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

Everyone eventually reaches a level at which the progress-to-work ratio drops too low to keep going. Maximizing learning efficiency raises that ceiling, keeping students on track further than they would otherwise go.

Want to get notified about new posts? Join the mailing list and follow on X/Twitter.

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.

Want to get notified about new posts? Join the mailing list and follow on X/Twitter.