How We Learn: Why Brains Learn Better Than Any Machine . . . for Now

By Stanislas Dehaene

Recommended on: 13th March 2020

How We Learn: Why Brains Learn Better Than Any Machine . . . for Now, by Stanislas Dehaene. This is the best book around, hands down, on how the brain learns. Part of the brilliance of Dehaene’s book is that he breaks everything down into easy-to-understand insights that allow you to grasp the big picture without getting bogged down in the minutia of complex neural interactions.  

Dehaene also describes why discovery learning is so problematic in comparison with explicit teaching: “[Discovery learning] is attractive. Unfortunately, multiple studies, spread over several decades, demonstrate that its pedagogical value is close to zero—and this finding has been replicated so often that one researcher entitled his review paper ‘Should There Be a Three-Strikes Rule against Pure Discovery Learning?’ When children are left to themselves, they have great difficulty discovering the abstract rules that govern a domain, and they learn much less, if anything at all. Should we be surprised by this? How could we imagine that children would rediscover, in a few hours and without any external guidance, what humanity took centuries to discern? At any rate, the failures are resounding in all areas: 

  • In reading: Mere exposure to written words usually leads to nothing unless children are explicitly told about the presence of letters and their correspondence with speech sounds. Few children manage to correlate written and spoken language by themselves…. The task would be out of reach if teachers did not carefully guide children through an ordered set of well-chosen examples, simple words, and isolated letters. 
  • In mathematics: It is said that at the age of seven, the brilliant mathematician Carl Gauss (1777–1855) discovered, all by himself, how to quickly add the numbers from one to one hundred (think about it—I give the solution in the notes…). What worked for Gauss, however, may not apply to other children. Research is clear on this point: learning works best when math teachers first go through an example, in some detail, before letting their students tackle similar problems on their own. Even if children are bright enough to discover the solution by themselves, they later end up performing worse than other children who were first shown how to solve a problem before being left to their own means. 
  • In computer science: In his book Mindstorms (1980), computer scientist Seymour Papert explains why he invented the Logo computer language (famous for its computerized turtle that draws patterns on the screen). Papert’s idea was to let children explore computers on their own, without instruction, by getting hands-on experience. Yet the experiment was a failure: after a few months, the children could write only small, simple programs. The abstract concepts of computer science eluded them, and on a problem-solving test, they did no better than untrained children: the little computer literacy they had learned had not spread to other areas. Research shows that explicit teaching, with alternating

If you’re into the neuroscience of learning, you will unquestionably want to read this book. (The last half, in particular, is extraordinarily enlightening.)

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