Hi all!
Following on from my recent posts re: my current project www.teachingyouhowtolearn.com (aiming to (a) advocate for meta-learning and (b) act as knowledge sharing hub / flash card repo for EA), I'm excited to announce that the "Key Ideas Guide 2: Learning, Knowledge, Intelligence, Mastery, Anki" website post has just been published (it's 10 to midnight in England, I've been in serious flow). Link here.
I'd really appreciate any feedback, and also if you know anyone to share it/ the project with that'd be great!
Copy paste of the intro below:
Introduction
With this website, I’m aiming to share the essentials of “meta-learning”, the field of learning about learning, in order to help empower people to increase their ability to learn and form knowledge. I used to feel incredibly daunted by new fields, but now feel completely empowered to gain deep understanding of new topics at a much improved pace to my old methods. Meta-learning also ensures that you retain your learnings over the long run, meaning you can engage in “cumulative” learning, which is a huge gain from the “sand spilling from your hands” feeling I had re: learning during University.
In the field of meta-learning there are a bunch of empirically demonstrated methods for how to learn, how to retain information, and how to gain deep understanding. The shocking and literally thrilling thing is that the free & open source tool Anki contains most if not all of these techniques. I feel silly in a way with how much I talk about Anki and how it’s really all I think you need to supercharge your learning, but it really is. Onwards!
See the footnotes for this post here
“Anki is just for learning facts”
For those who have heard of Anki but haven’t used it, I believe there’s a common thought (based on conversations with friends, comments on the internet, and feedback I received when first sharing this website with people) that Anki is good, but it’s really only for memorising things, for learning static facts, etc. We live in a time where memorisation/ learning by rote either just isn’t part of what we’d consider part of the toolkit of learning, or something that is actively looked down upon. As briefly argued in my first post (and heavily supported by the science and meta-learning practitioners), memorisation is actually one of vital components of learning, knowledge formation and intelligence.
I'm therefore here to argue that Anki is actually the most powerful way to improve knowledge, understanding, and even what constitutes intelligence. It’s a profound example of a tool for thought that allows us to greatly increase our capacity to learn via the use of computers.
I'm going to be pulling heavily from the book Make It Stick as well as my own experience of using Anki for years.
A note re: my learning workflow: I also use a tool called Obsidian (similar to Roam Research but open source and (whisper it) better) which is the other vital part of my learning framework. However, Anki is where the vast majority of true learning & knowledge consolidation occurs (whereas Obsidian is more for note taking and acting as a “second brain”). The two represent the “inert vs activated information” idea that I’ll touch on later.
This article is going to be an attempt to collect all the relevant topics/ concepts in a semi-structured narrative: whilst I’d love to take the time to really hone the ideas, I a) have limited free time outside of work and b) really want to get this stuff written down in relatively coherent but unpolished way to let me move onto other parts of this project (you should see the to do list for this site!!). There are loads of relevant things to cover. It’s worth saying that none of this is my own research: I’m just synthesising a few sources[1] into one guide along with my own experiences and learnings. The fact that I’m so excited about sharing this stuff is a testament to just how profoundly powerful I think this all is, and I’d really encourage you to give it a try.
A really quick (< 1000 words) primer on Anki
Before getting too deep into the weeds, here’s a really quick view on what Anki is and how you use it, just to give some context for what will follow.
(see page for full post)
Hi Alexander, thanks for writing this up!
Some context. I used to use Anki for 1-2 years. Completed the "Learning How to Learn" MOOC and read the book it was based on. Taught 13-16 year olds math and English for 2 years. Conducted EA presentations in Malaysia and previously in Singapore. Currently running EA Virtual Programs (I noticed that you're in the intro program!). FYI, my opinions are mine and not CEA's.
In conjunction with "learning how to learn better", "learning how to prioritise which learning strategy works for specific scenarios" seems just as important. It's really hard to know:
I think for many of the us, time is likely one of the biggest bottlenecks to better learning. For example, I really really want to apply a lot of the meta-learning tools when I'm reading The Happiness Trap, but I intuitively chose to just do two things only:
In my case, I don't think doing deep learning (e.g. writing notes, creating space repetition notes, reflect, do exercises, discuss, etc) is what I needed considering how busy life is for me now. My end goal is to be more sustainable mental health wise, and I want to apply the tools I've read in the book. It seems like the value of information is high here for achieving my goal, but the value of easy retrieval of information is low because I don't know how I'm going to use it or when I'm going to use it.
But again it's hard to know whether a certain information is valuable and should be easily retrievable. One failure mode that could happen is not being able to make a connection with something else important because I didn't do enough deep learning. Like if I didn't understand the concept of "cognitive fusion" fully, I might be forgoing a potential connection with another therapy technique that can help me better. But it's really hard to know for sure beforehand.
Applying this to EA VP, I wonder if there are certain key learning outcomes that participants should really internalise and do a lot of deep learning; and, whether there are other learning outcomes that are less important that reading and remembering fuzzy impressions of it is enough for most participants.
That makes me think that we should try to be clear as much as we can with the value of information and the value of easy retrieval of information for most of our learning outcomes so that participants can say, for example, "oh EA VP says X is super important and will likely need it in future work, so I should do more deep learning here. And Y isn't so important, so I'll just read it."
Besides these two things, I wonder if there's a simpler heuristic for choosing when one should prioritise doing deep learning versus prioritise doing shallow learning. Or something in the middle, which is the likelier case.