Why Learning Efficiency is Such a Big Lever in Maximizing Student Potential

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Almost everybody who pursues serious math eventually reaches a level at which there’s just not enough scaffolding to justify continuing.

It’s not a hard threshold at which you’re suddenly incapable of learning more advanced math, but rather a soft threshold at which the amount of time and effort required to learn begins to skyrocket until it’s effectively no longer a productive use of your time (when you consider the opportunity cost).

People get off the train and stop learning math once it begins to feel too inefficient. This isn’t even a math-specific thing – the same thing plays out everywhere else in life. In anything you do, once the progress-to-work ratio gets too low, you’re going to lose interest and focus on other endeavors where your progress-to-work ratio is higher.

That’s why maximizing learning efficiency is such a big lever in maximizing student potential. Everybody who gets thrown off the learning train due to pedagogical friction could have gone further if that friction were removed. Under high-efficiency learning conditions, students not only make faster progress, but also reach higher levels of math than they would otherwise.

Learning efficiency keeps the progress-to-work ratio as high as possible, keeping students on the rails as far as possible. If that’s not the definition of maximizing student potential, I don’t know what is.

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