In the last installment, I argued that sometimes we need to learn (gotta start easy). In a couple of Quora answers, I’ve been building up how we do that: an overview of effective learning techniques and a deep look at perceptual chunks and deliberate practice. What I’m slowly working toward is a philosophy and framework for designing learning tools. Today I want to talk more about a core tenant of this philosophy (hinted at in the last paragraph of my deliberate practice answer as well as a weekly review about Shuff’s philosophy): we are naturally good at learning within our local environment. However, we are increasingly living in a global, disconnected, artificial world that requires artificial environments to act and learn effectively.
Consider the example from Design vs. learning: my concern about my friend’s choices about bottled water was not about perceivable effects in the local environment, but rather the effects from its manufacturing halfway around the world. Rather than naturally learning within a responsive external representation, we require building up a mental model and evaluating decisions by running the effects of an action on that model. As my friend illustrates, this takes more work than most people will do, even with good intentions.
Let me revisit my favorite learning topic, spaced repetition, with this philosophy in mind. Scott H. Young and Khatzumoto are two of my favorite bloggers that write about learning. Several years ago Khatz introduced me to spaced repetition. To say it left an impact is an understatement. Since then I’ve made a series of projects that use spaced repetition in online learning systems. But I’ve always questioned when exactly spaced repetition is a good design choice for a learning tool.
Scott’s MIT Challenge is his attempt to study a four-year MIT curriculum in one year using online resources. He recently posted a great article questioning the value of spaced repetition for his own learning. He argues that aggressively pressing forward in learning new and more advanced material will naturally re-expose him to material from before, making a spaced repetition system unnecessary.
Such a result has been found for an elementary math curriculum. In Why Students Don’t Like School, Daniel Willingham (another of my favorite learning bloggers) summarizes the results of a longitudinal study: “A student who gets a C in his first algebra course but goes on to take several more math courses will remember his algebra, whereas a student who gets an A in his algebra course but doesn’t take more math will forget it. That’s because taking more math courses guarantees that you will continue to think about and practice basic algebra.”
Enter Khatzumoto. In his Unified Reading Process, he uses decks in a spaced repetition system as a collection system for everything interesting that he encounters. He lists thirteen examples of decks that he uses!
Some things that humans learn fold out naturally. Our brains co-evolved with the environment to let that happen. When we are born into the world, that world has, for hundreds of thousands of years, been one where we are expected to be in a society of people who talk to us as we naturally learn language and how to attract and care for other people and in an environment where learn how to navigate and hunt.
A good curriculum teaches you an artificial subject in a similar manner. It’s like a game that’s carefully set up to advance in difficulty as you use the skills and equipment you’ve gathered on the way. Subjects like math and science may be artificial, but curricula have undergone thousands of years of refinement to be somewhat learnable. This is what Scott Young is relying on when he presses forward with the MIT challenge.
But Khatzumoto’s method is setting up an artificial world where one doesn’t exist, where not even a good curriculum exists. He’s building his curriculum in place. Topics like foreign languages have some options for curricula, but why not make them contemporary and interesting by using real media? There’s no curriculum for being up to date with the latest trends in business or software engineering, but it’s important: you need to be able to converse with others in that vocabulary, and you may pick up some wisdom along the way.
Spaced repetition specifically replicates some of the advantages of a natural environment. Memory works like this: when we encounter important things a number of times in different contexts, we begin to learn them in the abstract. Otherwise, we’d be totally overwhelmed by the number of abstract concepts we could apply in any circumstance. By artificially spacing repetition, we allow the context to vary via the passing of time. Not only do our physical surroundings change, but the knowledge we have that can be related to what we learn changes.
So I don’t agree with the extent to which Scott thinks aggressive learning makes spaced repetition unnecessary. Moreover, he’s overlooking the “spaced” part of the concept: that not repeating, and allowing the context to vary, is an equally important part of the equation. But there is an art to keeping that environment spruce: Khatzumoto says, “I choose decks in order of fun/priority and I delete extensively. If I’m avoiding a deck, then I go on a deletion spree, and I keep deleting until the deck feels good again.” More on that to come!