the new math: old battle of the lovees was: PDP1 3602

snip -- stuff about Dijkstra replied to in another post

more snip -- the list here is of background for CS

When I say "proofs" I mean basic proof techniques -- how to set up a proof by contradiction, a proof by induction, etc.. Often one practices these techniques on simple problems (e.g., proving that the square root of two is irrational, or the sum of the integers from 1 to n is n(n+1)-2), but the point is to teach the techniques.

When I say "logic" I mean formalizations of the idea of a logical argument -- how to write down "if A, then B" in symbols and then derive conclusions from a set of hypotheses .... I'm not describing this very well, but maybe something of the idea will come across.

I'm not sure exactly what you mean by "how to build an algebra or a geometry", and how that's different from "theory".

snip a bunch of stuff where we're in agreement -- I think!

You know, this is probably nitpicking, but I don't get how physical limits are relevant to understanding buttembler language or machine language, or even to circuit design *at the level of AND and OR gates*. They certainly matter to what happens at levels even lower than that, and maybe if you don't understand those lower levels, then you can't

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Yeah, I guess I'd have to say the same thing about the ones I took, despite occasional efforts from the instructors to actually teach us something.

That wasn't a surprise. The surprise was that many times the results weren't even close to what they were supposed to be. I got pretty good at writing lab reports blaming this on "experimental error", but the implications .... Let me give an example.

One of the experiments involved taking time-lapse pictures of a falling object and using the result to compute a value for the gravitational constant g. I ended up with a value that was laughably different from the accepted one. What did I learn from this? I'm not sure, other than it's tougher than it might seem to accurately measure distances on a somewhat fuzzy photograph.

What did I not learn? How to design an experiment that would give a better result. How to tell, for a problem where there is no "right answer" (the accepted value of g here), whether the result is any good. And probably many other things I'm not thinking of right now.

I don't remember ever having that problem -- I was eager to get them done and get back to an environment where I felt more competent.

And I'm not sure longer labs would have helped anyway.

Like I said, I did learn that experiments didn't always come out the way they were "supposed to", but that doesn't seem to me like much of an insight. I mean, in real science, there isn't really a "how it's supposed to come out" to compare experimental results to, so you have to be able to analyze the results you got in a way that we weren't taught, as far as I can remember. If the result you got contradicts results other people got in the past, does that mean you did something wrong, or the other people did? Stuff like that.

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-- B. L. Mbuttingill ObDisclaimer: I don't speak for my employers; they return the favor.