Posts tagged ‘teachers’
New research report available at http://services.google.com/fh/files/misc/unconscious-bias-in-the-classroom-report.pdf from with Google and Thomas Dee of Stanford University and Seth Gershenson from American University.
In sum, Unconscious Bias (UB) is a nontrivial problem in education, especially in CS and STEM education, and it is not easily addressed via traditional educational policies and interventions. However, interventions that identify and alter the frequently unconscious psychological processes that harm individuals’ outcomes are currently being developed and piloted. Teacher-facing interventions, which can be administered to both pre- and in-service teachers, are particularly promising. In part, this is because by addressing UB among teachers, we can help shape the entire classroom context in supportive ways. Furthermore, teacher-facing interventions are potentially cost-effective and scalable, because infrastructure for teacher training is already in place.
Until I heard this recent Freakonomics podcast, I was not aware of this response to innovation and entrepreneurship trends. The quote below speaks directly to engineering education, but is as much about computing education.
The value of engineering is much, much more than just innovation and new things. Focusing on taking care of the world rather than just creating the new nifty thing that’s going to solve all of our problems. If you look at what engineers do, out in the world, like 70-80 percent of them spend most of their time just keeping things going. And so, this comes down to engineering education too, when we’re forcing entrepreneurship and innovation as the message, is that we’re just kind of skewing reality for young people and we’re not giving them a real picture and we’re also not valuing the work that they’re probably going to do in their life. That just seems to me to be kind of a bad idea.
The quote is from Lee Vinsel who was a co-author on a thought-provoking essay, Hail the maintainers, sub-titled: “Capitalism excels at innovation but is failing at maintenance, and for most lives it is maintenance that matters more.”
To take the place of progress, ‘innovation’, a smaller, and morally neutral, concept arose. Innovation provided a way to celebrate the accomplishments of a high-tech age without expecting too much from them in the way of moral and social improvement.
It’s easy to see this emphasis on innovation over maintenance. We talk about disruption and transformation much more than reforming, repairing, or improving. We talk more about creation than understanding.
We increasingly teach computer science to prepare students to be innovators and create new things (e.g., join startups), when the reality is that most computer science graduates are going to spend the majority of their time maintaining existing systems. (See the papers by Beth Simon and Andy Begel tracking new hires at Microsoft.) Few who do enter the startup world will create successful software and successful companies, so it’s unlikely that those students who aim to create startups will have a lifelong career in startups. In terms of impact and importance, keeping large, legacy systems running is a much greater social contribution than creating yet another app or game, when so few of those startup efforts are successful. Aren’t we then as guilty as the engineering educators, described in the first quote?
In terms of what we teach in computing and how, innovation and maintenance is a hard balance to strike. As Alan Kay has noted, “The computing revolution hasn’t happened yet.” We’re still inventing and innovating because what we have isn’t good enough. But, that desire to value what’s new leads us to overvaluing the fad of the moment, rather than exploring, developing, and understanding what we have at-hand. Why do we have to keep changing the introductory programming language, when it’s clear that we don’t understand how students learn (and don’t learn) the programming languages that we currently teach? Why did we give up on Logo when it’s still better than most languages for children today? It’s a tough balance — to strive for better than we have, but valuing, developing, and improving what we currently have.
My Blog@CACM post for this month is about imagining the remedial teaching techniques of a school-based “Computing Lab” in the near future.
It’s becoming obvious that computing is a necessary skill for 21st Century professionals. Expressing ideas in program code, and being able to read others’ program code, is a kind of literacy. Even if not all universities are including programming as part of their general education requirements yet, our burgeoning enrollments suggest that the students see the value of computational literacy.
We also know that some students will struggle with computing classes. We do not yet have evidence of challenges in learning computation akin to dyslexia. Our research evidence so far suggests that all students are capable of learning computing, but differences in background and preparation will lead to different learning challenges.
One day, we may have “Computing Labs” where students will receive extra help on learning critical computational literacy skills. What would happen in a remedial “Computing Lab”? It’s an interesting thought experiment.
I list several techniques in the article, and I’m sure that we can come up with many more. Here’s one more each DO and DON’T for “Computer Lab” for struggling computationalists.
- DO use languages other than industry standard languages. As I’ve mentioned before in this blog, CS educators are far too swayed by industry fads. I’m a big fan of Livecode, a cross-platform modern form of HyperCard. An ICER 2016 paper by Raina Mason, Simon et al. estimated Livecode to have the lowest cognitive load of several IDE’s in use by students. If we want to help students struggling to learn computing, we have to be willing to change our tools.
- DON’T rely on program visualizations. The evidence that I’ve seen suggests that program visualizations can help high-ability students, and well-designed program visualizations can even help average students. I don’t see evidence that program visualizations can help the remedial student. Sketching and gesture are more effective for teaching and learning in STEM than diagrams and visualizations. Sketching and gesture encourage students to develop improved spatial thinking. Diagrams and visualizations are likely to lead remedial students into more misconceptions.
ICER 2016 was just held in Melbourne, Australia, so I found the article linked at the bottom (and from which these images come from) particularly relevant and interesting.
Australia is facing a boom in primary school students, which creates additional demand for teachers. As has been mentioned here previously, there is a shortage of teachers. The shortage isn’t distributed across fields. In particular, over 30% of computing teachers in Australia are teaching without qualification (see image below). When considering other shortages (e.g., declining number of computing teachers in Scotland, as described in the last post), it’s clear that the pipeline of CS teachers is going to be an impediment to CS for all.
But an influx of new students isn’t the only problem our school system needs to address.Shortages in specific subject areas mean that many students are being taught by teachers working outside of their qualifications.
Losing CS Teachers in Scotland: Latest report on CS teacher numbers from Computing At School Scotland
If you can forgive the bias in the graph (what looks like a 90% drop is actually a 25% drop), you will find this to be an important and interesting report. Scotland has one of the strongest computing at schools efforts in the world (see site here), with an advanced curriculum and a large and well-designed professional development effort (PLAN-C). Why are they losing CS teachers?
When I wrote about this in 2014 (the trend has only continued), I pointed out that part of the problem is teachers refusing to shift from teaching Office applications to computer science. The current report doesn’t give us much more insight into why. The point I found most interesting was that Scottish student numbers dropped 11%, and teacher numbers in the other disciplines are also declining (e.g., mathematics teachers are declining by 6% over the same period), but at a much slower rate than the CS decline of 25%. That makes sense too — if you’re a teacher and things are getting tough, stick with the “core” subjects, not the “new” one. It’s worth asking, “How do we avoid this in the US?” and “Can we avoid it?”
We know too little about what happens to CS teachers in the US after professional development. I know of only one study of CS teacher retention in the US, and the observed attrition rate in that study was far worse than 25%. Do we know what US retention rate is for CS teachers? Maybe Scotland is actually doing better than the US?
Today we launch our latest report into the numbers of Computing Science teacher numbers across Scotland. We have carried out this survey in 2012, 2014 and now 2016 as we are concerned about the decreasing number in Computing teachers in Scottish schools. Nationally we now have 17% of schools with no computing specialist and a quarter of Secondary schools have only one CS teacher.
From Lauren Wilcox:
Betsy DiSalvo, Dick Henneman and I have designed a survey about a topic that is near and dear to us as HCI faculty: topics, learning goals, and learning activities in HCI classrooms!
We hope to do an annual “pulse” of HCI instructors across the globe.
We are hoping that you can take the survey, and also please share with your colleagues who teach HCI-related classes.
My Blog@CACM post for this month is on JES, the Jython Environment for Students, which at 14 years old and over 10,000 downloads, is probably one of the oldest, most used, and (by some definition) most successful pedagogical Python IDE’s.
The SIGCSE Members list recently had a discussion about moving from Python 2 to Python 3. Here’s a description of differences. Some writers asked about MediaComp. With respect to the Media Computation libraries, one wrote:
I’m sad about this one, because we use and like this textbook, but I think it’s time to move to Python 3. Is there a compatible library providing the API used in the text?
Short answer: No. There are no compatible Media Computation libraries for CPython 2 or 3.
We keep trying. The latest attempt to build Media Computation libraries in CPython is here: https://github.com/sportsracer48/mediapy. It doesn’t work on all platforms yet, e.g., I can’t get it to load on MacOS.
We have yet to find a set of libraries in Python that work cross-platform identically for sample-level manipulations of sounds. For example, PyGame’s mixer object doesn’t work exactly the same on all platforms (e.g., sampling rates aren’t handled the same on all platforms, so the same code plays different speed output on different platforms). I can do pixel-level manipulations using PIL. We have not yet tried to find libraries from frame manipulations of video (as individual images). I have just downloaded the relevant libraries for Python 3 and plan to explore in the future, but since we can’t make it work yet in Python 2 (which has more mature libraries), I doubt it will work in Python 3.
I complained about this problem in my blog in 2011 (see post here). The situation is better in other languages, but not yet in Python.
- I have been building Media Computation examples in GP, a blocks-based language (see post here).
- Jeff Gray’s group at U. Alabama has built Blockly-like languages Pixly and Tunely for pixel and sample level manipulations.
- Cynthia Lee at Stanford has been doing Media Computation in her classes in MATLAB and in C++
- The Calico project supports Media Computation in IronPython (based on Python 3) and many other languages, because it builds on .NET/MONO which has good multimedia support.
When we did the 4th edition of our Python Media Computation textbook, I looked into what we’d have to change in the book to move to Python 3. There really wasn’t much. We would have to introduce