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Why MIT switched from Scheme to Python (2009)

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284 points borski | 1 comments | 25 Jul 25 16:38 UTC | HN request time: 0.217s | source
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MontyCarloHall ◴[25 Jul 25 17:23 UTC] No.44685710[source]▶
Isn't this just part of the broader trend of CS departments switching away from teaching computer science to teaching computer engineering, which in turn is part of the more general trend of colleges becoming more vocational?

LISP dialects like Scheme are excellent for teaching pure computer science because they are the closest thing to executing lambda calculus expressions. Whereas Python is excellent for teaching applied computer engineering, because it's essentially executable pseudocode for imperative languages, and imperative languages are the most common language a computer engineer encounters in the real world.

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darksaints ◴[25 Jul 25 18:10 UTC] No.44686308[source]▶
Colleges becoming more vocational is a consequence of colleges becoming more expensive for students. If you are paying all of that money for college, you better get a good job out of it. I don't see that as a bad thing necessarily, but it would definitely be nice if we had better paths for those who want to end up in research.

I'd argue that SML (or derivative thereof) would make for a better teaching language, for both the lambda calculus aspect and the type theory aspect.

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1. marsten ◴[25 Jul 25 21:29 UTC] No.44688710[source]▶
I attribute the curriculum shift to something slightly different, which is the changing perception of CS as a career.

When I was in college in the late 1980s, CS was not perceived as the moneymaking career it is today. Accordingly the kids who went into CS were typically the nerds and hackers who truly loved the field.

Many kids now perceive CS as a safe, lucrative career option akin to becoming a doctor or lawyer. It attracts many students who are smart but perhaps not as intrinsically excited about the field. The universities adjusted their curricula to what these students care about: Less beautiful theory, and more practical training.

A similar thing happened in statistics. At one time it was hardcore stats nerds. Now "data science" has brought a ton more people into the field and the teaching methods have changed dramatically.