free-spaced-repetition-scheduler
autoEaseFactor
| free-spaced-repetition-scheduler | autoEaseFactor | |
|---|---|---|
| 11 | 4 | |
| 260 | 35 | |
| 5.8% | - | |
| 5.3 | 0.0 | |
| 23 days ago | about 2 years ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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free-spaced-repetition-scheduler
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Anki – Powerful, intelligent flash cards
... https://github.com/open-spaced-repetition/fsrs4anki/wiki/The... ...
I'm not sure I believe we understand our own learning/memory anything like enough for this not to be total pseudoscience? Reminds me of A Beautiful Mind.
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FSRS: A modern, efficient spaced repetition algorithm
Libraries: https://github.com/open-spaced-repetition/free-spaced-repeti...
All are MIT licensed I believe, Anki is primarily AGPL
- The FSRS (Free Spaced Repetition Scheduler) Algorithm
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FSRS explained, part 1: What it is and how it works
Just read the wiki ¯\_(ツ)_/¯
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FSRS is supported in AnkiMobile now!
The model used by FSRS: https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler
- How to use the next-generation spaced repetition algorithm FSRS on Anki?
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How did I publish a paper in ACMKDD as an undergraduate? A fantastic research experience on spaced repetition algorithm. Open source the code and dataset.
Also, sorry for nitpicking, but I just checked the code here, and I saw that you changed the formula for post-lapse stability, but you didn't update the formula in the description here.
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New progress in implementing the custom algorithm.
I have another one, also about difficulty. Look at the long "Stability updating formula after successful review" formula, there is a term D-b. This seems very counter-intuitive. A large value of D corresponds to an easy card, but thanks to that formula it will produce a very small change in stability. A small value of D corresponds to a difficult card, but according to that formula it will change stability a lot.
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Implement a new spaced repetition algorithm based on anki custom scheduling.
So I checked this and it seems that there's a whole bunch of different parameters (initial difficulty, initial stability, etc), and a lot of them are constant. So the next step would be to use some kind of optimization algorithm, like gradient descent, to optimize those parameters based on user's review history, right?
autoEaseFactor
What are some alternatives?
fsrs4anki - A modern Anki custom scheduling based on Free Spaced Repetition Scheduler algorithm
anki_straight_reward - Escape Ease Hell!
highlight-search-results - Highlight Search Results in the Browser add-on for Anki
anki-stats - Python package to make it easier to analyse an anki database.
fsrs4anki-helper - An Anki add-on that reschedules all cards via FSRS4Anki scheduler
mnemocards - In addition to helping you memorise, this code helps you do other things that I don't remember...
SSP-MMC - A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling
autocards - Accelerating learning through machine-generated flashcards.
tatoeba-to-anki - Creates Anki Flash cards from Tatoeba sentences, ordering them by difficulty and downloading audio
incremental-reading - Anki add-on providing incremental reading features
anki-delay-siblings
passfail2 - Pass/Fail addon for Anki