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663 points domenicd | 3 comments | | HN request time: 0.885s | source
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bearjaws ◴[] No.44020922[source]
Spaced repetition has been all the rage for 20 years now.

Dozens of apps, thousands of lectures, and it turns out its not really a silver bullet.

There's nothing really wrong with it, it's just that people tend to fall off the same way they do on any other education pattern.

A couple years ago I was thinking "If Google and Apple really cared about kids they would make a spaced repetition unlock system", where by you have to make note cards every week and then have to answer correctly to get into your phone. (obviously requires some bypass system, other rules, etc)

You could probably jury rig it with a popup that comes up after you unlock, but people would never install it anyway.

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InkCanon ◴[] No.44021340[source]
There's some UX problems of SRS (that I'm working on) that makes it high friction 1) Time taken to create cards 2) Need for self marking 3) Creates a one to one mapping of prompt-answer 4) If you're an autodidact, you have to teach yourself first (alternatively called understanding, scaffolding, etc)

More fundamentally, SRS isn't a superpower because it's just very specific to creating a direct prompt retrieval. Generalization is poor. Even creating a graph of knowledge, is a chain of edges between bits of knowledge, isn't done very well here.

And I suspect there's a very deep, fundamental difference between recollection knowledge and logical-modeling knowledge. Recollection seems very similar to a dictionary access, and if you recorded the time to recall in humans I suspect they'd all be constant. But learning the knowledge of a logical model, like of a mathematical concept, appears to be vastly different and have very different time to compute.

Proponents of SRS will point out logical models need facts as well, like formulas, lemmas, etc. Which is true. But if you already grasped it before you'd grasp it faster the second time. So the practical use of SRS is a significant step above having a very well sorted and labeled notebook, but still way below becoming a genius.

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1. nomadpenguin ◴[] No.44021714[source]
Poor generalization (overtraining on prompts) and loss of context over time are the biggest issues I've found with them. Slow card creation workflows and needing to rate your own reviews are merely UX issues -- losing context and losing generalization make SRS actively harmful when used for some topics.

There's 2 solutions I've thought of but haven't tried implementing:

1. A free-recall based approach. Free recall allows you to operate at a higher level of organization and connect concepts at lower levels. However, how you would schedule SRS with free recall is not clear.

2. Have an LLM generate questions on-the-fly so that you don't overtrain on prompts. You might also instruct the LLM to create questions that connect multiple concepts together. The problem with this approach is that LLMs are still not so good at creating good test questions.

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2. barrell ◴[] No.44022238[source]
I implemented free recall into FSRS pretty easily. Granted, it’s only for language learning, and I have it set up to work in a free recall friendly way (you don’t learn cards, you learn actual words and morphemes) but it’s been working for a few weeks now. I’m working on a product video atm, but once that’s done my next task (sometime this week) is to clean up the UI and merge it to master.

I almost never see someone talk about free recall so I was too excited to see it mentioned not to comment

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3. nomadpenguin ◴[] No.44023681[source]
How are you handling scheduling with FSRS? The challenge that I quickly saw was that it was difficult to figure out when you should advance a segment of information. If you get 80% of the info right, should it be advanced? What happens to the 20% you missed? How do you prevent yourself from missing the same 20% every time it comes around?