Teaching Computational Thinking across an Entire University, With Guest Blogger Roland Tormey

During Spring Break, Barbara and I were invited to go to Switzerland.  Sure, when most people go someplace warm for Spring Break, let’s head to the mountains!

Roland Tormey organized a fascinating workshop at EPFL in Lausanne, Switzerland (see workshop page here) to inform a bold and innovative new effort at EPFL. They want to integrate computational thinking across their entire university, from required courses for freshman, to support for graduate students doing Computational X (where X is everything that EPFL does).  The initiative has the highest level of administrative support, with the President and Vice-President of Education for EPFL speaking at the workshop.  The faculty really bought in — the room held 80-some folks, and it was packed most of the day.

Roland got a good videographer who captured both of the keynotes well.  I had the first keynote on “Improving Computing Education with Learning Sciences: Methods for Teaching Computing Across Disciplines.”  I argued that we need different methods to teach computing across the curriculum — we can’t teach CS the same way we teach CS majors as future software developers.  I talk about Media Computation, predictions (and they caught my audio demo with ukulele playing well), subgoal labeling, and Parsons problems.

Shriram Krishnamurthi had the second keynote on “Curriculum Design as an Engineering Problem.”  He talked about the problems of transfer and how Bootstrap works.  I liked how he broke down the problem of transfer — there there are three requirements: Deep structural similarities between the problems, explicit instruction, and a process for performing tasks.  He showed how all other design disciplines have multi-stage processes, use multiple representations in their designs, and look at problems from multiple viewpoints.  Mostly in CS classes, we just code.  I learned about how Bootstrap scaffolds problem-solving, and includes all of those elements.  I recommend the talk.

Barb’s panel on teaching computational thinking wasn’t captured.  She talked about the methods she’s developed for teaching computing, including her great results on Parsons problems.  In a short talk, she gave a lot of pointers to her work and others’ on how to teach CT.

Roland sent me a note with what he took away from the workshop. I thought it was a great list, so with his permission, I’m including it here:

For me, we also had a lot of other valuable take home points from the day:

(1) We need to work on putting Computational thinking (and maybe Math and Physics too) into the context of the students’ own disciplines — at least, though the examples and exercises we choose.

(2) The drive to better develop scientific thinking in disciplines like chemistry and life sciences and the development of CT are entirely consistent, but one shouldn’t eclipse the other. It’s not about replacing existing scientific processes with CT. It’s about augmenting them.

(3) We need to help professors gather data on effective methods of teaching as well as help them become aware of methodologies with demonstrated effectiveness (like the Parsons Problems for example).

(4) The exercises and exercise sessions will be crucial for making the link between CT and disciplines, but this implies giving the doctoral and teaching assistants a clear understanding of the goals and methods of CT. They have to understand what we are trying to achieve.

(5) CT provides an understanding of, a language for, and a toolbox for analysing processes, and these can be applied in a lot of domains. However that is not going to happen unless we explicitly teach CT in ways that promote near and far transfer

(6) We need to make the most of the EPFL initiative by properly evaluating the impact, which implies the need to collect some pre-intervention data now.