Content-Aware Spaced Repetition

For what you describe, he ideal system would do this:

1. Identify knowledge blocks that you want people to learn. This is what would be tracked with the SRS.

2. Create cards, with a prompt which requires knowledge blocks to answer. Have the answers in this system feed back knowledge to the SRS.

3. When one of the knowledge blocks changes, take the previous knowledge familiarity and count that against the user.

So for example, at some point a card might be "Q. What effect will eating eggs have on blood cholesterol? A. Raise it." That would be broken down into two knowledge blocks: "Cholesterol content of eggs" and "Effect of dietary cholesterol on blood cholesterol".

At some point you might change that card to "Q. What effect will eating eggs have on blood cholesterol? A. None, dietary cholesterol typically doesn't affect blood cholesterol." (Or maybe we're back again on that one.)

The knowledge blocks would be the same, but you'd have to take the existing time studied on the "Effect of dietary cholesterol on blood cholesterol" and mark it against recall rather than towards recall. Someone who'd never studied it would be expected to learn it at a certain pace; but someone who'd studied the old value would be expected to have a harder time -- to have to unlearn the old value.

I think you could probably hack the inputs to the existing FSRS algorithm to simulate that effect -- either by raising the difficulty, or by adding negative views or inputs. But ideally you'd take a trace of people whose knowledge blocks had changed, and account for unlearning specifically.

Phrasing looks amazing!

There's a lot of UX work to do for SRS. Do you have a sense of how well the ideas behind Humane SRS translate outside of language learning? I imagine the main challenge would be identifying a steady influx of new cards.

I agree that gains in scheduling accuracy are fairly imperceptible for most students. That's why, over the past few years building https://rember.com, we've focused on UX rather than memory models. People who review hundreds of card a day definitely feel the difference, doing 50 fewer reviews per day is liberating. And now that LLMs can generate decent-quality flashcards, people will build larger and larger collections, so scheduler improvements might suddenly become much more important.

Ultimately, though, the biggest advantages is freeing the SRS designer. I'm sure you've grappled with questions like "is the right unit the card, the note, the deck or something else entirely?" or "what happens to the review history if the student edits a card?". You have to consider how review UX, creation/editing flows, and card organization interact. Decoupling the scheduler from these concerns would help a ton.

I would say probably 50% of the learnings from Humane SRS would be applicable in other fields/schedulers. There is another half that is language-specific though - at the end of the day, if you try to learn a language the same way you cram for a med school exam, you're probably not going to succeed. The inverse is also true, please nobody use Phrasing to cram for their med school exam XD

I agree most peoples collections get unwieldy and something needs to be done, so props to Rember! I take the opposite approach - instead of helping people manage large collections, I try to help people get the most out of small collections. This sort of thing is not possible in most fields outside of languages (I don't think — I cannot say I've given it any real thought though).

For example, the standard tier in Phrasing is 40 new Expressions per month. This should result in 2,000-3,500 words in a year, which would be a pretty breakneck pace for most learners, and is considered sufficient for fluency. Of course, users can learn Expressions other users have created for free, or subscribe to higher tiers, or buy credits outright, but it's often not needed.

Indeed Phrasing does not really use the idea of "cards," we reconstruct pseudo-cards based on the morphemes, lemmas, and inflections found within the Expression. So "cards" are indeed not the boundary I use.

It would be awesome to work on that data. I'm afraid of the privacy implications though.

Thanks for the write-up!

I've got a system for learning languages that does some of the things you mention. The goal is to be able to recommend content for a user to read which combines 1) appropriate level of difficulty 2) usefulness for learning. The idea is to have the SRS system build into the system, so you just sit and read what it gives you, and review of old words and learning new words (according to frequency) happens automatically.

Separating the recall model from the teaching model as you say opens up loads of possibilities.

Brief introduction:

1. Identify "language building blocks" for a language; this includes not just pure vocabulary, but the grammar concepts, inflected forms of words, and can even include graphemes and what-not.

2. For each building block, assign a value -- normally this is the frequency of the building block within the corpus.

3. Get a corpus of selections to study. Tag them with the language building blocks. This is similar to Math Academy's approach, but while they have hundreds of math concepts, I have tens of thousands of building blocks.

3. Use a model to estimate the current difficulty of each word. (I'm using "difficulty" here as the inverse of "retrievability", for reasons that will be clear later.)

4. Estimate the delta of difficulty of each building block after being viewed. Multiply this delta by the word value to get the study value of that word.

5. For each selection, calculate the total difficulty, average difficulty, and total study value. (This is why I use "difficulty" rather than "retrievability", so that I can calculate total cognitive load of a selection.)

Now the teaching algorithm has a lot of things it can do. It can calculate a selection score which balances study value, difficulty, as well as repetitiveness. It can take the word with the highest study value, and then look for words with that word in it. It can take a specific selection that you want to read or listen to, find the most important word in that selection, and then look for things to study which reinforce that word.

You mentioned computational complexity -- calculating all this from scratch certainly takes a lot, but the key thing is that each time you study something, only a handful of things change. This makes it possible to update things very efficiently using an incremental computation [1].

But that does make the code quite complicated.

[1] https://en.wikipedia.org/wiki/Incremental_computing