This is somewhat tangiential, but I encountered a similar phenomenon in a study I ran recently, and I'd be interested to hear what people on this list had to say about it.
Long story short, I gave my subjects a 4-hour window to complete a task, but found every one either did it within two hours, or failed to complete it at all within four (despite continuing to work, apparently understanding the problem, etc). I can't say the distribution is strictly "bimodal", but they definitely fell into two distinct classes. The paper is at http://ecs.victoria.ac.nz/twiki/pub/Events/PLATEAU/Schedule/plateau09-luff.pdf, and the graph is on page 5 (section 4.4). The "success breeds success" explanation does sound like an intuitive fit. I would be very interested to see what others have to say. (Other comments on the study are, of course, also welcome). Meredydd On Fri, Nov 27, 2009 at 6:43 PM, Raymond Lister <raym...@it.uts.edu.au> wrote: > > > 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 >
