[Edu-sig] FYI: PataPata postmortem link
Paul D. Fernhout
pdfernhout at kurtz-fernhout.com
Sun Nov 26 22:50:02 CET 2006
Just as an FYI, as a way to wind up the PataPata project (or at least one
phase of it), I wrote a lengthy postmortem critique of the PataPata
project to date, plus ideas for where to go from here. You can read the
critique by following this link:
"PataPata critique: the good, the bad, the ugly"
For reference, the PataPata project is/was """an experiment to support
educational constructivism on the Python platform, inspired by "Squeak"
and "Self", but going beyond those in a Pythonic way."""
From the introduction: """It's been about three months from my last post
to the PataPata list as well as my last major change to the system. I have
been thinking about the system in the intervening time, and feel ready to
produce a critique of it as an experiment (sort of as a, sadly,
"postmortem" report). Others are welcome to chime in. This critique covers
various good, bad, and ugly results from this experiment, and then
outlines some thoughts on where to go next. This note
marks the end of this phase of the PataPata experiment. I am uncertain if
this project on SourceForge will see more development, but I am certain if
there is more development on this particular SourceForge project, it will
likely be in a radically different direction than the work published here
to date. """
By the way, my decision to write a critique of PataPata was inspired in
part by this paper by Drew McDermott, "Artificial Intelligence meets
http://portal.acm.org/citation.cfm?id=1045340 [fee based link]
The core of the paper is here:
"McDermott explains how all research should be based on actual
implementations, and be a thorough report on them. What is needed is a
very clear picture of what was tried, what worked, what didn't, why didn't
that work. And there must be a working program that later researchers can
play with. Later research can build on these partial solutions, and report
the exact improvements made since the previous version, the improvement in
performance, etc. As McDermott states:
The standard for such research should be a partial success, but AI as
a field is starving for a few carefully documented failures. Anyone can
think of several theses that could be improved stylistically and
substantively by being rephrased as reports on failures. I can learn more
by just being told why a technique won't work than by being made to read
between the lines."
Thanks again to people here for your previous feedback on the project.
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