“We don’t work with greasy machines!”

“In the Mathematics Department we don’t work with greasy machines,” replied one of my undergraduate math professors with a sneer. “You’re going to have to go to the Applied Math department if that’s what you want to do.”

That was his response when I asked him whether I could learn to write computer programs in a course given by the Math Department. This was back in 1966… so surely matters are better now.

Or are they?

I just read an interesting post by Mark Guzdial, titled “What would convince faculty in other disciplines that programming is useful?”; it shows, among other things, that I was wrong in thinking that matters are better now. Here is the elevator pitch:

  • Mathematics faculty are clearly dubious that (a) programming to apply mathematics topics leads to more mathematics learning and (b) computer science is even related to mathematics.

This observation comes from analyzing a survey of 20 different universities, so it’s not a fluke. Do read the entire post, and I take the unusual step of saying “even the comments,” which I rarely recommend. At least high-school math teachers don’t feel this way — or the ones I’ve worked with, at any rate. I suppose I travel in circles that anti-computing teachers are unlikely to frequent.

Guzdial and others are trying to respond constructively to these professors’ attitudes:

I’ve written one NSF proposal and am developing several collaborators here at Michigan to explore the theme: What should our programming languages look like to be a good match for the content in different disciplines? Bootstrap makes a great argument that their form of Scheme is a good match to algebraic notations. I know that Shriram Krishnamurthi has been thinking a lot about the match from Pyret to Physics in their new curriculum. I’ve proposed (to NSF) do hold participatory design sessions with great high school teachers (from multiple disciplines) with different kinds of programming notations (including Wolfram Language, Scheme, and GP) to express different ideas, and have them work with us to say: What works? What is a bad match to their discipline and the concepts they’re trying to teach? What will students understand? What will be most effective for teaching?

And that triggers a shout-out to Shriram, whom I haven’t written about in 12 years!

Categories: Math, Teaching & Learning, Technology