How we Learn Best is so Unintuitive
Imagine a teacher who asks a maths question to the class, then gently nudges the students towards the correct answer (affirmation when they are on the right track; correcting them when they seem to be going off the path). A variant of the above approach is what David Epstein describes in Range:
“’Lemme
show you, there’s a better, easier way.” If the teacher didn’t already turn the
work into using procedures practice, well-meaning parents will.”
Next imagine a
second teacher who lets the students try and solve it on their own. No feedback
in real time. Afterwards, she corrects the paper and includes notes on the
right approach (if needed).
Which way do you
think results in better learning? Not just for the duration of the class or
course, but in the long run? It’s the let-them-struggle, let-them-fail approach
that yields better learning, not just in that course/class, but in long term
retention as well!
There’s even a
term for it, the “hypercorrection effect”:
“The
more confident the learner is of their wrong answer, the better the information
sticks when they subsequently learn the right answer. Tolerating big mistakes
can create the best learning opportunities.”
This extends
beyond maths. It has been found to be true for subjects where one needs to
think and analyze e.g. finding patterns, solving puzzles etc.
Another
counter-intuitive point is that one should have gaps. Learn something (the hard
way, not with hints), then take a gap, then try it again. This is the method
that produces the best learning, both in that semester/year, and beyond.
So why isn’t this
approach the popular way of teaching then? Well, it’s so counter-intuitive that
“it fools the learners themselves”! If the learner can’t see the benefit (only
the toil, failure, and frustration and oh yeah, the lack of help from the
teacher), how do you think he rates the course or instructor? With that
feedback, which kind of instructor would schools and colleges hire?
Unfortunately, it turns out:
“The feeling of learning, it turns out, is based on before-your-eyes progress, while deep learning is not.”
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