Evidence: let's promote not stifle innovation in education

Evidence: let's promote not stifle innovation in education

Earlier this week I was part of a high-level discussion about maths and computer science education, how we could improve their reach and effectiveness. Rather quickly the question of  evidence came up, and its role in driving innovation.

It's taken me a few days to realise that there were actually two very different "importance of evidence" conversations--one with which I completely concur, and one with which I vehemently disagree. In the end, what I believe this exposes is a failure of many in charge of education to understand how major innovation usually happens--whether innovation in science, technology, business or education--and how "evidence" can drive effective innovation rather than stifle it. In an age of massive real-world change, the correct and rapid reflection of this in education is crucial to future curricula, their effective deployment, and achieving optimisation for the right educational outcomes.

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Computation meets Data Science in London, Thursday 5 March

Computation meets Data Science in London, Thursday 5 March

I'm usually going on about "computation" or in education, "maths". But I've come to appreciate just how much of computation's utility in modern life centres around data (rather than, say, algebraic modelling).

Clearly data science is a major, growing and vital field—one that's relatively new in its current incarnation. It's been born and is driven forward by new technology, our abilities to collect, store, transmit and "process" ever larger quantities of data.

But "processing" has often failed to elucidate what's important in the data. We need answers, not just analytics, we need decisions not just big data.

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What are the modern areas of maths?

Traditional areas of maths like algebra, calculus or trig don't seem a good way to think about subdividing the subject in the modern world.

You might ask, why subdivide at all?

In a sense, you shouldn't. The expert mathematician utilises whichever maths areas helps them solve the problem at hand. Breadth and ingenuity of application is often key.

But maths represents a massive body of knowledge and expertise, subdividing helps us to think about different areas, for curricula to focus their energies enough that there's sufficient depth of experience gained by students at a given time to get a foothold.

However I believe the subdivisions should be grouped by modern uses of maths, not ancient divisions of tools.

So here goes with our 5 major areas:

  • Data Science (everything data, incorporating but expanding statistics and probability).
  • Geometry (an ancient subject, but highly relevant today)
  • Information Theory (everything signals--whether images or sound. Right name for area?).
  • Modelling (techniques for good application of maths for real world problems)
  • Architecture of Maths (understanding the coherence of maths that builds its power, closely related to coding).

Comments welcome!

PISA results: Let's win on the right playing field, not lose on the wrong one

PISA results: Let's win on the right playing field, not lose on the wrong one

Today's maths PISA results are predictable in the successes that many Asian countries show and the mediocrity of many of the traditional Western countries--like the UK. 

I believe PISA is meticulous in conducting its tests and reflects a good evaluation of standards of today's maths education. And yet I think if countries like the UK simply try to climb up today's PISA assessment, they'd be doing the wrong thing.

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Excited: Raspberry Pi gets free Mathematica + Wolfram language

Excited: Raspberry Pi gets free Mathematica + Wolfram language

I was very excited at our CBM summit this morning with Eben Upton to announce that Mathematica will be bundled on the Raspberry Pi computer for free, and so will the new Wolfram Language--also announced today.

This really has at least 4-dimensions of consequence:

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