Posts tagged ‘contextualized computing education’
Last week, I attended the Computing Research Association (CRA) Snowbird conference of deans and chairs of computing. (See agenda here with slides linked.) I presented on a panel on why CS departments should embrace computing education research, and another on what CS departments can do to support the CS for All initiative. I talked in that second session about the leadership role that universities can play in creating state partnerships and influencing state policy (see the handout for my discussion table).
Andy Ko was in both sessions with me, and he’s already written up a blog post about his experiences, which match mine closely (including the feeling of being an imposter). I recommend reading his post.
Here, I’m sharing a key insight I saw and learned at Snowbird. Before the conference even started, our Senior Associate Dean for the College of Computing, Charles Isbell, challenged me to name another field that is overwhelmed with majors AND offers service courses to so many other majors. (Maybe biology because of pre-meds?) Computer Science is increasingly the provider of courses to non-CS majors, and those majors want something different than CS majors.
The morning of the first day was dedicated to the enrollment surge. CRA has been gathering data at many institutions on the surge, and Tracy Camp did a great job presenting some of the results. (Her slides are now available here, so you don’t have to rely on my pictures of her slides.) Here’s the bottomline: Student growth has been enormous (across different types of institutions), without a matching growth in faculty. The workload is increasing.
But here’s the surprise: Much of the growth in course enrollment is not CS majors. A large percentage of the growth is in other majors taking CS classes. The below graph is for “mid-level” CS courses, and there are similar patterns in intro and upper-level courses.
Tracy also presented a survey of students (slides available here), which was really fascinating. Below is a survey of (a lot) of intro students at several institutions. All the differences described are significant at p<0.05 (not 0.5 as it says). The difference in what non-majors want and CS majors was is interesting. Majors want (significantly more than non-majors) to “make a lot of money.” Non-majors more significantly want to “Give back to my community” and “Take time off work to care for family.”
U. Illinois has the most innovative program I have heard of for meeting these new needs. They are creating a range of CS+X degree programs. First, these CS+X programs are significant parts of the “X” departments.
But these stats blew me away: CS+X is now 30% of all of CS at U. Illinois (which is a top-5 CS department), and 50% of all admitted first years this year! And it’s 28% female.
It’s pretty clear to me that the future of computing education is as much about providing service to other departments as it is about our own CS major. We have suspected that the growth is in the non-majors for awhile, but now we have empirical evidence. I’ve been promoting the idea of contextualized-computing education, and the notion that other majors need a different kind of CS than what CS majors need. We need to take serious the education of non-CS majors in Computer Science.
Three and a half years, and 1000 blog posts ago, I wrote my 999th blog post about research questions in computing education (see post here). I just recently wrote a blog post offering my students’ take on research questions in computing education (see post here), which serves to update the previous post. In this blog post, I’m going to go more meta.
In my CS Education Research class (see description here), my students read a lot of work by me and my students, some work on EarSketch by Brian Magerko and Jason Freeman, and some by Betsy DiSalvo. There are other researchers doing work related to computing education in the College of Computing at Georgia Tech, notably John Stasko’s work on algorithm visualization, Jim Foley’s work on flipped classrooms (predating MOOCs by several years), and David Joyner and Ashok Goel’s work on knowledge-based AI in flipped and MOOC classrooms, and my students know some of this work. I posed the question to my students:
If you were going to characterize the Georgia Tech school of thought in computing education, how would you describe it?
We talked some about the contrasts. Work at CMU emphasizes cognitive science and cognitive tutoring technologies. Work at the MIT Media Lab is constructionist-based.
Below is my interpretation of what I wrote on the board as they called out comments.
- Contextualization. The Georgia Tech School of Computing education emphasizes learning computing in the context of an application domain or non-CS discipline.
- Beyond average, white male. We are less interested in supporting the current majority learner in CS.
- Targeted interventions. Georgia Tech computing education researchers create interventions with particular expectations or hypotheses. We want to attract this kind of learner. We aim to improve learning, or we aim to improve retention. We make public bets before we try something.
- Broader community. Our goal is to have a broaden participation in computing, to extend the reach of computer science.
- We are less interested in making good CS students better. To use an analogy, we are not about raising the ceiling. We’re about pushing back the walls and lowering the floors, and sometimes, creating whole new adjacent buildings.
- We draw on learning sciences theory, which includes cognitive science and educational psychology (e.g., cognitive load theory).
- We draw on social theories, especially distributed cognition, situated learning, social cognitive theory (e.g., expectancy-value theory, self-efficacy).
I might have spent hours coming up with a list like this, but in ten minutes, my students came up with a good characterization of what constitutes the Georgia Tech School of Thought in Computing Education.
I liked Gary Stager’s argument in the post below about what’s important about the Maker Movement for schools: it’s authentic in a physical way, and it contextualizes mathematics and computing in an artistic setting.
For too long, models, simulations, and rhetoric limited schools to abstraction. Schools embracing the energy, tools, and passion of the Maker Movement recognize that, for the first time in history, kids can make real things – and, as a result, their learning is that much more authentic. Best of all, these new technologies carry the seeds of education reform dreamed of for a century. Seymour Papert said that John Dewey’s educational vision was sound but impossible with the technology of his day. In the early- to mid-20th century, the humanities could be taught in a project-based, hands-on fashion, but the technology would not afford similarly authentic opportunities in mathematics, science, and engineering. This is no longer the case.
Increasingly affordable 3-D printers, laser cutters, and computer numerical control (CNC) machines allow laypeople to design and produce real objects on their computers. The revolution is not in having seventh-graders 3-D print identical Yoda key chains, but in providing children with access to the Z-axis for the first time. Usable 3-D design software allows students to engage with powerful mathematical ideas while producing an aesthetically pleasing artifact. Most important, the emerging fabrication technologies point to a day when we will use technology to produce the objects we need to solve specific problems.
I’d love to see more detail on what they’re doing. It’s what Seymour Papert wanted for Logo, now happening not far from MIT and the original Logo experiments. I love what they say later in the piece about the goal being computing for creativity and calculation, not to become a professional software developer.
It’s a new, albeit eccentric experiment. The school isn’t launching mandatory programming courses into the schedule, exactly, but is instead having its teachers introduce coding (ideally, in the most organic ways possible) into their respective subjects. Calculation-heavy courses such as math and science, as well as humanities such as English, Spanish and history — even theater and music — will all be getting a coded upgrade.
True, Beaver may be the first of its kind to experiment with coding in every class, but the idea that more high school students should take STEM-related courses — particularly in programming and coding — isn’t new.
We have talked here before about the use of computing to teach physics and the use of Logo to teach a wide range of topics. Live coding raises another fascinating possibility: Using coding to teach music.
There’s a wonderful video by Chris Ford introducing a range of music theory ideas through the use of Clojure and Sam Aaron’s Overtone library. (The video is not embeddable, so you’ll have to click the link to see it.) I highly recommend it. It uses Clojure notation to move from sine waves, through creating different instruments, through scales, to canon forms. I’ve used Lisp and Scheme, but I don’t know Clojure, and I still learned a lot from this.
I looked up the Georgia Performance Standards for Music. Some of the standards include a large collection of music ideas, like this:
Describe similarities and differences in the terminology of the subject matter between music and other subject areas including: color, movement, expression, style, symmetry, form, interpretation, texture, harmony, patterns and sequence, repetition, texts and lyrics, meter, wave and sound production, timbre, frequency of pitch, volume, acoustics, physiology and anatomy, technology, history, and culture, etc.
Several of these ideas appear in Chris Ford’s 40 minute video. Many other musical ideas could be introduced through code. (We’re probably talking about music programming, rather than live coding — exploring all of these under the pressure of real-time performance is probably more than we need or want.) Could these ideas be made more constructionist through code (i.e., letting students build music and play with these ideas) than through learning an instrument well enough to explore the ideas? Learning an instrument is clearly valuable (and is part of these standards), but perhaps more could be learned and explored through code.
The general form of this idea is “STEAM” — STEM + Art. There is a growing community suggesting that we need to teach students about art and design, as well as STEM. Here, I am asking the question: Is Art an avenue for productively introducing STEM ideas?
The even more general form of this idea dates back to Seymour Papert’s ideas about computing across the curriculum. Seymour believed that computing was a powerful literacy to use in learning science and mathematics — and explicitly, music, too. At a more practical level, one of the questions raised at Dagstuhl was this: We’re not having great success getting computing into STEM. Is Art more amenable to accepting computing as a medium? Is music and art the way to get computing taught in schools? The argument I’m making here is, we can use computing to achieve math education goals. Maybe computing education goals, too.
This class sounds cool and similar to our “Computational Freakonomics” course, but at the data analysis stage rather than the statistics stage. I found that Allen Downey has taught another, also similar course “Think Stats” which dives into the algorithms behind the statistics. It’s an interesting set of classes that focus on relevance and introducing computing through a real-world data context.
The most unique feature of our class is that every assignment (after the first, which introduces Python basics) uses real-world data: DNA files straight out of a sequencer, measurements of ocean characteristics (salinity, chemical concentrations) and plankton biodiversity, social networking connections and messages, election returns, economic reports, etc. Whereas many classes explain that programming will be useful in the real world or give simplistic problems with a flavor of scientific analysis, we are not aware of other classes taught from a computer science perspective that use real-world datasets. (But, perhaps such exist; we would be happy to learn about them.)
I’ve told you a bit about how the Media Computation class went this summer, with the new things that I tried. Let me tell you something about how the “Computational Freakonomics” (CompFreak) class went.
The CompFreak class wasn’t new. Richard Catrambone and I taught it once in 2006. But we’ve never taught it since then, and I’d never taught it before on my own, so it was “new” for me. There were six weeks in the term at Oxford. Each week was roughly the same:
- On Monday, we discussed a chapter from the “Freakonomics” book.
- We then discussed social science issues related to that chapter, from the nature of science, through t-tests and ANOVA, up to multiple linear regression. Sometimes, we did a debate about issues in the chapter (e.g., on “Atlanta is a crime-ridden city” and on “Roe v. Wade is the most significant explanation for the drop in crime in the 1990’s.”)
- Then I showed them how to implement the methods in SciPy to do real analysis of some Internet-based data sets. I give them a bunch of example data sets, and show them how to read data from flat text files and from CSV files.
At the end of the course, students do a project where they ask a question, any question they want from any database. Then, they do it again, but in pair, after a bunch of feedback from me (both on the first project, and on their proposal for the final project). The idea is that the final projects are better than the first round, since they get feedback and combine efforts in the pair. And they were.
- One team looked at the so-called “medal slump” after a country hosts the Olympics. The “medal slump” got mentioned in some UK newspapers this summer. One member of the team had found in his first project that, indeed, the host country wins a statistically significant fewer medals in the following year. But as a pair of students, they found that there was no medal “slump.” Instead, during the Olympics of hosting, there was a huge medal “bump”! When hosting, the country gets more medals, but the prior two and following two Olympics all follow the same trends in terms of medals won.
- Another team looked at Eurozone countries and how their GDP changes tracked one another after moving to the Euro, then tried to explain that in terms of monetary policy and internal trading. It is this case that Eurozone countries who did move to the Euro found that their GDP started correlating with one another, much more than with non-Euro Eurozone countries or with other countries of similar GDP size. But the team couldn’t figure out a good explanation for why, e.g., was it because internal trading was facilitated, or because of joint monetary policy, or something else?
- One team figured out the Facebook API (which they said was awful) and looked at different company’s “likes” versus their stock price over time. Strongly correlated, but “likes” are basically linear — almost nobody un-likes a company. Since stock prices generally rise, it’s a clear correlation, but not meaningful.
- Another team looked at the impact of new consoles on the video game market. Video game consoles are a huge hit on the stock price of the developing company in the year of release, while the game manufacturers stock rises dramatically. But the team realized a weakness of their study: They looked at the year of a console’s release. The real benefit of a new console is in the long lifespan. The year that the PS3 came out, it was outsold by the PS2. But that’s hard to see in stock prices.
- The last team looked at impact of Olympics on the host country’s GDP. No correlation at all between hosting and changes in GDP. Olympics is a big deal, but it’s still a small drop in the overall country’s economy.
One of my favorite observations from their presentations: Their honesty. Most of the groups found nothing significant, or they got it wrong — and they all admitted that. Maybe it was because it was a class context, versus a tenure-race-influenced conference. They had a wonderful honesty about what they found and what they didn’t.
I’ve posted the syllabus, course notes, slides that I used (Richard never used PowerPoint, but I needed PowerPoint to prop up my efforts to be Richard), and the final exam that I used on the CompFreak Swiki. I also posted the student course-instructor opinion survey results, which are interesting to read in terms of what didn’t work.
- Clearly, I was no Richard Catrambone. Richard is known around campus for how well he explains statistics, and I learned a lot from listening to his lectures in 2006. Students found my discussion of inferential statistics to be the most boring part.
- They wanted more in-class coding! I had them code in-class every week. After each new test I showed them (correlation, t-test, ANOVA, etc.), I made them code it in pairs (with any data they wanted), and then we all discussed what they found in the last five minutes of class. I felt guilty that they were just programming away while I worked with pairs that had questions or read email. I guess they liked that part and wanted more.
- I get credit from the students for something that Richard taught me to do. Richard pointed out that his reading of cognitive overload suggests that nobody can pay attention for 90 minutes straight. Our classes were 90 minutes a day, four days a week. In a 90 minute class, I made them get up halfway through and go outside (when it wasn’t raining). They liked that part.
- Students did learn more about computing, inspired by the questions that they were trying to answer. They talk in their survey comments about studying more Python on their own and wishing I’d covered more Python and computing.
- In general, though, they seemed to like the class, and encourage us to offer it on-campus, which we’ve not yet done.
Students who talked to me about the class at the end said that they found it interesting to use statistics for something. Turns out that I happened to get a bunch of students who had taken a lot of statistics before (e.g., high school AP Statistics). But they still liked the class because (a) the coding and (b) applying statistics to real datasets. My students asked all kinds of questions, from what factors influenced money earned by golf pros, to the influences on attendance at Braves games (unemployment is much more significant than how much the team is in contention for the playoffs). One of the other more interesting findings for me: GPD correlates strongly and significantly with number of Olympic gold medals that a country wins, i.e., rich countries win more medals. However, GPD-per-capita has almost no correlation. One interpretation: To win in the Olympics, you need lots of rich people (vs. a large middle class).
Anyway, I still don’t know if we’ll ever offer this class again, on-campus or study-abroad. It was great fun to teach. It’s particularly fun for me as an exploration of other contexts in contextualized computing education. This isn’t robotics or video games. This is “studying the world, computationally and quantitatively” as a reason for learning more about computing.