A couple of problems I found with Anki for languages:
1. The words aren't in context, which leads to "flashcard blindness", where you see a word in context, know that you recognize it, but can't remember what it means.
2. The theory behind spaced repetition is that you learn most efficiently when you try to remember something just when you're about to forget it. But that means that if the algorithm is working properly, every single card is hard. This makes motivation a problem, because you know studying is going to be a grind, not fun.
3. While "being able to remember it after thinking a second or two" might be fine when studying for an exam, or in many other contexts where memorization might be important, that's too slow for languages. What you want for a language is "know it immediately without having to think about it".
4. The "scheduled review" system is too inflexible. Some days you get only a handful of cards to review, some days you get dozens. It's hard to tell when you're starting out how many cards you should be adding each day such that the number of cards match the amount of time / effort you have to study. Furthermore, if you skip a single day, you have twice as much the next day; and if life happens and you end up missing a week or a month, you come back with a giant jumble of cards, half of which you've forgotten, and it's really difficult to dig your way out of it.
By a strange coincidence, in 2019 I also started on an alternate to Anki to help myself study Mandarin. It has some similarities to his system, in that there's multi-level knowledge; but it's different in that instead of having a fixed schedule, it has the concept of "difficulty" and "study value" for each word / grammar concept, and the algorithm tries to give you a full "readunit" of "native input" which will balance the two. "Spaced repetition" emerges naturally from the model, and if you go away for a week (or 6 months), it knows you've forgotten some things, so it gradually refreshes your memory. And because you're reading actual native text, there's something which pulls you in.
It's in closed beta now; the first public language (MVP) will be in Biblical Greek, but the second one (if it happens, maybe in a year or two) will be in Mandarin; and hopefully there will be other ones after that. There's a sign-up form you can use to be notified for updates.
> Furthermore, if you skip a single day, you have twice as much the next day; and if life happens and you end up missing a week or a month, you come back with a giant jumble of cards, half of which you've forgotten, and it's really difficult to dig your way out of it.
The Anki system is actually really good at dealing with missed days. You end up recalling most of the cards that you skipped review for, and the system gives you extra credit for the increased interval before review, in that subsequent repeats for the same card will be spaced out even further. Cards that you outright fail to recall due to the missed reviews are a problem, but the best way to 'dig your way out' of that hole is to keep reviewing on a regular schedule and not to overwork or cram. The backlog gets cleared rather quickly.
Sure, a single missed day isn't bad; but have you ever skipped a month?
I'd been using Anki for Mandarin flash cards for a couple of years, and decided I wanted to memorize the "outs" (probabilities) of various hands for Texas Hold 'Em Poker. So I made a separate Anki deck and used it for a few months. Then I got busy, and stopped studying the poker deck (while maintaining the Mandarin deck). When I tried to pick it up again, it was just impossible -- I'd forgotten so much, and the card list was so long, that if I said "I forgot this", it would be scheduled for the next day, but because it was behind 100 other cards, I wouldn't actually be shown it for a week or two. The system was just completely broken.
Contrast that to the system of my own that I developed, based on the "study value" (effect of studying now on the difficulty) rather than fixed timeouts. After working on and using my own system for about 4 years, I got a bit tired of it (and I was also in the middle of redesigning the database from the ground up), and so decided to give Duolingo a go for a bit, just to see what it was like. Six months later, I came back to my own system, and it was great -- just slowly eased me back into the vocab I had before. The same is true after missing a day, or a week: it's always welcoming to get back into, rather than terrifying to get behind.
It's not broken, it's just doing its best to cope with your situation. Missing lots of days means that there will be lots of cards where it's just not clear if you're going to recall them or not. Figuring that out becomes the priority, then the system is effectively back to normal - possibly with some missed cards that will have to be learned again. I'm not sure how one could do better than that.
The comment you replied to described an edge case and explained why it's broken in that particular case. You haven't actually responded to the example provided.
> it's just not clear if you're going to recall them or not. Figuring that out becomes the priority
Presumably the priority ought to be (re)starting with a small subset of cards and gradually trickling the others back in. The algorithm needs to account for time spent by the given individual and adapt to changes in that over time.
I haven't used Anki for about a decade so I'm not familiar with the current state of things. At the time a major factor in my dropping it was that I found the algorithm to be more of a hindrance than a help.
It's not at all clear that "restarting with a small subset of cards" is better. It means that you're very likely to fail to recall almost every card outside the small subset, which increases the work you must do to memorize the deck again. Any card that you verify immediately is going to save you a lot of work down the line.
> It's not at all clear that "restarting with a small subset of cards" is better.
Sure it is. It could grow as quickly as allowed for given the time invested by the user. That could mean a return to the full subset within the span of a single day, or it could mean many months. Perhaps even never. It all depends on time invested by the user going forward. Starving regular review for the sake of verification is an example of an algorithm failing when faced with the real world.
At minimum it is clear from what was said that (better) prioritization between conflicting goals is needed. That somewhat matches my own experience with it from years ago. The algorithm was simply not flexible enough to fit my own usage patterns. In other words I was not part of the target audience, which I found frustrating because I very easily could have been.
Keep in mind that any unverified cards have been "starved of regular review" for longer than any of the cards that have been shown at least once already. It makes sense to prioritize them, at least once they've become "due" for review. The fact that some users might find this unfamiliar or even confusing (because it only happens after you've taken a break and then resumed using Anki, so quite rarely) doesn't make it broken.
> Missing lots of days means that there will be lots of cards where it's just not clear if you're going to recall them or not. ...I'm not sure how one could do better than that.
It's not clear to Anki because its model ("due" or "not due") and algorithm ("study the card that's been due the longest") are too simplistic. I know it can be done better because I built a system that does better with the same data by having a better model ("expected difficulty") with a better algorithm ("study the card which will have the highest long-term impact on difficulty").
I've seen studies that show that the optimal learning rate occurs when you are 70-75% likely to succeed in a challenge. If it is much easier you tend to downgrade your effort, which leads to learning to half ass at worst, and slow learning at best. If it is much harder the stress of the challenge actually inhibits learning. The exact optimum is thought to be person dependent as a lot has to do with how you respond to challenges, but for the average person 70% effort is the sweet spot.
As I said in a parallel thread, if you're going for "most number of facts you can recite per hour of time spent studying", I can well believe flashcards with a 70% failure rate are "optimum". But if you're trying to have a conversation, watch a movie, or read a newspaper article, and there's a 30% chance you're not going to recognize any given word, you're going to have a hard time.
What you really want is an appropriate level of difficulty for an entire thing you're trying to understand. This could be either because you have one completely new word per paragraph, or because you have 5 moderately difficult words, or 10 not-too-hard words. The fact that the rest of the words might already be super easy for you doesn't mean you aren't still reinforcing them.
That's basically what my algorithm is trying to do: hand you something to read (a sentence, paragraph, section, chapter, whatever) that's at the "right level" of effort for you.
> 1. The words aren't in context, which leads to "flashcard blindness", where you see a word in context, know that you recognize it, but can't remember what it means.
Clozes should help with this. If anything, I sometimes find it easier to remember words when they're presented in a sentence, though obviously you have to be cautious of overfitting.
In my experience, it's actually easier to memorize a sentence than to actually learn the principles behind something. (Turns out this is also true for neural networks, and there are loads of techniques for counteracting it.)
So OK, to counteract memorization and lack of contextualization, you have 5-6 sentences with the same word. But now Anki doesn't know that they're related, so the SRS system can't actually space out the learning the way it wants to.
With the system I developed, you're given a full phrase / sentence / paragraph / section / chapter, and it separately tracks the words or grammar elements you've seen, in a way similar to that described by OP. So you're always actually reading native content, of which much of the content will be new even if the words are already known to you.
I have been quite enjoying Clozemaster[0] for anki-based language learning. Actually, I think I got the recommendation from HN. I'm still on the free version right now, but the pre-curated lists, ChatGPT integration where it breaks down the grammatical translation of the cloze, and convenient links to wiktionary and native pronounciation examples all to be extremely helpful.
So let's separate out "effective" from "efficient".
I'm perfectly happy to accept that the "every single card is hard" is the most efficient system for memorizing facts: i.e., that if you measure the number of facts you can recite and divide it by the amount spent studying, that SRS will come out on top.
But is that the most "effective" -- will it actually result in you learning more facts at the end of some time frame?
For that you need to know not only the amount learned per unit of studying, but the amount that you actually study; and the amount you study depends in part on your motivation; and your motivation depends on how hard / engaging the study is.
Suppose that with every card being hard, you study on average 20 cards a day; but that with every card being only moderate, you average 60 cards a day. Even if the "effectiveness" of moderate card study is only one half of difficult card study, you still end up learning more, because you've studied three times as much.
The idea that after seeing a given card 30 times over the course of a year, you'd somehow end up knowing it less well than if you'd seen it only 10 times, because it wasn't "hard enough" when you did see it, seems really unlikely to me.
1. The words aren't in context, which leads to "flashcard blindness", where you see a word in context, know that you recognize it, but can't remember what it means.
2. The theory behind spaced repetition is that you learn most efficiently when you try to remember something just when you're about to forget it. But that means that if the algorithm is working properly, every single card is hard. This makes motivation a problem, because you know studying is going to be a grind, not fun.
3. While "being able to remember it after thinking a second or two" might be fine when studying for an exam, or in many other contexts where memorization might be important, that's too slow for languages. What you want for a language is "know it immediately without having to think about it".
4. The "scheduled review" system is too inflexible. Some days you get only a handful of cards to review, some days you get dozens. It's hard to tell when you're starting out how many cards you should be adding each day such that the number of cards match the amount of time / effort you have to study. Furthermore, if you skip a single day, you have twice as much the next day; and if life happens and you end up missing a week or a month, you come back with a giant jumble of cards, half of which you've forgotten, and it's really difficult to dig your way out of it.
By a strange coincidence, in 2019 I also started on an alternate to Anki to help myself study Mandarin. It has some similarities to his system, in that there's multi-level knowledge; but it's different in that instead of having a fixed schedule, it has the concept of "difficulty" and "study value" for each word / grammar concept, and the algorithm tries to give you a full "readunit" of "native input" which will balance the two. "Spaced repetition" emerges naturally from the model, and if you go away for a week (or 6 months), it knows you've forgotten some things, so it gradually refreshes your memory. And because you're reading actual native text, there's something which pulls you in.
It's in closed beta now; the first public language (MVP) will be in Biblical Greek, but the second one (if it happens, maybe in a year or two) will be in Mandarin; and hopefully there will be other ones after that. There's a sign-up form you can use to be notified for updates.
https://www.laleolanguage.com