Adaptive spacing algorithms using user's study history make decisions about which question to ask. There are several adaptive spacing applications like Anki, Supermemo, Mnemosyne.

In order to design spaced repetition adaptive schedulers, several researches have focused on modeling human memory statistically and the item which is recommended, should have a value closest to some predefined fixed value.

There are other approaches as well in which the researches don't rely on memory models. In this cases deep learning algorithms were used and they were tested on simulated students.

In this paper the authors follow the second approach of the above-mentioned ones.

Even thought traditional adaptive spacing algorithms make use of spacing retrieval strategy, they still lack the ability to adapt to learning and memorization of skills. The authors want to work on this part. They want to extend adaptive spacing algorithms to memorization of skills.