I’ve read a lot of astounding things recently. This is probably the most astounding:
“We are now living in a global state that has been structured for the benefit of non-human entities with non-human goals. They have enormous media reach, which they use to distract attention from threats to their own survival. They also have an enormous ability to support litigation against public participation, except in the very limited circumstances where such action is forbidden. Individual atomized humans are thus either co-opted by these entities […] or steamrollered if they try to resist.
In short, we are living in the aftermath of an alien invasion.” – Charles Stross.
Far fetched? Corporate brands now have personalities. And have more spending power than the US Government. And, with my Damon Knight head on, I’m wondering what they eat…
I suspect data. Unfortunately, non-human entities aren’t the pickiest of eaters, they’ll eat pretty much anything and hope the label is right. Remember how all those banks and hedge funds got ill:
“The quants on Wall Street now, in large part, don’t have market sense, and they don’t have a long history of experience with what’s wrong with models. They simply take the math. They, themselves, are wowed by it, and they build a machine which, in their imaginary world, must work just fine and must be safe and sound. But in fact, with the securitized pools that were put together to underlie these things, terrible, unsound mortgages were put into the pools, and that’s not directly the fault of the quants, except that the quants should, if they were poking around and curious, have known how unsound these things were.
It’s more the fault of the sell side, who didn’t care what was in there and figured they would sell it off, and they wouldn’t be caught holding the bag, and of the regulators in government who simply looked the other way.” – Ed Thorp
The failure here is actually one of translation. The “quants” speak a pure mathematical language, the “sell side” speak the language of commerce, of profit and advantage. And the non-human entities – they’re with the “sell side”. In Higher Education we are beginning to learn the “sell side” language too.
Of course, the financial bubble was really an insurance bubble – risk packaged and resold, labelled in increasingly misleading ways by algorithms and filters designed to maximise apparent value. But the numbers were always just numbers, the further away from the source they got, the less what the numbers meant mattered. It was the aggregation that made them attractive, but that very aggregation also made them less meaningful. And – if you like – less nutritious.
Enter David Wiley. He argued at eli2011 that although behaviour data in an LMS is limited it could be usefully captured and analyzed. Every industry does a better job than higher ed of using data to improve service. With sufficient data, we could turn teaching into more of a science. (paraphrasing Peter Froehlich‘s report). This would be an odd argument to make in 2008. In 2011, after all the chaos caused in the financial and insurance worlds by using approximations from data to drive up value, it’s just bizarre. (He’s not the only one pro-analytic – there was a whole conference about this stuff this year in Banff)
You see a parallel trend around the gamification of learning, with stuff like the Khan Academy Badges and the new Mozilla stuff. People are genuinely celebrating because an assessment of learning is now available from a non-human.
But isn’t a central characteristic of a gamified system that it is gameble? Didn’t those cheeky algorithms that those clever quants knocked up increase the appearance of value rather than realisable value? And in a world where learners already “learn to the test”, how long before the only thing higher education teaches you is how to reverse-engineer grading algorithms?
More importantly, why are the radical edges of the new HE so keen to jump aboard a three-years past band wagon that has already lost wheels? My colleague Amber once warned:
“At a time when the very notion of state-funded education is under attack, is it really a good idea to go about presenting the break-up of the education system as an opportunity to try out risky ideas?”
“Experiment, communicate, be passionate. But don’t make anything taboo: don’t exclude the pragmatists, the cautious implementators, the people who refuse to be swept along by your enthusiasm. Most of all, be very careful what you wish for. Because you might just get it and I’m not sure you’ll like it.”
She was talking about “open education” – but we need to be similarly suspicious of learner analytics. No matter how delicious our non-human software-peddling phone-hacking overlords find them.
This post represents my personal opinions, and not those of my employers, projects, or programmes I am or have been responsible for. This post is available under a CC-BY license. For this post, I especially want to be clear that there is clearly a lot of useful and interesting work to be done with learner activity data – CMU’s OLI stuff springs to mind for a start, and I know there is a lot of very cool JISC stuff in this space too – it’s just that historically us “quants” have been unable to reliably emphasise the limitations of data analysis to the “sell side”…
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