Overlearning

Overlearning is characterized by low return on investment (ROI). An example of when applying this definition is solving the same type of math problems over and over again within a short relative time period.

What do we mean by “relative”? If you are new at math or a certain branch of math, “relative” in an overlearning- and time-sense could mean solving the same type of math problems within an hour (they can differ, but still belong to the same “type” or “branch” in math). As one becomes better at the same branch of math, however, “relative” in terms of time and overlearning becomes longer i.e. that hour now “becomes” a week. It is similar, and caused by, the spaced repetition effect.

So if you learned how to solve the problem 1+1 a day ago, it is less likely that solving the same “type” of problem such as 3+2 two days later will constitute as overlearning, than if you solve similar problems one day later. Leaving too much time between each repetition, however, and you will forget more than you are learning, and may end up not being able to solve the same math problems anymore. So the art is finding the right timings: not too less, not too much.

Second, overlearning is characterized by a very short forgetting curve, because of massed repetition.

If you forget 50% of the recently acquired knowledge within 1 week, then one can imagine how you will forget a lot more “items” if you learn 200 (foreign) words in an hour as opposed to only 100 words.

Third, there is this so-called Einstellung effect, which says that we can get “stuck” at using the same methods of solution, that may or may not work, but are less efficient. See the Luchins water jar experiment for an example of the Einstellung effect.

So how do we avoid the Einstellung effect? By backing off, letting our subconscious take over, and allowing more room for (new) neural circuits to form that are more efficient.