Lecia Barker had a terrific paper in SIGCSE 2015 that I just recently had the chance to dig into. (See paper in ACM DL here.) Here’s the abstract:
Despite widespread development, research, and dissemination of teaching and curricular practices that improve student retention and learning, faculty often do not adopt them. This paper describes the first findings of a two-part study to improve understanding of adoption of teaching practices and curriculum by computer science faculty. The paper closes with recommendations for designers and developers of teaching innovations hoping to increase their chance of adoption.
I’ve published in this area before. Davide Fossati and I wrote a paper about the practices of CS teachers (based on interviews with about a dozen CS university teachers): how they made change, what convinced them to change, and how they decided if the change worked. (See blog post about this here.) The general theme was that these decisions rarely had an empirical basis.
Lecia and her co-authors went far beyond our study. She interviewed and observed 66 CS faculty from 36 institutions, explicitly chosen to represent a diverse set of schools. The result is the best picture I’ve yet seen of how CS faculty make decisions.
Lecia found more evidence of teachers using empirical evidence than we did, which was great to see. But whether students “liked” it or not was still the most critical variable:
On the other hand, if students don’t “like it,” faculty are unlikely to continue using a new practice. At a public research university, a professor said, “You can do something that you think, ‘Wow! If the learning experience was way better this term, the experiment really worked.’ And then you read your teaching reviews, and it’s like the students are pissed off because you did not do what they expected.”
Lecia discovered a reason not to adopt that I’d not heard before. She found that CS teachers filter out innovations that didn’t come from a context like their own. Those of us at research universities are filtered out by some teachers at teaching-oriented institutions:
Faculty trust colleagues who have similar teaching and research contexts, share attitudes toward students and teaching, or teach similar subjects. In describing what conference speakers he finds credible at SIGCSE, a professor at a private liberal arts university acknowledged, “I do have the anti- ‘Research One’ bias. Like if the speaker is somebody who teaches at <prestigious public research university>, the mental clout that I give them as a teacher—unless they’re a lecturer—I drop them a notch. When someone stands up to speak and they’re from a really successful teaching college <names several> or universities that have a real reputation of being great undergraduate teaching institutions, I give them a lot of merit.”
The part that I found most depressing (even if not surprising) is that research evidence did not matter at all in adopting new ways to teach:
Despite being researchers themselves, the CS faculty we spoke to for the most part did not believe that results from educational studies were credible reasons to try out teaching practices.
Lecia’s study is well done, and the paper is fascinating, but the overall picture is rather dismal. She points out many other issues that I’m not going into here, like the trade-off between cost and benefit of adopting a new practice, and about the need for specialized equipment in classrooms for some new practices. Overall, she finds that it’s really hard to get higher education CS faculty to adopt better practices. We reported on that in “Georgia Computes!” (see post here) but it’s even more disappointing when you see it in a large, broad study like this.
The New York Times weighs in on the argument about active learning versus passive lecture. The article linked below supports the proposition that college lectures unfairly advantage those students who are already privileged. (See the post about Miranda Parker’s work for a definition of what is meant by privilege.)
The argument that we should promote active learning over passive lecture has been a regular theme for me for a few weeks now:
- I argued in Blog@CACM that hiring ads and RPT requirements should be changed explicitly to say that teaching statements that emphasize active learning would be more heavily weighted (see post here).
- The pushback against this idea was much greater than I anticipated. I asked on Facebook if we could do this at Georgia Tech. The Dean of the College of Engineering was supportive. Other colleagues were strongly against it. I wrote a blog post about that pushback here.
- I wrote a Blog@CACM post over the summer about the top ten myths of computing education, which was the top-visited page at CACM during the month of July (see post here). I wrote that post in response to a long email thread on a College of Computing faculty mailing list, where I experienced that authority was able to sway CS faculty more than research results (blog post about that story here).
The NYTimes piece pushes on the point that this is not just an argument about quality of education. The argument is about what is ethical and just. If we value broadening participation in computing, we should use active learning methods and avoid lecture. If we lecture, we bias the class in favor of those who have already had significant advantages.
Thanks to both Jeff Gray and Briana Morrison who brought this article to my attention.
Yet a growing body of evidence suggests that the lecture is not generic or neutral, but a specific cultural form that favors some people while discriminating against others, including women, minorities and low-income and first-generation college students. This is not a matter of instructor bias; it is the lecture format itself — when used on its own without other instructional supports — that offers unfair advantages to an already privileged population.
The partiality of the lecture format has been made visible by studies that compare it with a different style of instruction, called active learning. This approach provides increased structure, feedback and interaction, prompting students to become participants in constructing their own knowledge rather than passive recipients.
Research comparing the two methods has consistently found that students over all perform better in active-learning courses than in traditional lecture courses. However, women, minorities, and low-income and first-generation students benefit more, on average, than white males from more affluent, educated families.
The linked blog post below bemoans the fact that the AP CS is growing, perhaps at the expense of growth in AP Statistics. AP Stats is still enormously successful, but the part of the post that’s most interesting is the author’s complaints about what’s wrong with CS. I read it as, “Students should know that CS is not worthy of their attention.”
It’s always worthwhile to consider thoughtful critiques seriously. The author’s points about CS being mostly free of models and theories is well taken. I do believe that there are theories and models used in many areas of CS, like networking, programming languages, and HCI. I don’t believe that most CS papers draw on them or build on them. It’s an empirical question, and unfortunately, we have the answer for computing education research. A recent multi-national study concluded that less than half of the papers in computing education research draw on or build on any theory (see paper here).
Though the Stat leaders seem to regard all this as something of an existential threat to the well-being of their profession, I view it as much worse than that. The problem is not that CS people are doing Statistics, but rather that they are doing it poorly: Generally the quality of CS work in Stat is weak. It is not a problem of quality of the researchers themselves; indeed, many of them are very highly talented. Instead, there are a number of systemic reasons for this, structural problems with the CS research “business model.”
The article below is from the Berkeley student newspaper, but it’s not just a Berkeley issue. Enrollment is surging, and schools have too few resources to meet demand. Dealing with the enrollment surge was a big topic at the ACM Education Council last month. Based on what I heard at last year’s meeting of the Ed Council, I predicted that the enrollment surge would like lead to less diversity in CS (see blog post here). This year, I came away with the sense that most attendees believe it’s quite likely. The issue now is measuring the impact and seeing what resources can be marshaled once there’s evidence that there has been damage to diversity. Both CRA and the National Academies are conducting studies about the impact of the enrollment surge. Right now, action is more about studying the impact than responding to the need — people might be willing to respond, but we have so few options. Google has funded several projects to invent new ways to respond (see blog post here), but those are just starting now. We won’t know for months if they’ll work.
When the culture at UC Berkeley simultaneously stresses the importance of a computer science education and heightens GPA requirements for the major, barriers to entry become increasingly difficult to overcome. More and more students entering UC Berkeley feel pressured to learn basic computer science skills to meet the needs of the postgraduation job market — a notion that the campus and its highly ranked computer science department encourage…But the upsurge in enrollment means fewer resources for beginner students, especially in terms of access to teaching assistants and professors.
The computer science department recently changed its requirements for petitioning for admission to the major: Students who entered UC Berkeley before this fall needed a cumulative GPA of 3.0 in the seven lower-division course requirements, whereas students who came in this fall need to complete, specifically, CS 61A, 61B and 70 with a cumulative GPA of 3.3. These are arguably the more difficult “weeder courses” within the prerequisites, and increasing the average required GPA from a B to a B+ makes a real difference for many deserving students hoping to earn a computer science degree. In CS 61A, for example, the past average is a 2.84, or a B-. Holding beginners to such a high standard, especially given the amount of pressure from an increasingly technologically focused society, is a tool to sort students into winners and losers rather than educate them.
The Wall Street Journal recently ran an article by an entrepreneur about why he doesn’t want to hire computer science majors at his start-up. I was particularly struck by this line:
The thing I look for in a developer is a longtime love of coding—people who taught themselves to code in high school and still can’t get enough of it.
Allow me to translate: “I want rich boys. I want those boys who were in the 10% of schools that have CS teachers (which are all rich districts), or parents who knew enough to provide their boys instruction and access from their teen years on. (Nobody really ‘teaches themselves to code.’) The females who start coding in high school will be filtered out by the time I’d hire them. I want those boy to come to me with a decade of immersion in the existing male-dominant, defensive, homogeneous culture that pervades CS classes, so that my startup will be just as lacking in diversity and just as unwelcoming to women. Let’s hear it for the status quo!”
I’ve heard Angela Duckworth talk about the importance of grit in achieving success in American schools (see National Geographic piece on her here). I’ve also heard Jane Margolis rail against this idea, saying that the grit narrative is blaiming the underprivileged for not succeeding more in schools. The below blog piece does a nice job explaining about the interaction of poverty and the grit narrative.
Teachers who subscribe to this “grit” narrative risk conveying the idea that poverty is caused by poor work ethic. The “grit” narrative presents America as a meritocracy where everyone person has full control over their destiny. The “American Dream” is certainly a seductive idea. It is also little more than a fairy tale for many living in poverty today. Just looking at the few examples of poor minorities who have broken through the barriers of poverty creates a blindspot towards all of the other reasons that make it difficult to break through those barriers. These other reasons desperately need attention – both inside and outside of the school system. I see the “grit” narrative as a classic confusion between correlation and causation. This narrative and other ideas highlight the risks that teachers take if they act purely out of a sense of helpful urgency.
I got an email from CodersTrust, asking me to help promote this idea of developing grants to help students in the developing world learn to code. But the education materials they’re offering is the same CodeAcademy, Coursera MOOCs, and similar developed-world materials. Should they be? Should we just be sending the educational materials developed for US and Europe to the developing world? I thought that that was one of the complaints about existing MOOCs, that they’re a form of educational imperialism.
CodersTrust is the brainchild of Ferdinand Kjærulff. As a Captain of the Danish army he served as recovery officer in Iraq after the fall of Saddam. He pioneered a recovery project with the allied forces, bringing internet and e-learning to the citizens of the region in which he was stationed. The project was a massive success and inspired him to eventually create CodersTrust – supported by Danida – with a vision to democratize access to education via the internet on a global scale.
via CodersTrust | About.