Thoughts on ICLS 2010: From reading poetry, to CS as infinite science

I got back from the International Conference of the Learning Sciences last Friday, and spent less than 48 hours visiting with family, preparing my workshops for this week, burning slides, and heading out to Philadelphia on the Fourth of July.  I just taught two days of workshops at The College of New Jersey, and leave tomorrow morning (from Philly, where I am right now) for Blacksburg, VA.  You can follow along in the galleries to see what teachers are doing.  (To all my friends in the Philly area, my apologies for not looking you up.  I’m pretty exhausted after these eight-hour days of teaching, so I’m just laying low.)

I wanted to write up some thoughts about ICLS before I forgot them all.

• ICLS had around 600 attendees — about 1/2 the size of SIGCSE, and six times the size of ICER.
• Carl Weiman gave a really impressive opening keynote talk, on the similarities between the various science disciplines in terms of research on learning.  Most impressive for me: It was a completely different talk than his SIGCSE talk!  A point that I found interesting related to an earlier blog piece — he said that learning the language of the discipline (the specialized vocabulary) is key and not knowing it interferes with learning.
• I learned a new term “dorsal teaching.”  That’s where the (typically, engineering and mathematics) teacher turns to the board and writes, and all you can see is their back and one writing arm (“dorsal fin”) sticking out.
• I told Jennifer Turns of U. Washington about Mike Hewner’s recent studies, and she says that she sees similar issues among her engineering undergraduates.  She says that they complain to her, “When are we going to get to engineering?!?” and she sees engineering in all their classes.  She likens the problem to the “dancing gorilla” awareness problem.  If you don’t know what Engineering is, you won’t recognize and attend to it when you’re studying it.
• A common theme (or hole, really) for me at ICLS was how the learning scientists are studying similar topics to the computing educators, but not asking the same questions.  There were umpteen papers and posters studying the use of videogames to support learning.  Some of them tracked males and females separately, and reported no differences in learning outcomes.  But not a single study asked if the girls were as engaged by the videogames as the boys!  When I asked that question in a talk, I was told, “No, the girls weren’t as interested in the games” and “Some of the boys got far too focused on learning and designing the videogames, but didn’t pay attention to the learning goals.”  I heard one talk about helping engineering students learn about ecology through video games, and it was perfectly okay with them that the male:female ratio was 5:1.  I heard another talk about helping improve reading through videogames, and all their subjects were male, and they argued that that’s important because boys do worse at reading than girls.  I am not pointing this out to critique the learning science researchers, because for their questions, maybe those other issues aren’t as important.  I found it fascinating that two such similar disciplines look at the same situations with radically different questions and goals.
• Pam Grossman gave a keynote talk on reading poetry, where she chided the learning sciences community for not studying learning in the humanities.  She talked about the complexity of the task of reading poetry.  She talked about the challenge of getting through ambiguity in a poem, filling in the gaps (especially around words whose meaning you are unsure), and expecting to read a poem multiple times to get it.  Sally Fincher was sitting next to me, and she mentioned that one also has to have life experience to draw upon to relate to a poem.  I realized that this was actually a really good list for the challenges of reading code.  Not knowing all the subfunctions/methods/whatever being called, code may seem ambiguous, and you may have to read it several times, looking things up, to get it.  Having read a bunch of code previously makes it easier to read new code.
• Our panel on learning in the computing disciplines was fascinating — slides are available on-line.  We could hardly have come more from different directions.  I talked about our learning science related challenges in computing education.  Yasmin gave this talk drawing on the history of computing education, especially for K-12, from Logo through tangible programming with LilyPad.  Ulrich Hoppe argued against trying to engage students and against using tangible programming, and in favor of  using Prolog instead of imperative or object-oriented languages.  And Sally drew it all together with some great quotes from Kuhn and Agre.

In answer to a question about biology, I made a claim in the panel that I’d like to bounce off you.  I argued that computer science is far bigger than any specific science, in the same way that mathematics is bounded only by human imagination while any natural science is bounded by the real world.  If you take any scientific phenomenon, there can be at least one program that simulates that phenomenon correctly, and you can study that using scientific methods.  However, there are an infinite number of programs that get close to simulating that phenomenon, but get it wrong, and you can only figure it out that it’s wrong by using scientific methods (experimentation, measurement, hypothesis setting) to figure that out.  In some sense, each program is another natural world to study, and computer science is about understanding any program.  Thus, our domain is infinitely larger than any natural science.  No wonder it’s so hard to get kids to pass CS1!