Announcement: Our First CBM Country
I’m very excited to announce that computerbasedmath.org has found the first country ready for our completely new kind of math education: it’s Estonia. (…and here’s the press release).
I thought Estonia could be first. They are very active on using technology (first to publish cabinet decisions immediately online, first to include programming in their mainstream curriculum), have ambition to improve their (already well respected) STEM aptitude and lack the dogma and resistance to change of many larger countries. There aren’t so many countries with all those characteristics.
In our first Estonia project we will work with them to rewrite key years of school probability and statistics from scratch. This is an area that’s just crazy to do without a computer, even harmful. It’s an area that’s only come to the fore since computers because it only makes sense with lots of data. No-one in real life does these hand analyses or works with only 5 data points, so why do we make our students? Why get students emulating what computers do so much better (computing) rather than concentrate on imaginative thinking, analysis and problem-solving that students ought to be able to do so much better even than today’s computers?
Worse, in a subject like probability and statistics, current math education often forces you to learn the wrong tools for the job.
Take the Normal Distribution—one of very few options taught to students for data analysis. Approximating your data with it rarely gets you the most accurate solution; it can be wildly wrong. Instead why not learn to select the best of 100+ other distributions and test their predictions against each other? Or why use a distribution at all, when you can work out results directly from each and every data point?
The reason is historical. Normal Distributions (and Poissons) are easiest to calculate, appear appropriate in over-simplified problems and you can’t practically compare lots of distributions or work directly with data by hand. But with a computer you can and you should.
Out in the real world, there are real consequences to drilling students in de- or mis- contextualised techniques—and with the idea that each school problem has one right technique, and each technique has particular patterns of problem. Take the miscalculating of large swathes of financial risk analysis: people applied Normal distributions because they knew of them, had been trained to expect them but that didn’t make for effective representations for the data.
This reminds me of an old adage. “If all you have is a hammer, everything looks like a nail.” In math context—the fact that every school problem can be solved with a small subset of math tools leads to a false expectation that in the real-world this same subset will suffice. CBM broadens the toolkit dramatically by not insisting students should make all the tools that they use, freeing time for using more in a wider variety of situations.
Estonia is the first place where we’re starting to change all of this though many other countries have voiced interest in pursuing CBM and being within the first group.
But it’s slow work on several fronts for a little while. Even though several of us have been thinking along CBM lines for ages, we’re constantly questioning whether a particular way of thinking or doing is in fact now the best way or simply a legacy of the pre-computer era. Indeed what outcomes are we trying to achieve and how does learning tools of math fit with learning how to solve problems?
And education changes slowly, though now is the most vibrant and exciting time of change in my lifetime. Even with this, I expect it to be a couple of decades until the world’s mainstream math subject is universally computer-based math rather than today’s “history of hand-calculating”. But today is an important step.
Countries that start the change early will reap many benefits from being first—a bit like the changes that universal education brought to countries who were first, but in microcosm for math.
In fact it’s more of a macrocosm. It affects lucrative problem-solving STEM jobs where pushing the boundaries of modeling is crucial to success. But it can make happier citizens too—able to assess risk, understand complex finances better, have an in-built mathematical 6th sense by which to understand life.