Content-Aware Spaced Repetition
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Content-Aware Spaced Repetition
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Re: Content-Aware Spaced Repetition
#2I explored memory models for spaced repetition in my master's thesis and later built an SRS product. This post shares my thoughts on content-aware memory models.
I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.
Re: Content-Aware Spaced Repetition
#3I explored memory models for spaced repetition in my master's thesis and later built an SRS product. This post shares my thoughts on content-aware memory models. I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.
I've been playing with something similar, but far less thought out than what you have.
I have a script for it, but am basically waiting until I can run a powerful enough LLM locally to chug through it with good results.
Basically like the knowledge tree you mention towards the end, but attempt to create a knowledge DAG by asking a LLM "does card (A) imply knowledge of card (B) or vice versa". Then, take that DAG and use it to schedule the cards in a breadth first ordering. So, when reviewing a new deck with a lot of new cards, I'll be sure to get questions like "what was the primary cause of the civil war", before I get questions like "who was the Confederate general who fought at bull run"
Re: Content-Aware Spaced Repetition
#4> [....] Ignoring the following factors means we are leaving useful information on the table:
> 1. The review histories of related cards. Card semantics allow us to identify related cards. This enables memory models to account for the review histories of all relevant cards when estimating a specific card’s retrievability.
> 2. [...]
I've been thinking that card semantics shouldn't be analyzed at all, and just treated as a black box. You can get so much data off of just a few users of a flashcard deck that you could build your own map of the relationships between cards, just by noticing the ones that get failed or pass together over time. Just package that map with the deck and the scheduler might get a lot smarter.
That map could give you good info on which cards were redundant, too.
edit: this may be interesting to someone, but I've also been trying to flesh out a model where agents buy questions from a market, trade questions with each other, and make bets with each other about whether the user will be able to recall the question when asked. Bankrupt agents are replaced by new agents. Every incentive in the system is parameterized by the user's learning requirements.
Re: Content-Aware Spaced Repetition
#5[deleted]
Re: Content-Aware Spaced Repetition
#6I explored memory models for spaced repetition in my master's thesis and later built an SRS product. This post shares my thoughts on content-aware memory models. I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.
I've been playing with something similar, but far less thought out than what you have. I have a script for it, but am basically waiting until I can run a powerful enough LLM locally to chug through it with good results. Basically like the knowledge tree you mention towards the end, but attempt to create a knowledge DAG by asking a LLM "does card (A) imply knowledge of card (B) or vice versa". Then, take that DAG and…
I'd love to see it.
What I like about your approach is that it circumvents the data problem. You don't need a dataset with review histories and flashcard content in order to train a model.
Re: Content-Aware Spaced Repetition
#7> [....] Ignoring the following factors means we are leaving useful information on the table: > 1. The review histories of related cards. Card semantics allow us to identify related cards. This enables memory models to account for the review histories of all relevant cards when estimating a specific card’s retrievability. > 2. [...] I've been thinking that card semantics shouldn't be analyzed at all, and just treated…
Yes, that reminds me of knowledge tracing and methods like 1PL-IRT.
I think you can do both and get even better results. The main limitation is that the same flashcards must be studied by multiple students, which doesn't generally apply.
I also love the idea of the market, you could even extend it to evaluate/write high-quality flashcards.
Re: Content-Aware Spaced Repetition
#8You mention that FSRS treats each card independently, even if they derive from the same note. I wonder whether you've tried this Anki plugin, which tries to increase the interval between reviews of 'sibling' cards: https://ankiweb.net/shared/info/759844606
Re: Content-Aware Spaced Repetition
#9I've been thinking about this for a while too as an FSRS developer [1].
In general, we can think of a spaced repetition system as being (i) Content-aware vs. Content-agnostic and (ii) Deck-aware vs. Deck-agnostic
Content-aware systems care about what you're studying (language, medecine, etc) while Content-agnostic systems don't care about what you're studying.
Deck-aware systems consider each card in the context of the rest of the cards (the "deck") while Deck-agnostic systems consider each card in pure isolation.
Currently, FSRS is both Content-agnostic as well as Deck-agnostic. This makes it extremely easy to integrate into a spaced repetition system, but this also means the model will underfit a bit.
It it interesting to note that you could in practice optimize seperate FSRS models for each deck covering different topics, which would make it Content-aware in a sense. Additionally, "fuzz" is a somewhat Deck-aware feature of the model in that it exists specifically to reduce interactions between other cards in the deck.
Re: Content-Aware Spaced Repetition
#10You mention that FSRS treats each card independently, even if they derive from the same note. I wonder whether you've tried this Anki plugin, which tries to increase the interval between reviews of 'sibling' cards: https://ankiweb.net/shared/info/759844606
Ah, I totally missed this, thanks for sharing it.
Since in Anki the "note" is the editing unit, that works for some cloze deletions but not for QA cards (only for double-sided QA cards). A content-aware memory model would allow you to apply "disperse siblings" to any set of cards, independently of whether they were created together in the same editing interface.