Posts tagged ‘BPC’
Starting from the students to build engaging computing courses for non-CS majors: Response to Goldweber and Walker
Michael Goldweber and Henry Walker responded to my blog posts (here in Blog@CACM and here in this blog) in the Inroad blog (see article here). My thanks to them for taking the time to respond to me. I found their comments especially valuable in helping to see where I was making assumptions about common values, goals, and understanding. It’s too easy in a blog to only get responses from people who share a common understanding (even if we violently disagree about values and goals). I found it helpful to get feedback from Dr. Goldweber and Dr. Walker with whom I don’t correspond regularly.
“Pedagogy” isn’t just “how to teach” for me. They argue that their articles are not about pedagogy but about what should be taught in a course that students might take to explore computer science. The page I linked to at the US Department of Education is about evidence-based education, not evidence-based pedagogy. The definition of pedagogy is “the discipline that deals with the theory and practice of education” (Wikipedia link). One meaning of pedagogy is the whole field of education, which is how I meant it in that piece (as in Pedagogy of the Oppressed.) What to teach is part of pedagogy. If we don’t use evidence for making decisions what to teach, we are practicing folk pedagogy.
My larger point was about the role of evidence rather than intuition. Whether we’re talking about how to teach or what to teach, I believe that we have to gather evidence (or in Paulo Freire’s terms, have a dialog with the students and stakeholders). Certainly, we want to gather evidence about the effectiveness of our teaching. We also need to gather evidence when designing education. My background is in education and HCI. For me, “Know thy users for they are not you” is a given in HCI, and “Student-centered” is a given in Education. Both saying suggest that we start with not-me: not the designer, not the teacher, not the domain expert. But for Dr. Walker, “The starting point is identifying the themes and Big Ideas, not pedagogy.”
The unspoken assumption behind my posts, which may not be shared with Dr. Walker and Dr. Goldweber, is that any CS course for non-CS majors (whether a service, elective, or exploratory course) should aim to increase interest in the field of CS, and especially, should be designed to attract and engage women and under-represented minorities in CS. If we are happy with just having the male white and Asian students that we typically attract now, then sure, Dr. Goldweber’s right — we can just do like Philosophy does and build the course based on what we think is important.
Dr. Walker is absolutely right — there is too little time in a course to fit in everything that we think is important about CS. Even if we leave programming out, there is still too much material. How do we decide which Big Ideas to include?
In my process, I start with the students. What are their life goals and desired careers? What’s needed from computing for them to be successful? What are their values? How can I show that computer science is relevant to those values? To choose among the ideas of computer science, we should use what the students need. To teach the ideas that students may not know they need, we should speak to their values.
I disagree with Dr. Goldweber on these points:
The design of a non-major’s course in computing, which is not a service course for some other department/program, should belong in the hands of the CS faculty. Students electing to explore a discipline take these courses. Surely, discipline experts are those who can best decide what to present from the discipline.
We can just design courses for non-CS majors based on our own experience and intuition. We shouldn’t be surprised, then, if we mostly attract white or Asian males and if we fail to engage diverse audiences. Since all three of us (Dr. Walker, Dr. Goldweber, and me) are white, male, CS professors, I believe that we’re the wrong people to use only our own experience and intuition when designing courses for non-CS majors, for a more diverse student population. Yes, we’re disciplinary experts, but that’s not enough. It is our responsibility to design the courses — on that, we’re agreed. It’s our responsibility to design for the students’ success.
One of my favorite quotes about computing education comes from Betsy DiSalvo and Amy Bruckman. “Computer science is not that difficult, but wanting to learn it is.” (See article here.) If we our goal is for students to learn computer science, we have to figure out will make them want to learn it.
Every year, Barbara Ericson does an analysis of the AP CS exam demographics by state. The 2013 analysis (see here) got a lot of media attention (see on-going list). Here’s the run-down for 2014. Her detailed national analysis (from which I quote in this document) can be found here, and her detailed race and gender analysis (which I include some) can be found here.
Nationally, 37,327 students took the AP CS A exam in 2014. This was a big increase (26.29%) from the 29,555 students who took it in 2013.
- The number of schools who passed the audit (which is a reasonable proxy for the number of AP CS teachers) went up by almost 300: 2,525 versus 2,252 the previous year.
- The number of female exam-takers was 7,458 (20%) which was up from 5,485 the year before (18.5%).
- The number of black students was 1,469 which was an increase from 1,090 the previous year. The number of Hispanic students was 3,270 up from 2,408 the previous year.
The top 10 states in terms of the number of exams taken were in 2014 were (with their 2014 and 2013 positions listed — Florida rose and Maryland dropped):
But California is also the largest state. If we control for population, here are the top 10 states by # exams in 2014 / estimated 2012 population / 100,000:
Eight states had a decrease in the number of students taking the AP CS A exam from the previous year: Oregon, Oklahoma, South Dakota, Kansas, Montana, Arkansas, West Virginia, and Maine.
Eighteen states had less than 100 people take the AP CS A exam in 2014, with Wyoming still the only state with no students taking the exam.
Barbara had help from Phil Sands from Purdue this year in doing the demographic analysis.
Females: The top three states with the most women taking the exam in 2014 are:
- California with 1599 exams (24%) and a pass rate of 65%
- Texas with 1102 exams (24%) and a pass rate of 51%
- New York with 504 exams (18.4%) and a pass rate of 56%
The top three states with the highest percentage of females taking the exam are (number of women / number of exams)
Mississippi (1/4 = 25%), Washington (260/1048 = 25%), Oklahoma (42/171 = 25%).
Tennessee, which had 31% female exam-takers in 2012, is no longer in the top ten of states.
No females took the exam in Montana (0 women of 4 exam takers) or Wyoming (but nobody took the exam in Wyoming). Eight more states had at least one woman but less than 10 women take the exam:Mississippi (1/4), North Dakota (1/14), Nebraska (2/71), Kansas (3/40), Alaska (4/30), South Dakota (4/29) and Utah (5/104) and Delaware (7/79).
African American: The top three states that had the most African American students take the exam in 2014 are:
- Maryland with 192 exams and a pass rate of 30.2% for African Americans compared to the overall pass rate of 62.1%.
- Texas with 161 exams and a pass rate of 40% compared to the overall pass rate of 55.7%.
- Georgia with 155 exams and a pass rate of 23% compared to the overall pass rate of 45.8%.
Thirteen states had no African American exam-takers in 2014 (number of African Americans / number of exams)
Alaska (0/30), Idaho (0/58), Kansas (0/40), Maine (0/99), Mississippi (0/4), Montana (0/4), Nebraska (0/71), New Hampshire (0/108), New Mexico (0/61), North Dakota (0/14), South Dakota (0/29), Vermont (0/71), and Wyoming (0/0).
Hispanic: The top three states that had the most Hispanics take the exam in 2014 (the College Board separates this into Mexican American, Puerto Rican, and Other Hispanic)
- Texas with 968 and a pass rate of 32% compared to the overall pass rate of 55.7%.
- California with 610 and a pass rate of 45.2% compared to the overall pass rate of 67.3%.
- Florida with 450 and a pass rate of 39.1% compared to the overall pass rate of 42.5%.
Seven states had no Hispanics take the exam in 2014: Iowa (0/119) which is 5.5% Hispanic by population, Mississippi (0/4) which is 2.9% Hispanic, Montana (0/4), North Dakota (0/14), South Dakota (0/29), West Virginia (0/48), and Wyoming (0/0).
A nice list with interesting history — I didn’t know most of these (Thanks to Guy Haas who sent it to me):
Although “Amazing Grace” Hopper is sometimes mentioned, Lovelace often serves as a token when talking about women in technology. However, her isolation in the midst of the male-dominated history of computer science does not reflect reality: There have been many, many other women who have made their careers in computer science, but whose stories have been erased and forgotten, many of their successes snubbed due to sexism. In fact, says Kathy Kleiman, founder of the ENIAC Programmers Project, “Programming was a pink-collar profession for about the first decade. There were some men, but it was actually hugely women.”
Lest we forget these female pioneers, here are ten that should be remembered alongside their male counterparts.
An argument for diversity is that it leads to better team decisions and designs. But it turns out that having women on the team at all leads to better group performance. It’s an important finding to argue why we need more women in CS, which is still a question I hear regularly, “So what if almost all our undergraduates are women?” Or as one blogger recently put it (see here if you really want to read more of this), “No one in the tech sector right now gives a shit about diversity. There is no reason whatsoever why a lack of diversity in the field would be a problem unless it comes from government quotas and legal threats.”
Instead, the smartest teams were distinguished by three characteristics.
First, their members contributed more equally to the team’s discussions, rather than letting one or two people dominate the group.
Second, their members scored higher on a test called Reading the Mind in the Eyes, which measures how well people can read complex emotional states from images of faces with only the eyes visible.
Finally, teams with more women outperformed teams with more men. Indeed, it appeared that it was not “diversity” (having equal numbers of men and women) that mattered for a team’s intelligence, but simply having more women. This last effect, however, was partly explained by the fact that women, on average, were better at “mindreading” than men.
I’ve argued before that there is no reason to believe in the Geek Gene (see post here), and every reason to believe that good teaching can overcome “innate” differences (see post here). Now, a study in Science suggests that that belief in “innate gift or talent” can explain why some fields have more diversity and others do not.
Sparked by sharing anecdotes about their personal experiences in fields with very different gender ratios, a team of authors, led by Andrei Cimpian, a psychologist at the University of Illinois, Urbana-Champaign, and philosopher Sarah-Jane Leslie of Princeton University, surveyed graduate students, postdoctoral fellows, and faculty members at nine major U.S. research institutions. Participants rated the importance of having “an innate gift or talent” or “a special aptitude that just can’t be taught” to succeed in their field versus the value of “motivation and sustained effort.” The study, published online today in Science, looked across 30 disciplines in STEM (science, technology, engineering, and mathematics) fields, the social sciences, and the humanities.
The authors found that fields in which inborn ability is prized over hard work produced relatively fewer female Ph.D.s. This trend, based on 2011 data from the National Science Foundation’s Survey of Earned Doctorates, also helps explain why gender ratios don’t follow the simplified STEM/non-STEM divide in some fields, including philosophy and biology, they conclude.
I wrote my Blog@CACM post for January on the rising enrollment in computer science and how that is making irrelevant our advances in retaining students (see article here). Retention is simply not the problem in US CS programs today.
But thinking about the 1980’s and today (as described in this blog post), I began wondering if our boom-and-bust cycles might be related to our inability to manage the boom.
- First, we get a huge increase in enrollment due to some external factor (like the introduction of the personal computer).
- Then, we have to manage the rise in enrollment. We try to hire faculty, but we can’t bring them in fast enough. We stop worrying about high-quality, high-retention education — we need the opposite! We set up barriers and GPA requirements.
- Word gets out: CS is hard. The classes are too difficult. It’s too competitive. Minority group students suffer from the imposter phenomenon and leave faster than majority students.
- Result: Enrollment drops. Diversity decreases.
- Then the next external factor happens (like the invention of the graphical Web browser), and we start the sequence again.
If we could give everyone a seat who wanted one, and we continued to focus on retention and high-quality education, might we actually have a steady-state of a large CS class? Could our inability to manage the load actually be causing the bust side of the cycle?
Why there’s no such thing as an ‘F’ in computer science: The Fear Factor in CS (and other things we learn)
Nice article by my colleague Ayanna Howard and Oracle’s Alison Derbenwick Miller. I’m teaching Media Computation again this semester, and it seems to me that the biggest barrier for my students is simply fear. They don’t type code because they’re afraid of failure. They don’t try to understand the code because they’re afraid they can’t.
It’s not just CS, of course. At my last ukulele meetup, I sat next to a woman who brought her ukulele, but wouldn’t take it out of the case. She just sang the songs along with us. When I encouraged her to bring it out, she said, “I’m just a beginner. I’ll learn to play it first, then I’ll play along.” That gets the process wrong — it’s playing along that leads to learning to play it. But I was struck by her body language and voice when she made the statement. She was deeply frightened — of making mistakes and being ridiculed for it, I guess.
I get that. I did my first public performance singing and playing the ukulele in December. (Christmas carols are pretty easy, and are completely acceptable at a December open mic night.) I was so frightened. I finished one song (“Silent Night”), then invited my daughters up to sing with me (“Jingle Bells”) to help me get through it (thanks to them for the support!), but still couldn’t get past one verse. My legs and arms were shaking so badly I didn’t think I could go on. Fear is powerful.
Ayanna and Alison point out something important and real that we have to help students get past.
They will give you myriad reasons, among them that the work just isn’t interesting, that the cool kids don’t do it, and fear – fear it’s too hard, fear they’ll be ridiculed as “nerds,” fear of being exposed as an intellectual fraud, or ironically, as the “too smart kid,” fear of failure.
Fear is an awful thing. It’s a four-letter “f” word that holds incredible power – power to keep us from doing what is good, what is right. Power to stop us from taking risks. Power to maintain the status quo, to stop disruption, to inhibit change. Power to stymie innovation, and to limit opportunity.
Fear is bad. Fear stands between us and a better world. It stands between us and our better selves.