of these effects confirmed that only the group of studies with adjusted mathematics scores displayed the stereotype threat effect. We conclude that although stereotype threat may affect some women, the existing state of knowledge does not support the current level of enthusiasm for this as a mechanism underlying the gender gap in mathematics. We argue there are many reasons to close this gap, and that too much weight on the stereotype explanation may hamper research and implementation of effective interventions
As I dug into this further, I found that there has been a lot of misinterpretation of the research on stereotype threat. There is already a gap between genders and between races on many of these tests. If you remind someone of a negative stereotype, that can make the gap larger. But if you don’t remind someone of the stereotype, the gap is just the same. The gap was already there. If you adjust the scores so that they’re the same pre-test (that’s the “statistical adjustment of the preexisting mathematics exam scores” referenced above), you find no difference absent the threat invocation. The measured impact of stereotype threat has worked when the test-takers are consciously aware of the threat. The blog post cited below goes into alot of detail into the efforts to replicate, the problems with interpreting the result, and how the methodology of the experiment matters.
Thus, rather than showing that eliminating threat eliminates the large score gap on standardized tests, the research actually shows something very different. Specifically, absent stereotype threat, the African American–White difference is just what one would expect based on the African American–White difference in SAT scores, whereas in the presence of stereotype threat, the difference is larger than would be expected based on the difference in SAT scores.
I come away with the opinion that stereotype threat is real, but it needs more experimentation to understand just how reliable the effect is and what triggers it. It’s probably a small impact, more like the impact of general test anxiety than an explanation for much of the gaps between genders and races.
Interesting blog from Andrew Williams, reflecting on his interactions with Steve Jobs about diversity at Apple.
Two years later, I wonder if Steve would be happy with the progress made at Apple and other major computing companies like Google, Facebook, Amazon, Microsoft, and Twitter. The issue of diversity in engineering and computing goes way beyond just finding students in college. It starts when they are born, nurtured, educated in their families and communities. If children are not valued and exposed to technology, engineering, and science when they are young and encouraged throughout their developing years, they will not be equipped to meet the opportunities of meeting a Steve Jobs or a Marissa Mayer, Yahoo CEO, in the cafe to talk about issues of engineering, computing, and diversity.
In one week, I found two articles about all female programming academy (first quote and link below) and engineering high school class (second link and quote below). Both of them talk about issues of sexism and intimidation that they hope an all-female cohort will help to avoid.
Why just for women? Because some hiring managers, in response to these statistics, are particularly interested in hiring women, Worthy says. “The need is very top-of-mind,” she says. In addition, there are other training options for men, though they aren’t free like the Ada Developers Academy is, she admits. Moreover, some women and girls have encountered sexism in school and training programs themselves; an all-female class may forestall that problem.
The Ada Developers Academy isn’t the only such effort to challenge this trend. A number of other parallel training opportunities for women are also springing up, some for students and some for working women, to help fill jobs and address the growing gender gap in programming.
Seventeen female students are enrolled in Wisconsin’s first high school class aimed at women in engineering.
Women comprise more than 20% of engineering school graduates but only 11% of practicing engineers, according to the National Science Foundation. Only about 30% of the 14 million Americans who work in manufacturing are women, a study from the National Women’s Law Center noted.
“If we are going to have any hope of replacing all of the retiring baby boomers, we have to get women involved,” Moerchen said.
“It’s a pretty wide gender gap,” Moerchen said, adding that only about three of 35 students in computer-aided machining courses are female.
“The data show that female students are easily intimidated by technology and engineering classes that are traditionally dominated by male students,” Moerchen said.
After researching programs in other states, the Kewaskum teachers said they believed they could create an engineering class specifically for girls that would prepare the students for advanced courses.
An interesting though somewhat sad story from a school-age girl (probably high school level?) about why she’s not interested in Information and Communications Technology. A good part of her story has to do with self-efficacy — how do you get better at this?
Throughout my first two years, my ICT assessment levels have always been much lower than other subjects and this can put you in the frame of mind that you’re bad at ICT, and if there are other subjects you’re better at, surely it’s simpler to take them for GCSE. And of course, IT is not the ideal job for me if I can’t even pass an exam.
Unless computing was made a compulsory subject like a language or maths, I don’t think this will change. To improve your English you can read, and to improve your ICT there are particular websites, but I certainly would not spend time on them and I’m sure my friends wouldn’t either
My colleague Mary Jean Harrold lost her battle with cancer last week. Mary Jean worked hard for women in computing, and was always a strong supporter of efforts to improve and broaden computing education. The classes I’m now teaching in TA preparation were originally proposed by Mary Jean and a committee she chaired — she thought it was important that we produce PhD’s who know something about teaching and communicating ideas. She will be missed.
Harrold also was a fierce advocate for women and minorities in computing fields. At Georgia Tech, she was the NSF ADVANCE Professor in the School of Computer Science for 10 years, from 2001 to 2011; she also was a member of the Leadership Team and Director of the Georgia Tech Hub for the National Center for Women and Information Technology (NCWIT). Outside Georgia Tech, Harrold served many years (several as co-chair) on the CRA’s Committee on the Status of Women in Computing Research (CRA-W), whose goal is to increase the number of women in computer science research and education. She was instrumental in establishing the biennial Software Engineering Educators’ Symposium (SEES), which aims to forge ties between faculty at minority-serving colleges and software engineering researchers.
The Brogrammer Effect: Women Are a Small (and Shrinking) Share of Computer Workers – Jordan Weissmann – The Atlantic
Good to see The Atlantic caring about this. I don’t see much evidence offered that it’s a “Brogrammer” effect, though, other than the title.
So here’s why everybody, whether or not they’ve ever given a hint of thought to brogrammers and the social mores of Silicon Valley or Alley or Beach, should care. A large part of the pay gap between men and women boils down to the different careers they pursue. And STEM jobs, with their generally high salaries, are an especially important factor. Meanwhile, as the Census notes, computer fields make up about a half of STEM employment. So when you talk about women retreating from computer work, you’re talking about a defeat for their financial equality.
I’m teaching a TA preparation course at Georgia Tech this semester. My students are PhD students who are learning how to be teaching assistants. In a session on dealing with classroom behavior and FERPA, I introduced peer instruction — I put scenarios up on the screen with four or five choices of responses, and the students used clickers to choose what they thought was the appropriate response. One of the scenarios was:
In a class discussion, a student starts yelling at another student: “You moron! C# is a terrible language for that! You should use C++!” What do you do?
I had a distractor that collected a surprising number of votes: “Just let it go – that’s the way CS students are.” And after the discussion period — that one still got some votes. The expectation that “That’s just the way CS students are” is surprisingly pervasive. Computer science teachers need to stand up to it, to demand change in culture and expectations.
Later in my class, the students are reading chapters of Diana Franklin’s new book.
So, you see, I was all too familiar with what my daughter was going through, but I was unprepared for the harassment to start in high school, in her programming class.I consulted with friends — female developers — and talked to my daughter about how to handle the situation in class. I suggested that she talk to you. I offered to talk to you. I offered to come talk to the class. I offered to send one of my male friends, perhaps a well-known local programmer, to go talk to the class. Finally, my daughter decided to plow through, finish the class, and avoid all her classmates. I hate to think what less-confident girls would have done in the same situation.My daughter has no interest in taking another programming class, and really, who can blame her.
NYTimes just had a nice article about the Georgia Tech online Masters degree program based in MOOCs. I’m glad that the OMS (Online MS) group is getting that kind of attention.
For my research interests, I’m more excited about the alternative to MOOCs described below. I am not well-versed in feminist perspectives, but I appreciate the values that are informing Anne Balsamo’s design and do see that this approach has a greater chance of drawing in women (based on research like Joanne Cohoon’s) than traditional MOOCs.
At participating colleges, professors will base their own courses on each weekly theme, sharing course materials and assignments, but customizing them for their own students. The courses will vary, as some are undergraduate and some are graduate, and the institutions see list at right vary widely by mission and geography — including institutions in Australia, Britain, Canada and the United States. The class sizes will be between 15 and 30 students each, decidedly non-massive. “There is another pedagogical commitment here,” Balsamo said. “Who you learn with is as important as what you learn. Learning is a relationship, not just something that can be measured by outcomes or formal metrics.”
In September, I’m going to Dagstuhl for a workshop on “Collaboration and Learning through Livecoding.” It’s mostly people who “livecode” in the musical sense and put on “AlgoRaves“, but will also include people who talk about “livecoding” in the CS education sense. I’m really looking forward to it.
But I was surprised when I looked at the list of attendees: There is only one woman that I can identify by name. I figured that music would be a more welcoming community than computer science, so there would be lots of women coming to livecoding from the music side. I asked my colleague, Jason Freeman, in GT’s School of Music, who told me that music composition is even more male than computer science! He told me about the NYTimes piece (linked below) that talks about the severe gender disparity in music composition. Jason said that GT’s Masters in Music Technology program has had 60-70 graduates so far — and only six women cumulatively (across all years).
Why is that? What’s common about music composition and computer programming that both careers would have so few women, when fields like medicine, law, and veterinary science used to be male-dominated but are now more gender-balanced?
Kirsten acknowledged that women have had an agonizingly difficult time gaining a creative foothold in classical music, whose repertory is male-dominated to a stifling degree. But, in light of the international renown of such figures as Kaija Saariaho, Unsuk Chin, and Sofia Gubaidulina, she argued that the “woman composer” no longer required special pleading or affirmative action. “Neither art nor artist is served by segregation—even if it’s well intended,” Kirsten wrote. Rather than going out of their way to boost female composers, she suggested, programmers should embrace only works that speak to them strongly, trusting that women will continue to advance.
I get to teach our Media Computation in Python course, on Georgia Tech’s campus, in Spring 2014. I’ve had the opportunity to teach it on study abroad, and that was wonderful. I have not had the opportunity to teach it on-campus since 2007. Being gone from a course for seven years, especially a big one with an army of undergraduate TA’s behind it, is a long time. The undergraduate TA’s create all the assignments and the exams, in all of the introductory courses in the College of Computing. Bill Leahy, who is teaching it this summer semester, kindly invited me to meet with the TA’s in order to give me a sense for how the course works now.
It’s a very different course than the one that I used to teach.
- I mentioned the collage assignment, which was one of the most successful assignments in MediaComp (and shows up even today in AP CS implementations and MATLAB implementations). Not a single TA knew what I was talking about.
- The TA’s complained to me about Piazza. ”Nobody posts” and “I always forget that it’s there” and “It seems to work in CS classes, but not for the other majors.” I told them about work that Jennifer Turns and I did in 1999 that showed why Piazza and newsgroups don’t work as well as integrated computer-supported collaborative learning, and how that work led to our development of Swikis. Swikis were abandoned many years ago in MediaComp, even before the FERPA concerns.
- Sound is mostly gone. Students have to play a sound in one assignment based on turtle graphics. Students never manipulate samples in a sound anymore.
- I started to explain why we do what we do in MediaComp: Introducing iteration as set operations, favoring replicated code over abstraction in the first half of the semester, avoiding else. They thought that those were interesting ideas to consider adding to the course. I borrowed a copy of the textbook from one of them, and read them part of the preface about Ann Fleury’s work. Lesson: Just because you put it in the book and provide the citation, doesn’t mean that anybody actually reads it, even the TA’s.
It’s a relevant story because I’m presenting a paper at ICER 2013 on Monday 12 August that is a 10 year retrospective on the research on Media Computation. (I’m making a preview version of the paper available here, which I’ll take down when the ACM DL opens up the ICER 2013 papers.) It was 10 years ago that we posted our working document on creating MediaComp and our 2002 and 2003 published design papers, all of which are still available. We made explicit hypotheses about what we thought Media Computation would do. The ICER 2013 paper is a progress report. How’d we do? What don’t we know? In hindsight, some seem foolish.
- The Plagiarism Hypothesis: We thought that the creative focus of MediaComp would reduce plagiarism. We haven’t done an explicit study, but if we found a difference with statistical significance, it would be meaningless. Ten years later, still lots of academic misconduct.
- The Retention Hypothesis: Perhaps our biggest win — students are retained better in MediaComp than traditional classes, across multiple institutions. The big follow-up question: Why? Exploring that question has involved the work of multiple PhD students over the last decade, helping us understand contextualized-computing education.
- The Gender Hypothesis: We designed MediaComp based on recommendations from people like Jane Margolis and Joanne Cohoon on how to make an introductory CS course that would be successful with women. Our evidence suggests that it worked, but we don’t actually know much about men in the class.
- The Learning Hypothesis: We hoped that students would learn as much in MediaComp as in our traditional CS1 class. Answering that question led to Allison Elliott Tew’s excellent work on FCS1. The bottom line, though, is that we still don’t know.
- The More-Computing Hypothesis: We thought that non-CS majors taking MediaComp would become enlightened and take more CS classes. No, that didn’t really happen, and Mike Hewner’s work helped us understand why not.
There are two meta-level points that I try to make in this paper.
- The first is: Why did we think that curriculum could do all of this, anyway? Curriculum can only have so much effect. There are lots of other variables in student learning, and curriculum only touches some of those.
- The second is: How did we move from Marco Polo to theory-building? Most papers at SIGCSE have been classified as Marco Polo (“We went here, and we saw that.”) MediaComp’s early papers were pretty much that — with the addition of explicit hypotheses about where we thought we’d go. It’s been those explicit hypotheses that have driven much of the last 10 years of work. Understanding those hypotheses, and the results that we found in pursuit of those hypotheses, have led us to develop theory and to support a broader understanding of how students learn computing.
Lots of things change over 10 years, and not always in positive directions. Good lessons and practices of the past get forgotten. Sometimes change is good and comes from lessons learned that are well worth articulating and making explicit. And sometimes, we got it plain wrong in the past — there are ideas that are worth discarding. It’s worth reflecting back occasionally and figuring out how we got to where we are.
I was sent links to the She++ documentary by several people. It’s a nicely done documentary on the issues of women in undergraduate computing at Stanford.
The Twitter account for She++ posted the video link with the comment, “Show this to your daughters!” Others in social media are suggesting that this should be seen by all girls to encourage them in CS. This is a great video for describing the students’ experience. I’m not sure it works as a recruiting tool.
In some of our GaComputes work, we found that female workshop leaders were more likely to warn the girls in their computing workshops, “Now, I know that this is hard, but you’ll be able to do something cool here.” The male leaders were more likely to just say, “This is so cool!” The female leaders tended to get declines in interest in computing — girls left the workshop saying more often, “Computing is hard” and “Girls can’t do computing.” The male leaders tended to get positive improvement in attitudes. Notice that the male leaders didn’t say it was easy. They didn’t lie. They just emphasized the benefit.
This video feels honest and heartfelt. The women interviewed say things like, “It was really difficult” and “I didn’t feel I fit in.” And when they speak to the camera, they say, “Girls, it will be hard at first, but it will get better.” I believe that the speakers are being honest, but I worry that those descriptions might trigger stereotype threat. Does telling girls about imposter syndrome make it less likely? Some pretty amazingly successful people suffer from imposter syndrome.
I recommend that the video be seen by all computer science teachers, especially teachers of undergraduates. It’s important for teachers to know about the experience of women in their classrooms. I don’t recommend it for girls that you hope to recruit into computing.
We have very few AP CS teachers in the United States — about 1 for every 12 high schools, and they’re not evenly distributed. I do get that an AP CS MOOC may make it more available to more students. Still, I’m not too excited about a MOOC to teach AP CS. AP CS is already overwhelmingly white and male. The demographic data from existing CS MOOCs is even more white and male than our face-to-face classes. I can’t see how an AP CS MOOC will improve diversity, and we have a desperate need to improve diversity.
But beyond that — Rupert Murdoch?!? Really? Why is he interested in CS education? I do note that he is starting out with a monetizing scheme. Want your questions answered? $200 per student per year. I do see how this AP CS MOOC may deal with some of the shortcomings of other MOOCs, and may even be better with diversity than existing MOOCs, because of the availability of direct support — at a price.
Now, Rupert Murdoch, the billionaire media mogul behind News Corp., wants to do something about the lack of computer science education. Murdoch’s Amplify education unit plans to launch a new advanced placement online computer science course this fall, taught by longtime high-school instructor Rebecca Dovi.
The course is described as a MOOC, short for massive open online course. It is free to high school students, though additional resources will be made available for $200 per student. It is geared toward those who want to take the computer science AP exam in 2014.
An interesting study suggesting that role models and how they’re described (in terms of their achievements, or in terms of their struggles) has an interaction with students’ stereotypes about scientists and other professionals in STEM fields. So there are not just cognitive benefits to learning from failure, but there are affective dimensions to focusing on the struggle (including failures) and not just the success.
But when the researchers exposed middle-school girls to women who were feminine and successful in STEM fields, the experience actually diminished the girls’ interest in math, depressed their plans to study math, and reduced their expectations of future success. The women’s “combination of femininity and success seemed particularly unattainable to STEM-disidentified girls,” the authors conclude, adding that “gender-neutral STEM role models,” as well as feminine women who were successful in non-STEM fields, did not have this effect.
Does this mean that we have to give up our most illustrious role models? There is a way to gain inspiration from truly exceptional individuals: attend to their failures as well as their successes. This was demonstrated in a study by Huang-Yao Hong of National Chengchi University in Taiwan and Xiaodong Lin-Siegler of Columbia University.
The researchers gave a group of physics students information about the theories of Galileo Galilei, Issac Newton and Albert Einstein. A second group received readings praising the achievements of these scientists. And a third group was given a text that described the thinkers’ struggles. The students who learned about scientists’ struggles developed less-stereotyped images of scientists, became more interested in science, remembered the material better, and did better at complex open-ended problem-solving tasks related to the lesson—while the students who read the achievement-based text actually developed more stereotypical images of scientists.
A fascinating set of studies! (Follow the link below to see the description of the second one.) It reminds me of our GaComputes findings about the importance of early computing experiences for minority students. Just taking a single CS class changed the women’s definitions of what a computer scientist is. I’ve written on Blog@CACM about how under-represented minorities were more likely than majority students to have had some CS experience in middle or high school that influenced them. These studies together support the argument that having some CS in K12 will likely have a significant impact on later attitudes towards computing.
First, they asked undergraduates from the UW and Stanford University to describe computer science majors.
They found students who were not computer science majors believed computer scientists to be intelligent but with poor social skills; they also perceived them as liking science fiction and spending hours playing video games. Some participants went so far as to describe computer scientists as thin, pale (from being inside all the time), and having poor hygiene.
“We were surprised to see the extent to which students were willing to say stereotypical things, and give us very specific descriptions. One student said computer science majors play ‘World of Warcraft’ all day long. And that’s a very specific, and inaccurate, thing to say about a very large group of people,” Cheryan said.
However, women who had taken at least one computer science class were less likely to mention a stereotypical characteristic. There was no difference in men’s descriptions, whether or not they had taken a computer science class.