An open-source, well documented, spaced repetition algorithm.
- Introduces sub-decks for efficiently learning large decks (Super useful!)
- Intuitive variable names and algorithm parameters.
- Fully open-source with human-readable examples.
- Easily configurable parameters to accommodate for different users' memorization abilities.
- Computationally cheap to compute next card. No need to run a computation on every card in the deck.
card: an item to be memorizeddeck: a set of cardssub-deck: a subset of a deck. This is generated dynamically by the algorithm.
The use case of sub-decks is as follows. A user adds a large number of cards to
their deck (Ex: 100+) and now attempts to study. In order to facilitate efficient learning it
is imperative to repeat the new cards with a frequency greater than 1/100. To solve this problem SaneMemo
groups new cards into sub-decks of a configurable size default sub_deck_size=20. Cards in a sub-deck are repeated more frequently, once the user indicates that they know the card, it is removed from the sub-deck and replaced with a new card.
Users is given three options to describe the difficulty of a card.
- "I KNOW IT"
- "I KNOW IT BUT WILL FORGET"
- "DON'T KNOW"
These are roughly equivalent to easy, medium and hard. But are more intuitive, and more accurately model the human learning process.
See code.py