Re:What’s the Algorithm for Spaced Repetition?
by kazinator on Tuesday September 22, 2020 @01:52AM
(#24551313)
The algorithm is exponential backoff. Every item has an interval associated with it. The interval determines when you will see the item next. If you recognize the item, the interval increases geometrically by some configurable factor, like say 1.25. If you lapse, then the interval shrinks by some other configurable factor, like say 0.6.
The initial short intervals for learning new items can follow some canned pattern measured in minutes: 5, 15, 45, 60. When an item goes through the by-the-minute intervals, then it jumps to a some initial interval that is measured in days (say 1 day, so you see all the new cards again tomorrow), and after that the exponential backoff kicks in.
To make the algorithm usable, you need a strategy for lapsed items. Suppose that you lapse on an item that has a 6 month interval. It cannot be that you just see the item once, lapse on it, and then the interval is adjusted to 4 months and you do not see the item again for that 4 months. A lapsed item requires relearning. It should go through a re-learning cycle with a sequence of temporarily instituted short intervals, like 10 minutes, 20 minutes, 40 minutes. This is similar to the initial learning, but it can be less intensive: fewer reviews. Once it goes through that relearning sequence, then its regular interval (now adjusted to 4 months) kicks in, and you don't see it until then.
The learning and relearning sequences are like a ladder. When the user lapses during learning, they fall off the ladder and go back to the shortest interval. E.g. you're shown a new item. Then you see it again in 5 minutes, and again in 15, then again in 40. Oops, you forgot it; so you slide back to 5 minutes, then 15, then 40, ... you must climb it through the learning or re-learning ladder to propagate or return the item to the long-term schedule.
When re-learning a lapsed item, additional lapses during re-learning can further trim its long interval. E.g. lapse on a 6 month item, it might go to 4. Lapse on it again during re-learning, it might drop to 2.4 months.
The initial short intervals for learning new items can follow some canned pattern measured in minutes: 5, 15, 45, 60. When an item goes through the by-the-minute intervals, then it jumps to a some initial interval that is measured in days (say 1 day, so you see all the new cards again tomorrow), and after that the exponential backoff kicks in.
To make the algorithm usable, you need a strategy for lapsed items. Suppose that you lapse on an item that has a 6 month interval. It cannot be that you just see the item once, lapse on it, and then the interval is adjusted to 4 months and you do not see the item again for that 4 months. A lapsed item requires relearning. It should go through a re-learning cycle with a sequence of temporarily instituted short intervals, like 10 minutes, 20 minutes, 40 minutes. This is similar to the initial learning, but it can be less intensive: fewer reviews. Once it goes through that relearning sequence, then its regular interval (now adjusted to 4 months) kicks in, and you don't see it until then.
The learning and relearning sequences are like a ladder. When the user lapses during learning, they fall off the ladder and go back to the shortest interval. E.g. you're shown a new item. Then you see it again in 5 minutes, and again in 15, then again in 40. Oops, you forgot it; so you slide back to 5 minutes, then 15, then 40, ... you must climb it through the learning or re-learning ladder to propagate or return the item to the long-term schedule.
When re-learning a lapsed item, additional lapses during re-learning can further trim its long interval. E.g. lapse on a 6 month item, it might go to 4. Lapse on it again during re-learning, it might drop to 2.4 months.