There’s a phenomenon that verteran Anki users are familiar with - the so-called “Anki hell” or “ease hell.” Origins of ease hell The descent into ease hell has to do with the way Anki handles correct and incorrect answers when it presents cards for review. Ease is a numerical score associated with every card in the database and represents a valuation of the difficulty of the card. By default, when cards graduate from the learning phase, an ease of 250% is applied to the card.
In Anki 2.1, it’s practically impossible to get plain text from the web into note fields. A solution (on macOS, at least)…
Yet another diversion to keep me from focusing on actually using Anki to learn Russian. I stumbled on the R programming language, a language that focuses on statistical analysis. Here’s a couple snippets that begin to scratch the surface of what’s possible. Important caveat: I’m an R novice at best. There are probably much better ways of doing some of this… Counting notes with a particular model type Here we’ll use R to do what we did previously with Python.
Continuing my series on accessing the Anki database outside of the Anki application environment, here’s a piece on accessing the note type model. You may wish to start here with the first article on accessing the Anki database. This is geared toward mac OS. (If you’re not on mac OS, then start here instead.) The note type model Since notes contain flexible fields in Anki, the model for a note type is in JSON.
I previously wrote about accessing the Anki database using Python on mac OS. Extending that post, I’ll show how to work with a specific deck in this short post. To use a named deck you’ll need its deck ID. Fortunately there’s a built-in method for finding a deck ID by name: col = Collection(COLLECTION_PATH) dID = col.decks.id(DECK_NAME) Now in queries against the cards and notes tables we can apply the deck ID to restrict them to a certain deck.
Not long ago I ran across this post detailing a method for opening and inspecting the Anki database using Python outside the Anki application environment. However, the approach requires linking to the Anki code base which is inaccessible on mac OS since the Python code is packaged into a Mac app on this platform. The solution I’ve found is inelegant; but just involves downloading the Anki code base to a location on your file system where you can link to it in your code.
For the last two years, I’ve been working through a 10,000 word Russian vocabulary ordered by frequency. I have a goal of finishing the list before the end of 2019. This requires not only stubborn persistence but an efficient process of collecting the information that goes onto my Anki flash cards. My manual process has been to work from a Numbers spreadsheet. As I collect information about each word from several websites, I log it in this table.
As I’ve written before, I use Anki for Russian language learning. One of the skills to master in learning a foreign language is to quickly speak and recognize numbers. With a little help from macOS, I’ve developed a way of rapidly creating audible content of spoken numbers for my Anki cards. That’s the good news. The bad news is that as of right now, you’ll have to have Xcode and build the app yourself.
The spaced repetition software system Anki is the de facto standard for foreign language vocabulary learning. Its algorithm requires lots of performance data to schedule flashcards in the most efficient way. Anki displays these statistics in a group of thorough and informative statistical graphs and descriptive text. However, they aren’t easily available for the end-user to export. Thus, the reason behind the companion projects AnkiStats and AnkiStatsServer. The premise is that you can run your own more extensive experiments and statistical tests on the data once you have it in hand.