Ah! That is interesting!
For me, the learning curve is like the energy required for a chemical or
a biochemical reaction to occur. Thus, on the X-axis, you could have the
amount of R learned/assimilated, and on the Y-axis, you have the
energy/effort/time (or whatever measure of learning effort you like to
use). Thus, a steep learning curve means you have to provide a lot of
effort to reach a little bit of learning. A flatter learning curve
allows you to progress faster with little effort (like running on a flat
ground versus climbing a mountain, to reuse John's metaphor).
Still with the (bio)chemical reaction analogy in mind, anything that
makes R more "digest" (a good tutorial, a good GUI, etc.) is like an
enzyme that allows for the (bio)chemical reaction to occur with less energy.
Otherwise, I am always amazed that people could use a metaphor like
here, a reference to the shape of a curve on a graph, without even
knowing what exactly are the X- and Y-axes. Shame on us!
Best,
Philippe
..............................................<°}))><........
) ) ) ) )
( ( ( ( ( Prof. Philippe Grosjean
) ) ) ) )
( ( ( ( ( Numerical Ecology of Aquatic Systems
) ) ) ) ) Mons University, Belgium
( ( ( ( (
..............................................................
On 13/12/10 00:36, William Revelle wrote:
I share with Carl about the misuse of the term learning curve.
The original derivation was from learning theory where one plotted
number of correct responses on the y axis against trial number on the x
axis. Steep learning curves thus implied rapid learning (of easy
material). Flat learning curves implied slow learning (of difficult
material).
When I complain about this misuse of the term to my psychological
colleagues, they smile, agree with me that their usage was incorrect,
and then suggest I go back to worrying about how to code things in R.
When I teach R, I suggest that yes, the learning curve is steep, smile
and then point out that therefore it is easy to learn. Realistically, as
is true of learning any skill, the curve is negatively accelerated with
asymptotic learning achieved only by the wizards of Core R.
Bill
At 8:42 AM +1100 12/13/10, John Maindonald wrote:
Surely what is envisaged is the sheer effort involved in climbing
a step mountain side. It does not have a graph in mind. If one
wants to change the metaphor and turn it into a graph, it is not
at all obvious what the horizontal axis ought to be, though
various rather strained interpretations can be proposed.
There is a further aspect to the metaphor that deserves attention.
The reward for negotiating the steep learning curve is to reach
a great height, where marvellous vistas spread out before the
climber!
John Maindonald email: [email protected]
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
http://www.maths.anu.edu.au/~johnm
On 11/12/2010, at 8:25 AM, Rolf Turner wrote:
I agree with you completely about ``begging the question''. The
nearly universal misuse of this expression drives me crazy. I'm
not so sure about ``steep learning curve'' however. My impression
is that this phrase has *always* been used to convey the idea that
a subject area is difficult to learn, whence to use it (as you suggest)
in the sense that the subject area can be learned quickly would be to
change the original meaning of the phrase. That would be undesirable,
even given that the original meaning is counter-intuitive.
I recall having heard/read a ``justification'' for the original meaning
to the effect that what is envisaged is plotting effort expended on
the *y* axis and knowledge level on the *x* axis. Thus a steep learning
curve would entail expending a great deal of effort for a small increase
in knowledge.
I agree that this is a silly choice of axes --- I certainly wouldn't
make
such a choice. But I don't suppose that there's any law against it.
cheers,
Rolf Turner
On 11/12/2010, at 4:22 AM, Carl Witthoft wrote:
Next to "begging the question," the phrase "steep learning curve" is
probably the most misused cliche out there.
A 'learning curve' represents knowledge (or understanding) as a
function
of time. THerefore, the steeper the better.
Please help save the English language from descent into Humpty-Dumpty
land, and train your colleagues in the correct usage of both these
terms.
Carl
Message: 2 Date: Thu, 9 Dec 2010 09:51:27 -0800 From: Payam
Minoofar<[email protected]> To:
"[email protected]"<[email protected]> Cc:
"[email protected]"<[email protected]> Subject:
[R-SIG-Mac] R for Mac, good enough?
Message-ID:<[email protected]>
Content-Type: text/plain; charset="us-ascii"
The power of R is virtually unmatched, and R for Mac works extremely
well.
The learning curve is steep, however, and documentation is difficult
>>> to grasp, even though it is abundantly available. I am more partial
to a commercial data analysis package with which I grew up, but I
have done enough work with R on the mac platform to recommend it
highly.
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