Summary: Since using spaced repetition software as a learning tool and memory aid seems to still be relatively niche, I give a brief introduction to spaced repetition learning and why I’ve found it useful. Then, I link to some of my current shared decks, broken down by topic, made using the free, open-source software Anki. First Published: 12/10/12. Last Updated: 1/13/18.
Most of the time, it is acceptable to learn something in the short-run and accept that you will mostly forget it later. Maybe you don’t care about the material long-term and just want to pass a course. Or maybe you need to understand a topic to execute a two-month long project, and after that you have good reason to expect the knowledge will lose its value.
But sometimes forgetting information over the long run is intolerably inefficient. For example, if you learn some aspect of statistics in a course and want to be able to apply it in your research career over the next ten to twenty years.
In the education system that I was raised in, you were incentivized and aided to learn topics one semester at a time and implicitly expected to understand them from then on. This is an excellent way to retain information for a few months, but does not lead to efficient generation of long-term memory.
Instead of this, there are two effects we want to take advantage of. One is the spacing effect, which claims that humans learn things better when they learn them a few times over a long time period rather than the same number of times all at once. Taken to its logical extreme, the effect suggests that there is an optimal point at which it is best to recall something, which is when you are about to forget it.
The second effect is the testing effect, which claims that humans learn better when they are forced to actively recall or use an idea instead of passively reading or being exposed to it.
Computer-based spaced repetition systems with flashcards represent the parsimonious union of these two effects in an actionable and convenient format. The algorithms are automatically designed to refresh your understanding of a topic when they estimate that you are likely to forget it. Study interval periods increase exponentially with repetitions, so, for example, you will be re-tested on a note in (approximately) 1 day, then 4 days, then 12 days, then 1 month, then 3 months, then 9 months, and etc.
By taking advantage of the spacing effects and testing effects, spaced repetition can allow you to learn in ways that are more likely to stick in the long-term.
Although I’ve been doing spaced repetition via Anki for awhile now, I actually wanted to start doing SR flashcards for several years before I actually started — approximately speaking, since 5/6/08, when I read this Wired article about Piotr Wozniak. Wozniak truly devoted himself to spaced repetition. He just generally seemed like an interesting person, his technique seemed like a good idea, and I wanted to do it.
For the next 3 years after that I had a pretty constant low-level of guilt/anxiety about how I should be doing SR flashcards. This anxiety spiked whenever I forgot something I had previously learned or clicked on a purple Wikipedia link. Of course, after a year or so, I thought it was too late and that I was a lost cause with respect to spaced repetition.
Coincidentally, I now have a flashcard for how to solve this exact problem. It’s called “future weaponry” — instead of thinking about what you could have done with a particular tool/knowledge/ability in the past, choose to think about what you will be able to do with it in the future.
Spaced repetition flashcards weren’t really feasible, though, until I got a smartphone. Say what you want about smartphones with respect to productivity overall, but the ability to do SR flashcards on them while walking and waiting around is insanely crucial and underrated. And this basically requires easy syncing via an internet connection.
Looking back at my cards from when I started, they’re pretty terrible. For example, I used tons of cloze deletion cards, a lazy and less effective way of making flashcards, rather than thinking about what knowledge I really wanted to retain and framing it in question form. I was also obsessed with memorizing math equations even though these have been pretty much completely useless. That said, by far the most important thing for me back then was to maintain motivation, and I’ve been able to do that so far.
When I was doing full-time research, I was mostly using SR flashcards for two purposes:
- Learning programming languages, both the syntax and the concepts. The syntax has probably been more useful, but learning vocabulary related to the concepts has also been especially high yield for knowing what to search for.
- Learning about my research topics, e.g., Alzheimer’s and genomics. The jury is still out on whether this is a good use of time, but in general, I would say not to sleep on the potential for it if you’re a researcher. A lot of people like to read, but there’s a lot of value to be gained from systematically reflecting upon what you’ve read, especially if you have an imperfect memory like me.
I owe a lot to Damien Elmes, who wrote the open-source, free Anki software. I also owe a lot to Gwern, who wrote about spaced repetition extensively, and who made thousands of his Mnemosyne cards available to anyone to download for free. I downloaded these one day on a whim, converted them to Anki, and that was what really made me think of having my own cards as being a realistic, practical option. Thanks, Damien and Gwern.
Here is a link to the deck. Major sources:
- Dan Klein’s “Lagrange Multipliers without Permanent Scarring”, tutorial, pdf. Under the tag “LAGR”.
- Wikipedia’s article on complex numbers.
- Santo Fortunato’s “Community detection in graphs”, article, arxiv. This is a major source for the “NETW” tag.
- Python documentation, article. Major source for the “PYTH” tag.
- Stack Overflow user e-satis’s classic answers on the yield command, decorators, and metaclasses.
- Wikipedia’s article on character encoding.
- Wikipedia’s list of programming languages by type.
Here is a link to the shared deck. Major sources:
- Wikipedia’s list of cognitive biases. A major source for the “BIAS” tag.
- Wikipedia’s article on the Big Five personality traits. A source for the “PERS” tag.
- Dan Ariely’s RSA Animate video: The Truth About Dishonesty. Referenced under the search term “Ariely”.
- Memory-related terms from Eric Kandel’s book “The Principles of Neuroscience”
- Psychological nuggets from Jonathon Haidt’s book “The Happiness Hypothesis”.
- Psychologial nuggets from Sarah Perry’s book “Every Cradle is a Grave”
- Reinforcement learning and animal behavior, some from “Learning and Behavior”, Paul Chance’s book, amazon. Under the “BEHA” tag.
Here is a link to the basic statistics and intuition deck. Some sources I found helpful:
- Judea Pearl’s ”Causal inference in statistics: An overview”; paper, pdf. Pearl calls this “everything I know about statistics in only 40 pages.” A major source for the “CAUS” tag.
- Edwin Jaynes’ Probability Theory: The Logic of Science; amazon, pdf of TOC and preface. Heavily Bayesian. A major source for the “BAYS” tag.
- Cosma Shalizi’s “Advanced Data Analysis from an Elementary Point of View”, page. Noted as the Shalizi reference.
- Bayesian Data Analysis, by Devinderjit Sivia and John Skilling; amazon. The first five chapters. Noted as the “S&S” reference; also a major source for the “BAYS” tag.
- Wikipedia’s list of probability distributions the major ones. A major source for the “DSTN” tag.
- Lindsay Smith’s Tutorial on PCA, pdf. Philipp Janert’s book Data Analysis with Open Source Tools, google books, the chapter on PCA. Wikipedia’s page on eigenvalues and eigenvectors. Under the “EIGN” tag.
- The book “An Introduction to Statistical Learning”; page. Excellent introduction to statistics. Gareth James, in consultation with co-authors, kindly gave me permission to post some of the figures from this book in the deck.