MOOCs in an hourglass economy

Last time we discussed Alexandre Borovik‘s analysis of the crisis of the mathematical education and its socio-economic roots and impacts.

While I fully agree with Borovik’s analysis, I do miss one factor that can be important for the future of education – Massive open online course (MOOCs). They have several limitations in their current iterations and it is almost comic to see the awkward monetization schemes that many providers are currently experimenting with. However, I think that they do have a disruptive potential on higher education and it is only a question of time that we will figure out how to do them better.

The promise of MOOCs

MOOCs can address several of Borovik’s requirements for a better education. They provide wide access to the best mentors, allowing access to some elements of the “deep education” to a larger audience of pupils. Some personalization of the content is possible, though they can’t be as deeply personal as say a Zunft system. In my opinion, however, they provide a very acceptable trade-off point on the availability – personalization axis.

While I was lucky enough to have several excellent teachers during my education, it never was tutoring on individual level. I think the impact of individual mentorship might be generally overestimated, with an exception on the extreme high-end of achievement spectrum. A MOOC could there at least give the mentor access to the highest performers to spin-off deeper, smaller circle education.

Cognitive inequality and the hourglass economy

Borovik talks about an hourglass economy: in a technologically advanced society, there is no market demand for “middle” level mathematical skills. The largest fraction of population requires only rudimentary arithmetic for its everyday life (using a calculator or a spreadsheet at best). On the high-end there is a very small group of high-skill workers required to develop and implement the newest technological advances.

MOOCs do not solve the disappearing middle problem. In fact they might be driving an even larger wedge between high- and low- ability ends of the distribution. This is because they rely more on self-motivation and therefore profit strongly those that have not only high aptitude, but also a high “appetite” for knowledge. This contributes strongly to the growing cognitive inequality, but by tapping into a wider population, it might be sufficient to fill the upper bulb of the hourglass economy.

The cognitive inequality gap will be a very important factor in the near future (it is showing already now). It runs very deep into our cores – indeed in our genetic essence – and we do not have simple mechanisms to alleviate it, like taxation in case of wealth inequality.

MOOCs, assuming they stay free and internet access continues to spread in developing countries, have here at least the upside, that they rely purely on self-selection. The burgeoning cognitive elite doesn’t receive its status from an “entitled” institution or similar but is self-selected by its own virtue of putting time and effort into self-education. The system can be also more meritocratic than most of its alternatives.

Late end-game?

Lot’s of questions and not many solutions. Ultimately, however, it might be also simply too late to worry. If development of an artificial general intelligence is maximum a few decades in the future, human knowledge, may it be as deep as it wishes, will soon be completely left behind. The ultimate limit is clear and independent of the exact timing – as illustrated by Greenspan’s quote in Borovik’s paper:

While there is an upside limit to the average intellectual capabilities of population, there is no upper limit to the complexity of technology.

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