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Content-Aware Spaced Repetition

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Re: Content-Aware Spaced Repetition

#11

Post by ran3000 »

joshdavham wrote: post #9

I'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 t…

Being easy to integrate is an underappreciated feature of FSRS.

Using decks to draw semantic boundaries is likely overly constraining. I think we want to account for finer differences between cards. Decks are coarse and people differ in the ways they use them, some people recommend having just one global deck. Notes are too fine. We explored something in between: a note capturing an idea or concept, plus an associated set of cards. Turns out it's hard to draw idea boundaries. That's why I think it's easier to relate cards by semantic embeddings or more rigid but clearer structures, like the DAG of dependencies suggested elsewhere in this thread.

Re: Content-Aware Spaced Repetition

#12

Post by daft_pink »

After reading this, I would really like to know what other spaced repetition software there is for things like ai driven speech?

I love Anki and used it before when I needed to memorize things, but would love to know what other options on the market exist.

Re: Content-Aware Spaced Repetition

#13

Post by mabster »

On the scheduling end, I'm surprised the article didn't mention https://github.com/fasiha/ebisu which uses Bayesian statistics.

When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random.

Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.

Re: Content-Aware Spaced Repetition

#14

Post by kebsup »

I'm building a SRS language learning app [1] so I've thought about this topic a bit, but I've come to a conclusion that srs algorithms might be just a nerd optimization obsession. My app has "stupid" 1,3,7,15,30 or something like that intervals, and the reality is that if I know a card, I can swipe it within 2 seconds, and if I just barely know it, I can spend 30 seconds on it.

So optimizing the algorithm such that every card comes at the exact right moment might cause all cards to feel too hard or too easy. I think having a mix of difficult and easy cards is actually a feature, not a bug.

[1] https://vocabuo.com

Re: Content-Aware Spaced Repetition

#15

Post by jeffalyanak »

In the language learning world there are some great tools already for adding content-awareness.

AnkiMorphs[1] will analyze the morphemes in your sentences and, taking into account the interval of each card as a sign of how well you know each one, will re-order your new cards to, ideally, present you with cards that have only one unknown word.

It doesn't do anything to affect the FSRS directly—it only changes the order of new, unlearned cards—but in my experience it's so effective at shrinking the time from new card to stable/mature that I'm not sure how much more it would help to have the FSRS intervals being adjusted in this particular domain.

1: https://mortii.github.io/anki-morphs/intro.html

Re: Content-Aware Spaced Repetition

#16

Post by cosmic_cheese »

mabster wrote: post #13

On the scheduling end, I'm surprised the article didn't mention https://github.com/fasiha/ebisu which uses Bayesian statistics. When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random. Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more…

> When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random.

>Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.

Just the other day I was thinking about how there’s a good chunk of vocab that could be “mined” from the sentences in my vocab deck.

I think that this idea would work well, but would probably require a whole new SRS program to be able to implement it cleanly. It’s too dynamic for a traditional SRS app like Anki which is pretty static in nature.

Re: Content-Aware Spaced Repetition

#17

Post by emsixteen »

kebsup wrote: post #14

I'm building a SRS language learning app [1] so I've thought about this topic a bit, but I've come to a conclusion that srs algorithms might be just a nerd optimization obsession. My app has "stupid" 1,3,7,15,30 or something like that intervals, and the reality is that if I know a card, I can swipe it within 2 seconds, and if I just barely know it, I can spend 30 seconds on it. So optimizing the algorithm such that e…

This site makes my (more than good) computer's browser crawl to a halt.

Re: Content-Aware Spaced Repetition

#18

Post by [deleted] »

ran3000 wrote: post #2

I 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.

[deleted]

Re: Content-Aware Spaced Repetition

#19

Post by pessimizer »

ran3000 wrote: post #7
pessimizer wrote: post #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…

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.

> The main limitation is that the same flashcards must be studied by multiple students, which doesn't generally apply.

I think only a kernel of the same flashcards, because in my mind new cards would quickly find their position after being reviewed a few times, and might displace already well-known cards. I see the process as throwing random cards at students, seeing what's left after shaking the tree, and using that info to teach new students.

The goal, however, would definitely be a single standard but evolving set of cards that described some group of related ideas. I know that's against Supermemo/Anki gospel, but I've gotten an enormous amount of value out of engineered decks such as https://www.asiteaboutnothing.net/w_ultimate_spanish_conjuga....

> I also love the idea of the market, you could even extend it to evaluate/write high-quality flashcards.

It's been my idea to drive conversational spaced repetition with something like this.

Re: Content-Aware Spaced Repetition

#20

Post by dllthomas »

joshdavham wrote: post #9

I'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 t…

I ran into a question a while ago that I couldn't find a good answer to, and while it's not exactly on topic this seems like a good place to ask it.

I was working in a detail rich context, where there were a lot of items, about which there were a lot of facts that mostly didn't change but only mostly. Getting a snapshot of these details into approximately everyone's head seemed like a job for spaced repetition, and I considered making a shared Anki deck for the company.

What wasn't clear was how to handle those updates. Just changing the deck in place feels wrong, for those who have been using it - they're remembering right, the cards have changed.

Deprecating cards that are no longer accurate but which don't have replacement information was a related question. It might be worth informing people who have been studying that card that it's wrong now, but there's no reason to surface the deprecation to a person who has never seen the card.

Is there an obvious way to use standard SRS features for this? A less obvious way? A system that provides less standard features? Is this an opportunity for a useful feature for a new or existing system? Or is this actually not an issue for some reason I've missed?

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