> [....] 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.
SuperMemo's neural network component (implemented in SM-15) already does something similar by tracking correlations between items without semantic analysis, effectively building that "map" of relationships based purely on performance data.
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.
I would be valuable for shared decks, like the one you mentioned.
As far as I can tell, the majority of Anki users are medical school students or language learners. Both groups benefit from shared decks. So I think it's a good idea to pursue.
My personal interest is more on conceptual knowledge, like math, cs, history or random blog posts and ideas. It's often the case that, on the same article, different people focus different things, so it would be hard to collect even a small number of reviews on a flashcard you want to study.
> 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.