Computational spaced repetition
Computational spaced repetition is a computer-based learning method of computing optimum intervals that should separate repetitions of pieces of knowledge to ensure high recall.
Today we can skip the adjective computational from spaced repetition.
As the term spaced repetition was loosely used before the birth of SuperMemo (1985), and adopted in the present meaning only in 1999, we may occasionally use the term computational spaced repetition to differentiate the SuperMemo method from a review schedule in which intervals increase but are not explicitly computed or optimized.
See: Origins of the term "spaced repetition"
This glossary entry is used to explain "History of spaced repetition" by Piotr Wozniak (June 2018)
Figure: Changes in memory status over time for an exemplary piece of knowledge. The horizontal axis represents time spanning the entire repetition history. The top panel shows retrievability (tenth power, R10, for easier analysis). Retrievability grid in gray is labelled by R=99%, R=98%, etc. The middle panel displays optimum intervals in navy. Repetition dates are marked by blue vertical lines and labelled in aqua. The end of the optimum interval where R crosses 90% line is marked by red vertical lines (only if intervals are longer than optimum intervals). The bottom panel visualizes stability (presented as
ln(S)/ln(days)for easier analysis). The graph shows that retrievability drops fast (exponentially) after early repetitions when stability is low, however, it only drops from 100% to 94% in long 10 years after the 7th review. All values are derived from an actual repetition history and the three component model of memory.