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More good stuff, thanks Martin.

But ... and it's a minor point ... my power law spider sense is tingling! The black line with its equation looks a lot like a power law.

Obviously, when she gets back from maternity leave, Katy will be wanting to explore an exponential fit, and a two-population exponential fit (like in the Educause paper from Coursera folk: http://www.educause.edu/ero/article/retention-and-intention-massive-open-online-courses-depth-0 - though notably they do not discuss what other fits they explored) and all sorts of other things, following best practice (starting here: http://tuvalu.santafe.edu/~aaronc/powerlaws/) before claiming that these data are best fit by a power law.

Sorry - it's just red rag to a bull for me.


My evidence is anecdotal, but I dimly recall that the OU doesn't 'count' students who've registered for a course until they submit their first TMA. Is that still the case? There doesn't seem to be any other way of knowing remote/distance students are physically studying a course until they complete an assignment.
Traditional and conventional campus universities - so I believe - use a different measure, which is something like counting students who have stayed until a third of the way through their first term, but HEFCE and other Councils will be the source for those data and that criterion.
It's a bit like signing up for a gym immediately after the excesses of the festivities we are approaching: many sign up, but fade away after one month...


This is good. I can see that the MOOC going from few hundred to 40 participants I ran last year fits with this pattern without needing to be truly massive (just much bigger than usual for the subject).

But there is more to it than just the numbers. I wrote more about a year ago about how the drop out rates don't really matter given the investment and the experience and are not as drastic when compared with meat space universities in real terms: http://researchity.net/2012/08/18/mooc-motivations-and-magnitudes/

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