Nice article! This would've been very helpful a few years back when I was attempting to make an Anki clone while teaching myself to code.
I'd still love to create a better open-source SRS algorithm at some point in the future. Mostly to use for language learning.
When I was learning Chinese characters, I did a bit of a deep dive into the Anki algorithm and found that the biggest flaw (imho) was the Ease Factor knocking a card back to 0.
If you mostly know a card, but miss it on one day for some reason - maybe you were tired or distracted, that card's learning progress should not be totally reset (as if you were learning it from scratch). That leads you to have too many cards to review on a daily basis. Instead, you should use some modifier to increase the interval to a reasonable level.
I think it would be awesome to pair SRS with high quality images and audio, which I find most helpful for language learning. I've used Rosetta Stone and Duolingo in the past; Rosetta Stone has great audio and images but lacks a powerful SRS (it also has a number of other flaws in my mind, but I'll save that for another time). Duolingo is great for grammar and explanations, but I can't take the pronunciations and tediousness of it all.
> If you mostly know a card, but miss it on one day [..] that card's learning progress should not be totally reset [..] Instead, you should use some modifier to increase the interval to a reasonable level.
In anki "Deck Options -> Advanced -> New interval -> 0.50" will make it so marking it as "Again" will cut the SRS interval by 50%, so i.e. if "easy" would have been "1 year", it now becomes "6 months" if it's easy the next time.
The above setting is called out in the "Adjust your New Interval" section of the link you provided, so it looks like you may have just missed that you already found the solution.
I've found 0.5 to be a good value, but you can tweak it of course. The default is 0, which gives the behavior you've observed (reset it all the way back).
> I think it would be awesome to pair SRS with high quality images and audio, which I find most helpful for language learning.
Anki cards can include images and audio, and while a little less well, they also support videos.
It's probably worth trying to create a deck within anki that does what you want before building your own system. It'll at least let you find actual issues with anki, rather than the surface-level misunderstandings of it you've got now.
I admit it's been a few years since I've dug really deep into Anki - you are right, I could probably fix the problems with the algorithm by tweaking the advanced options. Still, it'd be nice to have something that works out of the box.
Yes - I'm aware that Anki cards can include images and audio, and I've used those features extensively enough to know they are definitely lacking for my use case! Mostly, I'd like to make it easier to add the images and audio. The way I've done it in the past was googling for a stock image, downloading Forvo/Wiktionary audio files, and then dropping them into the Anki app. I'd appreciate an easier way to add these audio files and images when I add new vocabulary to my decks.
Anki's editing tools are a really generic, and thus tricky to use, tool, agreed!
Like many other anki users, I've ended up writing code that generates my decks.
If you can think of a better UX for editing cards specific to what you're studying, implementing a tool that creates and edits cards outside of anki, and then using anki for the study part, will probably end up being less work in total.
If it's _only_ the editing part, that's solvable without having to rewrite the whole SRS system too.
SuperMemo is proprietary software, and only works on Windows. Anki's algorithm is deemed adequate by most, and the benefits of newer algorithms which introduce more complexity are marginal/questionable.
From experience, my rep load in SuperMemo was half of what it'd have been in Anki for Japanese. over time, gap would've exploded. Being the only people with 30 years of good data makes a pretty big difference
But is your retention the same? These algorithms can be tuned, and obviously the amount of repetition to retain 95% of info is higher than retaining 90%.
Parent post was talking about algorithms. I linked to the site containing articles and research written by the person who created the algorithms.
It’s completely separate from supermemo the app.
If you’re looking at SRS algorithms, it’s worth looking through, for no other reason than to see what the flaws of the SM2 algorithm (used by Anki) are.
I’ve been using SuperMemo for almost 17 years every single day, started using it before Anki even came out. Too invested in my SuperMemo flashcard collection to jumó ship. I am 100% satisfied with SuperMemo 18’s algorithm, I’ve upgraded every single version since this is kind of my thing now. I’m a guy that does flashcards every day and can almost perfectly memorize everything I put in it.
You're describing a separate problem than ease hell, what you are looking for is fixed just by changing the lapses->new interval setting to be from 0% to something higher like 50%. I do agree that the default 0 is pretty bad though.
>That leads you to have too many cards to review on a daily basis.
Anki Review Hell.
I created Memory Hammer to solve this[1], It's an always on E-paper Anki client on a Raspberry Pi Zero. I can now review cards during small breaks from work regularly, Instead of a dedicated review schedule where its easy to get overwhelmed.
That's neat! You'd be having better latency than memory hammer but loosing out on always-on feature which IMO helps is addressing review hell.
I'm planning a companion mobile app for memwory hammer which would help me choose decks, Say if I want to revise a particular subject or language for test.
You might find this app relevant, one of the ideas behind it is that by selecting your own images the concepts stick better: https://fluent-forever.com/index.html
A small SRS system is my go-to project to learn any new programming language, it really helps to already know the problem inside-out when you're learning a new set of tools.
I've experienced what you describe with Anki also. Moreover, I've felt there needs to be a model of the user's current acuity, because it's highly variable. If I make one error, then yeah, apply the SRS algorithm to that card. But if I get 50% more errors than expected, maybe I'm just exhausted and the session should take that into account. I used to use Anki to study Chinese characters on the way home from work, and after a long hard day with poor sleep the night before, I hit the learning wall a lot faster than usual.
tl;dr a bonus effect of regular Anki practice is you can detect when you're in a stupor
Shameless plug: I developed a space repetition algorithm which has a different goal. Mine is focused on learning a large number of new things fast, rather than adding new things over time and retaining memory of them.
I don't quite understand how that is different? Is the idea that you have nebulous knowledge of more things instead of locked in knowledge of fewer things?
What's your use case for this? Would it be good for learning say the top 5000 words in a new language or something like that?
I've used Anki to learn languages with SRS for years. I keep running into the same problem, even with difficult languages: my knowledge of the language quickly outpaces the rate at which I can study new cards, and my ability to retain vocabulary doesn't really seem to fade. Once I learn words, I don't need to review them in x months or years. I just know them and, for the most part, won't forget.
I learned Hungarian (one of the more difficult languages to learn) by loading up a database of ~10k cards. While it worked great in the beginning, within a year, I was just constantly telling the algorithm that I didn't need to review cards for 3-5 years. But the thing is, with a language, I'm not going to forget all those words in 5 years. There will be no point in reviewing them. So I just started deleting cards if I knew them already. And eventually I was spending so much time deleting cards that I just gave up on SRS altogether.
It could be that I have some latent savant-like language learning abilities, though I doubt that. I think my experience is likely related to language learning itself. With a language, if you are immersed, you have constant daily repetition with or without the cards.
All that to say, I think SRS algorithms should have a language learning mode that automatically archives vocabulary cards once you've mastered them to a certain degree. I'm not sure what would work best. Maybe once you've crossed the 6 month threshold, it could just automatically archive the card.
Language learning has a lot of quirks and this just isn't the case for most people who didn't learn a language early on, and if you're immersed/actively using the language then you're way less likely to forget. Your daily usage and exposure is simply your spaced repetition.
If one is bilingual at an early age, then it's much easier to learn/retain additional languages. Most language learning in America starts kids off much later than it should (middle or high school). Full immersion is also a great way to learn.
And as far as SRS goes, it's important to not make cards if you think they're not necessary as it's easy to just make the whole review process too much to chew on. And sometimes you can cluster information in a single card (maybe a sentence that's an idiom if using language as an example) such that you do get continued exposure, and can decide later if you need to break it up if you can't recall some parts of it.
I think you should just drop SRS and print a vocabulary sheet a day, read it and place the sheet it in the shredder and never worry about that page again.
There was a "retirement" add-on to address this issue. Not sure if it's still maintained, but it automatically "retired" (suspended, moved to another deck, etc.) when a card reached a certain interval.
I have been thinking about learning apps for years now (without actually trying my hands at the problem) and my idea for solving this is dropping cards, and instead use a “knowledge graph” and its nodes as the primitives. Then you can have the vocabulary and for example example sentences with a missing word. If that example sentence contains a word you have also in your database, answering this harder question will also count as a review for the used words, so you won’t have to review them separately anymore, while you still get to practice them at no additional effort.
Interesting idea, though it's important to note that vocabulary is probably only 20% of learning a language. There's also grammar, pronunciation, tone, etc. And many languages don't even have a clear concept of what constitutes a "word".
I've had a similar experience. I think the SRS algorithms are tuned with the assumption that you're not doing anything else to remember the words. If I'm learning a language, and I'm doing SRS and reading the news every day, the SRS gets less useful.
My takeaway is that SRS is great for the early stages when it's impossible to grok real material. You can rely on the SRS to help you "bootstrap" your language knowledge to the point where you can start reading. Once you reach that point, you're free sailing and don't really need the SRS.
100% agree with this. In the early phases (in my experience) encountering of new word drastically outpaces your ability to do flashcards. And this is fine! Then, as you progress, you don't don't need to review every damn flashcard because it's way more basic than the level you're at.
This is is one of the primary reasons why I think SRS is not more widely adopted for languages, the other being that most people will never go through the effort to learn why and how they should do it.
AFAIU, SM2 computes the datetime of a next review, whereas Ebisu models a probability of remembering a given flashcard. It seems it’s a more straightforward representation that’s more amenable to implementing functionalities like “show me 10 least remembered cards”.
It should be noted that Ebisu currently has a severe flaw that causes it to massively underestimate how well you know a card[1]. The author has been working on a rewrite to fix the issue for a year and a half, and is currently asking for comments on the new API for Ebisu v3 which should fix this issue[2]. (The author argues this never would've caused to you to get suboptimal reviews if you use it in the "just give me a few reviews now" mode, but I'm not personally convinced.)
All of that being said, I am a fan of the Bayesian approach Ebisu uses (and Ebisu v3 would allow you to eliminate the awful way SM2/Anki deal with ease factors). Thankfully the v3 Anki scheduler will be pluggable so you'll be able to use Ebisu v3 with Anki on every Anki platform. :D
I enjoy using Anki and have found it effective in the very rare instances where I need to rapidly memorize a lot of material.
But as a developer, I've never found a need for memorization. Am I missing out? Do any developers have suggestions for how to utilize Anki to make myself more professionally efficient?
I suppose one starting point might be to track my most googled phrases ("trim newlines in sed," "bash printf into a variable," etc.) and turn those into cards.
I use Anki every day and it helps in the following ways...
1. It's rare that I have to use Google figure out how to do basic tasks in my Programming Languages of choice (C#, & TSQL). Don't get me wrong I, and everyone on the planet, looks up code on the internet. It's just a more rare scenario for me.
2. I write much quicker then your average developer. Of course most of software development is design and requirements gathering/clarification but when I do get down to writing code I'm very fast.
3. I spot bugs almost immediately because when I go through my Anki deck I get repeated exposure do different error scenarios. In fact I can often simply think about the scenario and it will occur to me where the bug is and what is going on. I attribute this to using Anki on a daily basis.
4. I can read code much better then before I started using Anki. I know at a glance what code is part of the language/framework vs what is custom code for the application.
Could you share how you structure your cards for this ? I've been using Anki for a few months and I've yet to find a way to cover CS topics appropriately.
I use Anki every day. I throw in everything that I've learned during a day, broken into tiny pieces.
All the specific terminology that a new technology uses and the exact keywords that it uses goes in. Any time that I need to look up something and I'm convinced I will need it again goes in. All of the company-specific tools go in.
When it comes to programming I consider SRS useful for things like the tradeoffs and characteristics of various data structures, modelling techniques, and algorithms. Things like code snippets (including shell invocations) I prefer to simply keep in a personal library because those don't require as much innate knowledge.
Compare:
Oh, I have to remove this user from all the services this machine provides? I've got a script for that.
To:
We need to sort all the users by how much they've spent in purchases? A heap sort has a fairly decent worst case but doesn't parallelize all that well and if the users are already mostly sorted it doesn't get much advantage, maybe merge sort would be better...
Nice! When I was building a spaced repetition toy a few years ago I took a different 'bottom-up' approach, where I dynamically fit the forgetting curve to the user's actual performance on any given card to come up with a unique memory half-life value for that card for that user, which was then used to schedule the next review at a given estimated recall % (typically 70-95% depending on whether the user wanted an "easy" or "hard" experience). As you need at least two data points to be able to fit the curve, you never have enough data to schedule the second review of a given card, so for this I used the average half-life accross the set of cards, or hard-coded values if it was the first item in the group. This way a set of cards (such as world flags) was always initially scheduled as though they were similarly difficult for the user, and then harder cards within the set would naturally be reviewed with a higher priority.
Reminds me of one of my backlogged projects: I'd like to have an SRS app that just draws cards at random, with probabilities proportional to how 'urgently' you need to review the cards (using the same logic of exponential growth in review intervals). I just couldn't get myself to stick with Anki so far, because I didn't like the 'X cards to review today' UX of the SM2 algorithm. I also think it's false accuracy trying to predict the exact review time down to the minute, I couldn't find much research supporting that any of the SM family algorithms would be the claimed optimum.
If that already exists, ideally compatible with Anki cards, I'd be happy for recommendations. I have other projects and ideas that I feel would add more to the world than the umpteenth Anki-clone.
I'm surprised that no spaced repetition systems seem to exploit relationships between facts/cards. One might imagine that capturing a graph of similar ideas would allow for better algorithms.
>Some people want to extend this link between arbitrary cards. They want to be able to tell Anki "after showing me this card, show me that card", or "don’t show me that card until I know this card well enough". This might sound like a nice idea in theory, but in practice it is not practical.
>For one, unlike the sibling card case above, you would have to define all the relations yourself. Entering new notes into Anki would become a complicated process, as you’d have to search through the rest of the deck and assign relationships between the old and new material.
>Secondly, remember that Anki is using an algorithm to determine when the optimum time to show you material again is. Adding constraints to card display that cause cards to display earlier or later than they were supposed to will make the spaced repetition system less effective, leading to more work than necessary, or forgotten cards.
>The most effective way to use Anki is to make each note you see independent from other notes. Instead of trying to join similar words together, you’ll be better off if you can determine the differences between them. Synonyms are rarely completely interchangeable - they tend to have nuances attached, and it’s not unusual for a sentence to become strange if one synonym is replaced with another.
Doesn't just putting the cards in order of concepts essentially accomplish this? When I was doing Anki I adjusted my new card rate so my retention rate stayed above 90%. If you do it this way then your chances of getting stuck in a certain areas of the dependency graph (if you were to model it) is pretty low. If it does end up happening then do some extra study on those concepts outside of SRS. This way you don't have to model the actual dependency graph.
You really shouldn't be doing all your study in SRS anyways because so called "flashcard blindness" (i.e. only being able to recall the concepts in the context of your SRS program) is a real thing.
Furthermore, in my experience, what really made Anki effective for me was to write my new cards just in time. Meaning no pre-made decks except for something like the primitives of what you are studying (i.e. symbol names, alphabets, extreme base level concepts, etc). When you write your cards just in time you can really connect the context of what you are learning to the concept. From this perspective it really seems strange to build an entire ontology based on dependency graphs of a concept you haven't even learned yet!
I have found this to be a huge missing piece of any SRSs I've come across. Personally, I have a pipeline that looks at what knowledge I've already learned and only then reveals successive information.
For example, I'm learning numbers in Spanish. First, I ask myself to go from doce => 12; then, 12 => doce. Then, once I have a few numbers in my memory, I can start to combine those into diez + dos = ? (answer doce). The idea is that this last example requires recalling three previously-learned Spanish numbers to arrive at the answer. This is a trivial example but this underlying structure of successive layers of knowledge shows up everywhere and is horribly under-exploited by our current learning systems.
Programmatically this can be done. Manually, this stuff takes way too much work.
It does exactly this because I also thought this was a critical limitation of current systems.
I have a feature coming out soon (already merged to master but I need to make a proper release) which should make it way easier to use with Markdown files acting as the front and back of flashcards that are automatically picked up when starting up the program.
Trane looks very cool. I think it's also a great idea to have example (guitar, rust) projects that show the tool in action.
How do you find yourself engaging with it? A big positive for me of Anki is the ease with which I can review a single fact (~seconds). It looks like Trane is intended for more dedicated situations, probably for ~tens of minutes at a time. Is that accurate? I'm basing this off of playing with the Rustlings example.
I think it should work for facts, but I haven't personally tried much of that. Right now, it doesn't make a distinction in how it schedules each type of question. I have an update coming that should make it way easier to play with it for this type of purpose. The changes are still not in a formal release, but they are in master and the docs at https://trane-project.github.io/generated_courses.html should give you an idea of how it will work.
For music practice, I find it really helpful. Simply knowing the next thing you should practice without having to think about it too much is very helpful. I find it easier to practice for longer without getting bored as easily. If I get frustrated with something, I can get another question very easily. I find myself not realizing half an hour or sometimes more has gone by, which is not the case as often when I don't use it.
Interesting ideas for how to write the cards. But the dependency part seems to be limited to letting you pick questions from a subtree. SuperMemo has something similar, which is the closest I was able to find in existing solutions.
The problem is that this basically means dependencies can only form a tree, with one topic depending on only in at most one other topic. What I wanted was something that let me express the relationships as a directed acyclic graph. Something like learning a big chunk of a music piece is dependent on the questions for learning individual smaller passages. Something as basic as that cannot really be expressed with a tree but is trivial with a DAG.
Yes. That's required. I think they meant that the program uses those relationships to automatically advance the student to the next topic when it makes sense.
Wanikani [1], an SRS for learning Japanese kanji and vocabulary, kinda works like that, though on a simpler level than what I think you envision.
Basically their system involves first learning to recognize common parts of kanji (radicals), then learning to recognize kanji made up of those radicals, and finally learning vocabulary that uses those kanji. Later items in the graph are unlocked by getting the previous items to a certain SRS stage. Additionally, the entire syllabus is divided into 60 discreet levels of (very) roughly the same number of items, the next level being unlocked by getting 90% of the current kanji to a certain SRS stage, which helps keep it all manageable.
I tried using Anki before, but found the Wanikani "method" to work much better for me since I could constantly build on things I had learned previously.
I have been working on a form of this for a language learning app [1]. The basic idea is that it gets you do free form output (discussion questions, word games, etc), gives you feedback on where you have errors and then sets up SR flashcards automatically (e.g. if you don’t know a word, conjugate it wrong, etc).
It uses embeddings to make inferences across exercises and flashcards. This is handy for:
- detecting that you already have a flashcard for that particular word/phrase/grammar issue
- you know one sense of a word but not another
- you correctly used a word that you have a flashcard for and that review can be put off
It can look at your vocab and grammar skills of a particular area overall using Rasch scoring. Perhaps you answer almost all the questions about “cooking” correctly but none about “parts of the body” - well then the app can focus on the second area.
There is also work to be done to build out better regressions given this data. If a word I am studying is way above my skill level, I will probably forget it faster. Or maybe I forgot a conjugation which is very common. It should be easier to remember because I’ll encounter it more.
Whether or not my app succeeds, I think these types of approaches hold a ton of promise to increase learning efficiency.
In my personal system, I tag facts by sub-topic and the daily reps are ordered so that facts of the same sub-topic are shown consecutively to avoid context switching (eg. AAABBCCCCD not CABCCDBACA). In addition, there is logic so that after the mandatory daily reps are done, it will show cards that are close to the due-date (eg. within 0.25*LastInterval), again batched by sub-topic. This lets me efficiently get reviews out of the way on days when I have time to do so, without deviating too far from the basic SRS philosophy. As a result, for mature cards, I end up focusing on a small number of sub-topics on any particular day. I find that works a lot better.
All of this is trivial to implement - I think it's best to create from scratch and specialize it for one's own domain instead of using a pre-existing solution. In my case the relationships are straightforward (sub-topics) but in others it might be more fuzzy. Programmatic fact-generation/deletion is also something I put a lot of effort into (again, domain-specific).
More precision isn't that important-- at worst, you slightly over-review some information, but that's no different from using a flash card's information in normal life outside of a review period.
Dependency relationships between exercises are core to its design.
I tried to use Anki but this limitation is pretty baked into Anki and made it unusable for cases where there's a clear order in which things must be learned (music in my case).
This looks like a very cool project! The docs include this description of the core algorithm:
> The space repetition algorithm in Trane is fairly simple and relies on computing a score for a given exercise based on previous trials rather than computing the optimal time at which the exercise needs to be presented again. This will most likely result in exercises being presented more often than they would in other spaced repetition software. Trane is not focused on memorization but on the repetition of individual skills until they are mastered, so I do not believe this to be a problem.
- When Trane is asked for a batch of exercises, it performs a depth-first search of every branch in the dependency graph. It picks up exercises as it goes along, and stops exploring a branch when the dependencies of the last node are not met.
- Each exercise is given a 1-5 score by the student and those scores are used to produce a combined score, rather than compute a date on which the exercise should be reviewed. Two reasons:
- Trane is also meant to be used to learn skills, which do not fit the assumptions made by the SuperMemo algorithm.
- I believe most of the gains come from regularly being reminded to perform an exercise, so even a simple scoring algorithm should get most of the gains. Eventually, I want to replace the existing scoring with something more complicated that also makes a distinction between memorization and skill exercises to get better results. But based on my testing, this works for the most part as is and the gains to be made from more complicated algorithms are marginal.
- Once you get exercises from performing the search, you reduce that number into the final batch by grouping exercises based on difficulty and selecting a percentage from each bucket. This is done to make sure not too many very difficult or easy exercises are included in each batch.
There's also some elements of randomness added to prevent the same exercises from appearing all the time. For example, the branches are explored in random order and the elements from each difficulty bucket are selected at random. But that is the gist of it.
> I'm surprised that no spaced repetition systems seem to exploit relationships between facts/cards.
In fact, the Anki community largely endorses as an article of faith the complete insular atomicity of cards. I’ve always treated that orthodoxy with skepticism because I’m pretty sure that’s not how my brain works. Nor anyone else’s, probably.
An interesting example is chessable, which is a spaced repetition system for chess. It keeps track of each move in a sequence of moves separately, but every time it asks you to re-learn a specific move, you are asked to play out the whole sequence, instead of just being shown the position and asking you to play the next move.
One underrated benefit of using Anki is you become a god. In a meeting, when I can recite the Q1 revenue along with the names of everyone working on the project, everyone automatically becomes submissive to me because they think they have just observed a superior intellect.
The primary hurdle is the number of "activity types" practice can come from. Simply reviewing material isn't sufficient for learning and so we need more engaging practice as well. Also, similar to how "all students are different", topics encompass a large number of subskills that need to be trained and not all activity types target those skills. Also, "learning" something is quite complex - is simple repetition of the fact sufficient, or do we need the ability to apply the skill?
Its actually research we're doing at NC State. At present, we understand spaced, deliberate practice is beneficial [1] but students will largely reject AI recommendations to complete lower-level practice [2]. So our current methods are to develop a system that maintains an "instructor in the loop" to create tailored training regimens (my terminology for it), similar to how a coach builds regimens for athletes. The hope is that if we can analyze how instructors made their recommendations, we could then design AI to mirror the decisions.
Thank you. I suppose I was downvoted, hiding your careful response, because I appeared to be pitching a product. I was attempting a precise analogy, I had nothing to gain from the promotion. I was robbed on BART recently, and my judgment is off.
I've relied heavily on spaced practice for learning languages for travel. There are two phases to that experience: Study, where one takes in as much as possible, then real world practice without further study. I find myself wishing I'd memorized a play for the first phase, to access faster than a phrase book on the ground, with lots of pathway shortening while I travel. Learning models tend to assume learning is single-phased, though even more academic learning is two phased: acquire, then later apply.
That's actually where the inspiration of my work comes in. Outside of academia, I also teach martial arts, where practice is often structured as "present" -> "practice" -> "introduce new element" -> "practice" -> "introduce new element" -> repeat. The hope is the training regimens will help model that a little.
Another note when I think about learning is how, as adults, we are resistant to many of the techniques we give children. Thinking about Sesame Street, think about how much a skit/song will focus solely on something like the number 3. Simplified language, repetition, and enjoyable content are things adults think are "beneath them" despite the fact they work, just not as "fast" as adults want.
I'd still love to create a better open-source SRS algorithm at some point in the future. Mostly to use for language learning.
When I was learning Chinese characters, I did a bit of a deep dive into the Anki algorithm and found that the biggest flaw (imho) was the Ease Factor knocking a card back to 0.
If you mostly know a card, but miss it on one day for some reason - maybe you were tired or distracted, that card's learning progress should not be totally reset (as if you were learning it from scratch). That leads you to have too many cards to review on a daily basis. Instead, you should use some modifier to increase the interval to a reasonable level.
More explanation here: https://readbroca.com/anki/ease-hell/
I think it would be awesome to pair SRS with high quality images and audio, which I find most helpful for language learning. I've used Rosetta Stone and Duolingo in the past; Rosetta Stone has great audio and images but lacks a powerful SRS (it also has a number of other flaws in my mind, but I'll save that for another time). Duolingo is great for grammar and explanations, but I can't take the pronunciations and tediousness of it all.