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Dijkstra's paper: my footnotes
By
Steve Draper,
Department of Psychology,
University of Glasgow.
These are just my notes, mostly from my first read through.
I have no real idea at the moment, whether these are of low, medium, or high
interest to others.
Really, I also want to locate each note back in a section;
I'd like to only have to write the id nmb once, not twice.
And I'd like an extra section on collected key phrases/arguments summing up a
section when possible.
[1]
section B2d
(para.4) This argument amounts to an argument for shallow, not deep, learning;
avoiding links with existing knowledge.
This is to some extent anti-constructivist.
Or conversely, we could seen him as an unusual voice pointing out the advantages
in some cases of shallow learning, which is not generally admitted in
educational writing.
[2]
section A3
Scale. He's wrong.
"Deep time" as dealt with by geologists and then biologists like Darwin have
established it, and dealt with it. We are now pretty certain that the age of
our sun and earth is about 4.7 * 10
9; and the universe about 3 times
that age. If you work at it, you can get a grip on these scales, but you do have
to work at it. On the other hand, you also have to work to get a grip on much
smaller numbers such as the distance in miles (or kilometers) to places you
know; dates such as 1860 mean nothing until you have drilled yourself in a
handful of dates in that century.
Also: see Paul Nurse's view that there needs to be, and is coming to be, and
fifth great theory in biology to tackle the huge complexity of a single cell:
about 105 chemical reactions proceeding simultaneously in each cell.
[3]
section B2c
His anti-anthropomorphic campaign.
However a) "talk to" usually IS about communication
b) Biology and imputing intention to evolution or to habits: they often discuss
it and generally don't think it's too big a trap.
[4]
section A3
Operational thinking about behavour: but you should reason =about set.s
But note Bertrand Russell's point that (in 1913 when he wrote this) the highest
most developed science tends NOT to be about causation, but about invariants and
relations. You can "predict" where a planet was long ago as accurately as where
it will be in the future.
[5]
section A3
I think he's wrong in some ways, but we'll never have a decent theory and
practice of CCSE until we either accept and implement his ideas OR have better
arguments about what is wrong with them.
[6]
section A2
He says teaching is bad by always presenting things as easy, familiar, and so
unexciting. And this is also a problem because it tells the student that it
well be easy to learn and no need to think much about it: leading to shallow
learning.
But he doesn't recognise that this is a permanent dilemma in education:
it's just as bad to the do the opposite, and put them off trying by emphasising
the novelty and difficulty.
In fact we should recognise what is new; and what is an extension of the old.
[7]
section A3
Bayard: talk about what you haven't read; operate around what you couldn't
build yourself nor understand.
[8]
section A3
Testing: only test for errors already known and common.
BUT testing for unheard of errors (testing to discover the unknown unknowns)
is necessary, done by all sensible engineers; but essential but untheorised.
And so not faced.
[9]
section A3
Digital designs that violate continuity: stairs, cogwheels. The piano not the
cello.
Summaries
[91]
section A3
The two novelties he identifies are:
Scale (109 not 103);
Digital not continuous. Not continuous change in input leading to a continuous
change in output. This is not completely true. A program processing a range of
numbers is more likely to have errors at the ends of the range than in the
middle.
N.B. integers are digital vs. real (continuous) numbers in maths
[92]
section A4
These novelties are radical
because they have to be tackled differently.
[93]
section A3
These novelties are radical
because they have to be tackled differently.
[94]
section B2
Educational problems: because of using fundamentally faulty metaphors; because
of using old methods for new material.
[95]
section B1
Scientific consequences:
Computing is using human symbol manipulation
to create an abstract symbol manipulation (the code);
which when supplied to a computer, executes mechanical symbol manipluations.
Deal with ALL elements of a set by ignoring them and dealing with the definition
of the set.
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