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 memorized
• deck: a set of cards
• sub-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

• SuperMemo2
• http://www.blueraja.com/blog/477/a-better-spaced-repetition-learning-algorithm-sm2