Brain Based Learning and Neuroscience – What the Research Says!

Will Thalheimer provides a refreshing look at the over-hyping of (and quite pernicious lies about) neuroscience and brain-based learning. As he observes, neuroscience is barely out of diapers yet in terms of actual usable results for educators, and those actually researching in the field have no illusions that it is anywhere close yet (though they are very hopeful). What the research says is pretty close to nothing, when it comes to learning practice.

I am a little sceptical about whether neuroscience will ever be really valuable in education. This is not to say it is valueless – far from it. We have already had some useful insights into memory and have a better idea of some of the things that reduce or increase the effectiveness of brain functioning (sleep, exercise, etc), as well as a clearer notion of the mechanisms behind learning. Such things are good to know and can lead to some improvements in learning. The trouble is, though, that most researchers in the area are doing reductive science – seeking repeatable mechanisms and processes that underlie phenomena we see. This is of very little value when dealing with complex adaptive systems and emergence. Stuart Kauffman demonstrates that there are two main reasons reductive explanations fail to give us any help at all with understanding emergent systems: epistemological emergence and ontological emergence. Epistemological emergence means that it is impossible in principle to predict emergent features from constituent parts. Ontological emergence means that completely different kinds of causality occur in and between emergent phenomena than in and between their constituent parts, so knowledge of how the constituent parts work has no bearing at all on higher levels of causality in emergent phenomena. It’s a totally different kind of knowledge.

Knowing how the brain works in education is useful in much the same way that knowing about movements of water molecules in clouds is useful in meteorology. There are insights to be gained, explanations even, but they are of relatively little practical value in predicting the weather, let alone in predicting the precise shape of a specific cloud. Worse, in education, we don’t have a very precise idea of what kind of cloud shape we are seeking, most of the time. In fact, when we act like we do (learning objectives and associated assessment) we usually miss a great deal of the important stuff.

But it is worse than that. Those of us concerned with education are not just predicting or explaining phenomena, but orchestrating them. You can no more extrapolate how to teach from knowing how the brain works than you can extrapolate how to paint a masterpiece from knowing what paint is composed of. They are not even in the same family of phenomena. This doesn’t mean that a painter cannot learn useful things about paint that can assist the process – how fast it dries, its colour fastness, its viscosity, etc, and it does open up potential avenues for designing new kinds of paint. But we still need to know what to do with it once we know that. So, yes, brain science has value in education. Just not that much.

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I am a professional learner, employed as a Full Professor and Associate Dean, Learning & Assessment, at Athabasca University, where I research lots of things broadly in the area of learning and technology, and I teach mainly in the School of Computing & Information Systems. I am a proud Canadian, though I was born in the UK. I am married, with two grown-up children, and three growing-up grandchildren. We all live in beautiful Vancouver.

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