Posts tagged ‘NCWIT’
New AAUW Report: Solving the Equation: The Variables for Women’s Success in Engineering and Computing
Important new report from the American Association of University Women (AAUW). I particularly like the detailed analysis of what happened at Harvey Mudd, with a lot of credit to Christine Alvarado as well as the other excellent faculty who created initiatives there. As Maria Klawe keeps saying, it wasn’t just her.
More than ever before, girls are studying and excelling in science and mathematics. Yet the dramatic increase in girls’ educational achievements in scientific and mathematical subjects has not been matched by similar increases in the representation of women working as engineers and computing professionals. Just 12 percent of engineers are women, and the number of women in computing has fallen from 35 percent in 1990 to just 26 percent today.
The numbers are especially low for Hispanic, African American, and American Indian women. Black women make up 1 percent of the engineering workforce and 3 percent of the computing workforce, while Hispanic women hold just 1 percent of jobs in each field. American Indian and Alaska Native women make up just a fraction of a percent of each workforce.
End the ‘leaky pipeline’ metaphor when discussing women in science: Technical knowledge can be used in many domains
I’m familiar with the argument that we shouldn’t speak of a “pipeline” because students come to STEM (and computing, specifically) in lots of ways, and go from computing into lots of disciplines. The below-linked essay makes a particular point that I find compelling. By using the “leaky pipeline” metaphor, we stigmatize and discount the achievements of people (women, in particular in this article) who take their technical knowledge and apply it in non-computing domains. Sure, we want more women in computing, but we ought not to blame the women who leave for the low numbers.
However, new research of which I am the coauthor shows this pervasive leaky pipeline metaphor is wrong for nearly all postsecondary pathways in science and engineering. It also devalues students who want to use their technical training to make important societal contributions elsewhere.
How could the metaphor be so wrong? Wouldn’t factors such as cultural beliefs and gender bias cause women to leave science at higher rates?
My research, published last month in Frontiers in Psychology, shows this metaphor was at least partially accurate in the past. The bachelor’s-to-Ph.D. pipeline in science and engineering leaked more women than men among college graduates in the 1970’s and 80’s, but not recently.
Men still outnumber women among Ph.D. earners in fields like physical science and engineering. However, this representation gap stems from college major choices, not persistence after college.
Other research finds remaining persistence gaps after the Ph.D. in life science, but surprisingly not in physical science or engineering — fields in which women are more underrepresented. Persistence gaps in college are also exaggerated.
It’s that time of year when Deans and Chairs start prodding students and teachers about course evaluations. What do the students think about their courses? What do the students think about their teachers?
There is a significant body of evidence that suggests that course evaluations are a stable measure about the teachers themselves. For example, the scores for a teacher are consistent across instantiations of the course over time (see Nira Hartiva’s work). However, they still might not be measuring something that we consider significant about teaching.
For example, it’s a mistake to think that student course evaluations tell us what a teacher knows about teaching. The teacher’s pedagogical content knowledge is invisible to the student. The student only sees what the teacher has decided to do to interact with the students. The student can’t see the knowledge that the teacher used in making that choice.
Three kinds of knowledge that are particularly relevant to a CS teacher are:
- Knowledge about how to teach. A good teacher knows more than one way to teach a particular subject, and knows to change methods for a given student or to change the pace of a class. When I see students driving away in the back of my class, I know that it’s time for a Peer Instruction activity.
- Knowledge about misconceptions. As was shown in Phil Sadler’s exceptional study (see blog post), a characteristic of teacher expertise is knowledge about what students typically get wrong. Based on that knowledge, teachers can address those misconceptions, and lead students to discover and correct the misconceptions themselves.
- Knowledge about how to broaden participation in computing, which is particularly relevant to CS teachers. These include how to teach avoiding stereotype threat and triggering the imposter phenomenon, about how to give everyone a voice in the class and not let the loud and boisterous define the teacher’s perceptions of the course. I can offer a negative example, taken from real life but might have been invented after reading the negative examples in Unlocking the Clubhouse.
Teacher: How many of you students had Python in a previous class?
(Most students raise their hands, since it’s the language used in the pre-requisite class.)
Teacher: Well, you learned a terrible language. You’ll have to forget everything you know if you want to pass this class.
(Every student suffering imposter syndrome at this point decides to drop.)
This teacher actually has quite high course evaluation scores — and double the drop rate of every other teacher of that class.
Pedagogical content knowledge (PCK) is the key difference between novice and expert teachers, but is invisible to students. This is another reason why student evaluations of teaching (aka, Student Reviews of Instruction (SRI)) are inadequate as measures of teaching quality. They can’t measure a key indicator of teacher expertise.
I’ve been wondering how post-secondary teaching might change if we were to take a PCK perspective seriously. The knowledge of good teaching is definable and measurable.
- We might define courses not just in terms of learning objectives but in terms of what knowledge the teacher should have to teach the class effectively.
- We could evaluate University and College teachers based on their PCK — literally, taking a test on what they know about teaching the class.
- PCK tests would finally create an impetus for University and College faculty to pursue professional development — that’s where they’d learn the teaching methods, student misconceptions, and methods for encouraging BPC that they need to answer the PCK tests. One might even imagine teachers taking a class on how to teach a new class that they’ll be offering in the future — preparing for a course by developing expertise in teaching that course. What an interesting thought that is, that higher education faculty might study how to teach.
BBC is giving away 1 million mini computers so kids can learn to code: Prediction — little impact on broadening participation
I agree that these boards are cool, but I’m a geeky white guy. I predict that they’ll have little impact in increasing access to computing education or in diversifying computing. Bare board computers are not more attractive to teachers, so we don’t get more teachers going into CS. They’re not more attractive than existing computers to women who aren’t already interested in computing. Why are people so excited about handing out bare board computers to grade school children? Is this just white males emphasizing the attributes that attract them? Judith Bishop of MSR (whose TouchDevelop will work on these new computers) says that she’s seen girls get engaged by these new computers, but nobody has done any research to see if that’s more than the 20% of females who get interested in computing now, or if that happens outside of the pilot classrooms.
Currently in development, the Micro Bit is a small piece of programmable, wearable hardware that helps kids learn basic coding and programming. It could act as a springboard for more advanced coding on products, such as the single-board computer Raspberry Pi, according to the BBC.
Children will be able to plug the device into a computer, and start creating with it immediately.
“BBC Make it Digital could help digital creativity become as familiar and fundamental as writing, and I’m truly excited by what Britain, and future great Britons, can achieve,” BBC director general Tony Hall said in a statement Thursday.
Nice blog post from Barbara Ericson exploring the lack of women in the new surge in CS undergraduate enrollment.
A Surge in Majors, but Where Are the Women?
While a number of colleges and universities in the United States have recently seen a tremendous increase in the number of students who want to major in computing, the percentage of women who are interested is still low. A study conducted by the Association for Computing Machinery and the WGBH Educational Foundation in 2008 found that only 9 percent of college-bound teen girls thought that a career in computing was a very good choice for them, and only 17 percent thought that it was a good career choice. Teen girls associated computing with typing, math, and boredom. While the percentage of bachelor’s degrees awarded to women in the United States did increase from 11.7 percent in 2010–11 to 12.9 percent in 2011–12, women are still dramatically underrepresented.
The Percentage of Women Taking the Computer Science AP Exam Lags
The Advanced Placement (AP) computer science A course is equivalent to a college-level introductory computer science course. It focuses on object-oriented programming in Java. In 2014, only about 20 percent of AP computer science A exam takers were women. While that was an increase from the previous year, when the percentage was 18.5 percent, it is still far below the percentage of women who took the AP calculus AB exam (48.7 percent) and the percentage of women who took the AP biology exam (59 percent). It is even well below the percentage of women who took the physics B exam (34.7 percent), as shown below.
Nice story and presentation from Katie Cunningham about how she informed her faculty about why there are so few women in CS, and what they can do about it.
I based the main arc of my presentation on a book chapter by Whitecraft and Williams that Greg Wilson of Software Carpentry was kind enough to forward to me. It’s an evenhanded look at much of the research in this area, including theories that are often out of favor in most places I frequent. It served as a great overview, though I felt it could have focused more on issues involving differences in prior programming experience pre-college and intimidation brought on by “nerdy strutting“. (Update: I just discovered a fantastic 2012 report by NCWIT that can also serve as a great overview. It covers cultural issues more comprehensively, with more recent research and more focus on the pre-college years.)
C is Manly, Python is for “n00bs”: Our perception of programming languages is influenced by our gender expectations
Surprising and interesting empirical evidence that language use is mostly gender-neutral. Our expectations about gender influence how we think about programming languages. These perceptions help explain the prevalence of C and C++ in many undergraduate computing programs.
There is also a gendered perception of language hierarchy with the most “manly” at the top. One Slashdot commenter writes, “Bah, Python is for girls anyways. Everybody knows that PERL is the language of true men.” Someone else responds, “Actually, C is the language of true men…” Such views suggest that women might disproportionately use certain languages, but Ari and Leo found in their programmer surveys that knowledge of programming languages is largely equivalent between genders. Women are slightly more likely to know Excel and men are slightly more likely to know C, C#, and Ruby, but not enough to establish any gendered hierarchy.