Algorithmic Education (including the Mathematics of Cramming)

Samuel Arbesman:

Many of us don’t learn in optimal ways. We know that we forget new material, neglect to review older material, and study in ways that elevate cramming and procrastination to art forms. But there is research about how to be more efficient in these things. For example, dating back to 1885, there is a rich literature that explores how timing our learning of new and old material can affect education.
For a long time, these theories were only loosely applied. They couldn’t be put into quantitative practice because of the difficulty of carefully implementing them. But with the ability to create educational software, customized to ensure a student has an optimal learning experience, we have a wonderful opportunity to actually employ this knowledge. Unfortunately, there are so many competing concerns, it’s far from trivial: We need to begin constructing new algorithms to figure out how best to learn.