We use the word “learning” to mean a lot of different things. You can learn how to cook, learn to speak another language, learn to ride a bike, learn that person’s name, learn to read, learn to behave, and learn physics. Some learning is about knowing information, some learning requires refining decisions or body movements, some learning means changing our minds about something. The word means too much.
That’s why it’s helpful to think about the kind of learning we’re talking about. Different kinds of learning require different approaches.
Learning as information acquisition
One of the most basic forms of learning is about acquiring new information. For instance: we didn’t know that only about a third of the American colonists actively supported the American Revolution and now we do. In between not knowing and knowing, learning happened.
It’s difficult to underestimate how much information we acquire over the course of a lifetime: we match names to faces, dates to events, properties to objects, and objects to categories. Think about what a botanist knows — about mechanisms of plant growth, about plant species’ relationships, about geographical distributions, about common uses of plants. Or a mechanic — about the parts of an engine, the different makes of car, the utility of various diagnostic tests. Or a farmer — about plant and animal behavior, about soil types, about machinery, about food storage.
This kind of learning is sometimes sneered at. Yes, she can regurgitate information, but can she really solve problems with it? And yet: information is the backbone of reasoning. You can’t expect someone to be an astronomer without knowing lots of fundamental information about how celestial bodies form and move. Recalling the right ideas at the right time is at least half of the game of problem solving. If you’re solving calculus problems, having a toolbox of integration methods at your disposal is essential. And some information pays unbelievably rich dividends in modern society. Knowing the association between letters and sounds, for example. Or knowing Arabic numerals. You can’t get around knowing this stuff if you want to read or do math.
What learning approaches improve how much information we can acquire and how long we can remember it for? At least three ideas are key.
Cognitive load describes the limits of our working memory to process novel information. This processing limit means that pouring more information into our heads will not necessarily result in learning or remembering more information. That’s why particularly challenging or novel material should be simplified: remove the jargon, focus the visualizations, talk slowly, etc. Often, this means deciding what is the most essential part to learn and focusing squarely on learning that before adding complexities.
The elaboration effect describes how information is memorable when it is meaningful. Information is associated with other information in the brain. When we can’t remember something, it’s often because that information was not linked to the right things. When learners elaborate they link information to other information, which helps them remember information at the right time. Learning approaches like self-explanation and teaching others support learning in part by linking information to each other in meaningful ways.
The generation effect describes how trying to remember information is a darn good way of remembering it. It’s a lot better to get out a piece of paper and try to recall what you know about a topic than open up the textbook and re-read the chapter on that topic. Not only will you remember more, you’ll also have a better sense of what you don’t know. The effectiveness of generation for remembering information is one reason for frequent low-stakes tests: it’s the test itself that is facilitating learning; not just the studying beforehand.
Learning as practice
In most learning contexts, it’s insufficient to just “know” some information. Knowing how all of the pieces in chess move doesn’t make me any good at chess. I could memorize the moves from a thousand games, but I still wouldn’t be that great at chess because I don’t have any practice playing it. I haven’t had to make the decisions for myself. I haven’t seen the consequences of those decisions. I’ve never played against someone who is responding to my moves in real time. I’ve never felt any pressure to win.
The argument for practice is even more pronounced for learning contexts that require learning body movements. At least if I memorized a thousand chess games I would have some idea about how pieces interact with each other and how the course of the game goes. What if I’m learning basketball? I can visualize basketball players, I can watch basketball, I can memorize basketball stats, but none of these things is going to teach my body how to play basketball. To do that, I actually need to practice basketball.
What learning approaches develop practical skills? One over-arching concept captures the essential elements of good practice: deliberate practice. Basically, deliberate practice consists of:
- Focusing on the hard parts
- Practicing intently
- Receiving quality feedback on your performance
- Reserving time for reflection
- Aiming for improvement when repeating the practice
There are plenty of details, of course. The practice needs to be focused on what you want to get better at, and should include some (but not too much) variety. Play tennis in different courts with slightly different racquets in different weather conditions. Don’t play badminton and expect to get good at tennis. The feedback needs to be about how you can improve. It needs to come at the right time — long enough afterward for the mistake to be evident, soon enough afterward for the mistake to be remembered. It needs to be given in a way that doesn’t threaten the learner’s self-esteem.
Often, learning environments do not provide the kind of feedback that learners can make sense of. If you want to become a good poker player, you will need to play a fair amount of poker. But the feedback you receive is highly variable. You can play well and still lose or play poorly and still win, at least in the short-term. You have to overcome this confounding feedback in order to improve. In some professions, feedback about performance is just not present. Radiologists, for example, don’t usually find out whether their diagnoses were correct — someone else usually determines that. Even environments that provide clear feedback about right or wrong don’t provide information about where things went wrong. If you lost a chess game, you made at least one mistake, but when? An experienced player can diagnose the game and figure out where the probable mistakes were.
Developing basic skills requires something a little less than deliberate practice. Worked examples, which show students the steps of a procedure, is one effective technique. Basic imitation is another: watch the choreographer carefully and copy her moves to learn the dance. In both cases, these approaches work by exposing learners to steps they otherwise might not know or see. A good worked example exposes how an expert performs the procedure in a way that just seeing the expert’s work would not. It also explains why each step is being performed if the student can’t figure it out. Experts skip steps. They don’t have to explain their reasoning to themselves. It’s the same way that a choreographer slows down the steps to a dance and breaks the steps down into manageable bits for the dancers.
Once you move beyond the basics, however, these techniques lose their effectiveness; at that point deliberate practice takes over as the best approach.
Learning as insight
You can tell students about Cartesian coordinates: there are these two axes; we call one the x-axis, we call the other the y-axis; any straight line in the plane has two important attributes that define it — a slope and an intercept, etc. And you can even get them to practice solving slope-intercept problems: isolating the unknown variable and plotting the line or writing an equation in slope-intercept form that matches the line. They’re not going to be able to draw on this knowledge as a resource when needed to solve complex problems if they don’t have some deeper understanding of, say, slope as a rate of change.
All students, for example, enter physics classes with misconceptions derived from our everyday experiences. These misconceptions create profound barriers for learning how physics actually works and lead to misinterpretations of common physics experiences. These naive conceptions never fully go away, even among professional physicists. What insight experiences can do, however, is create productive ways of thinking about concepts and relationships.
What learning approaches facilitate insight? So many: analogies, contrasting cases, hands-on experiences, visualizations, and more. What these techniques share is some way of getting students to think about a concept in a different way.
You can simply memorize the formula that computes the area of a circle. Or a series of visualizations might help explain why the formula works — providing both a memory aid and a deeper understanding of the concept.
You can try to explain the concept of ratio. Or you can engage learners in “feeling” ratio with their bodies, using a Kinect system. Have students raise or lower their hands while maintaining the same ratio between their hand heights (not merely a consistent distance). This is surprisingly challenging.
Or you might present learners with well-chosen contrasting cases that illustrate the underlying concept — and power — of ratio. One tactic asks students to create a measure of “crowded-ness” for images like the ones below, introducing students to the idea of ratio.
As with all learning experiences, the timing matters. Learn the textbook meaning first and an insight learning activity may not produce much insight. Come to any learning experience without the appropriate prior knowledge and its hard to get much out of it.
Often, a single learning experience will involve acquiring information, practicing a skill, and gaining some insight. I didn’t know my pawn could move that way, and that’s what could have won me the game (learning new information to be used in later practice). These recent games have me thinking about chess as controlling space on the board rather just about controlling pieces (practice leading to insight).
Although this information-practice-insight approach does not exhaust all of the things we can mean by learning — we haven’t talked about social behavior, prior knowledge, identity, or motivation, for example — it breaks the word down in a meaningful way. There is a difference between being vague (“some sort of learning happens here”) and being precise, but whole (“this learning experience is about knowing these key terms and seeing these structural features and gaining practice at solving these kinds of problems”).