On Fri, 27 Nov 2009, Richard O'Keefe wrote:
> Anthony Robins
> http://www.cs.otago.ac.nz/department/staff.php?name=Anthony%20Robins
> recently noticed
> http://www.cs.otago.ac.nz/staffpriv/anthony/publications/pdfs/RobinsLEM.pdf
> that the characteristic bimodal distribution of outcomes in CS1
> can be explained by a very simple statistical model.
> (Roughly speaking: a series of topics where failure at any point makes
> later failure more likely, success makes later success more likely.)

First, I recommend Anthony Robins' paper as a refreshing alternative
voice to the repeated claims that passing a zero pre-requisite
programming course requires an innate talent (the "geek gene).  But, as
with all research, there are a issues with his work that require follow
up.

First, I'd like to see evidence collected, from multiple institutions,
examining the grade distribution in CS1.  I think the bimodal
distribution is an urban myth. (Would anyone care to formally define
what they mean by a bimodal grade distribution?  That's a rhetorical
question, and no correspondence is required.)

Second, if we are going to make research claims about grade
distributions, then we need to bring research standards to bear on the
contruction of our research instrument - the exam.  Most exams are
idiosyncratic constructions, of dubious validity and reliability.

Third, I regard Anthony's model as a good counter argument to the
conventional "geek gene" argument, but if I ignore the "geek gene"
argument and merely examine Anthony's model in isolation, then I
suspect that the number of free parameters in his model means that
you can get any distribution you want, by a suitable choice of
parameter values.

Raymond

Dr. Raymond Lister
Science Teaching and Learning Fellow,
University of British Columbia,
Department of Computer Science,
Vancouver, Canada

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