“Retaining people is important, but retaining customers is more important” – more insights from learning leaders including NIIT’s Edward Trolley in the article, “Out of Recession, Companies Turn to Training” in HRO Today magazine’s April 2014 edition – http://www.hrotoday.com/content/5616/out-recession-companies-turn-training
So we now have a simple model of learning that we can summarize : We have an experience that causes us to recall a previous similar experience, and use it as a guide as to what to expect. In other words:
EXPERIENCE -> RECALL -> EXPECT
Although this model seems to make intuitive sense, it is wrong—or at least, incomplete—in a very important way. To see that, think about what happens when you have a lot of experiences in the same essential situation. For example, you may have been fishing hundreds of times. If so, you will have experienced many different episodes in which you learned important things. So what happens when you go fishing again? Do you remember all of these episodes at once? If so, how do you manage to mentally process all those memories rapidly enough to extract and use the knowledge they contain? This issue has been referred to as the “paradox of the expert”—the notion that an experienced person might have so many relevant memories that she would be paralyzed by the effort to make sense of them all. The label is meant to be tongue-in-cheek, because of course real experts are not paralyzed in this way. The question is, why not?
The answer lies in something that psychologists call a schema. A schema is an organizing structure in memory that collects the set of things you should expect in a particular kind of situation. So for example, if you have been fishing a lot, you will have a fishing schema in memory that amalgamates all of the things you have learned from all of the fishing experiences you have had. For example, you may remember which spots are best to fish from, which kinds of fish you are most likely to catch at different times of year, what kind of bait works best for each kind of fish, and on, along with miscellaneous details such as remembering that you should stay out of the water in the winter months, and that if you leave your bait unguarded a raccoon may steal it. By collecting this knowledge in one place, the schema helps you avoid the paradox of the expert.
The simplest model of how schemas are built is that they are based on the first episode of a particular sort that you experience. So for example, the first time you went fishing, you created a schema for this new experience that basically told you to expect everything that happened this first time to happen every time (this is a little oversimplified since you probably would have understood you first fishing trip in terms of some pre-existing scheme—your schema for hunting, for example—but I’ll ignore this detail). From there, your fishing schema gets refined and amended whenever you learn something new from a fishing trip. This raises the quexstion, how do you know that you are learning something new? The answer is deceptively simple: you know you are learning something new when the expectations provided by your schema fail in the current situation.
For example, suppose that your fishing schema includes the expectation that when there is ice on the river, you can cut a hole in the ice and fish through it, but that on this occasion the ice cracks and you fall through. Your expectation that you can fish through the ice has failed. But simply removing this expectation would be too extreme, because sometimes you can fish through the ice; sometimes you can’t. To create an appropriate revised expectation, you have to explain the why this works sometimes but not others. Then you can revise your expectation—for example, to note that you can fish through the ice as long as it is thick enough to support your weight.
So expectation failure—the failure of your schema to accurately predict what is going to happen—is the trigger for learning. This is not exactly obvious, but if you think about it you can see that there really is no other way it could work. After all, when your schema tells you exactly what to expect in a situation, what is there to learn? It’s analogous to a scientific theory, which, if confirmed by experiment, remains unchanged, but that must be modified when a violation occurs.
This is of learning from expectation failures, by the way, is where the notion of “surprise” comes into the picture. As I noted above, surprise and emotion are the two features that tend to make an episode stick in memory. It is interesting to note that surprise triggers the production of the neurotransmitter dopamine in your brain, and dopamine has the effect of strengthening the active connections between your neurons. This is thought to have the impact of making it more likely that you will remember in the future whatever it is that you are thinking about now. Emotions—especially pleasure and pain—also trigger dopamine production. So there is at least a plausible neurological theory of how surprise and emotion determine what you remember.
By repeatedly experiencing and explaining expectation failures, you create progressively more sophisticated theories over time. Think, for example, about how you learn to get along with a particular person over time. At first, your schema for interacting with them may be more or less a copy of your generic schema for interacting with anyone. But over time various expectations in that schema will inevitably fail. To take a simple example, you may have a supply of jokes that make most people laugh, but you may discover your new acquaintance doesn’t find those jokes funny. From other interactions you may decide also decide that this person is quick-tempered, or sentimental, or iconoclastic, or shy, or any of a hundred other things. These hypotheses allow you to replace very specific expectations—say, that person X doesn’t like your joke about the three Neanderthals that walk into a bar—with a much more general expectation, for example, that person X doesn’t like anything having to do with negative stereotyping.
As I noted above, the theory of how this kind of reasoning works is far from worked out scientifically. The key point to hang onto, though, is that explanatory reasoning is triggered when an expectation fails. I want to make one other quick point about this. You might ask why, if a person knows enough to explain an expectation failure, they wouldn’t have anticipated that failure in the first place. The answer is that expectation failures tell you what to focus on. I’ll give a quick, slightly silly example from a more modern, though still somewhat ancient era: my graduate school career. During this time, I sprained my ankle playing football and ended up on crutches for the first and only time in my life. As you might imagine, there was a lot to learn from this. One thing I learned quickly was about getting sodas. It was my habit, when I got my daily diet coke out of the soda machine in the lab building where my office was housed, to pop the top immediately and take a quick drink. The first time I did that on crutches, I soon realized that now that the soda was open, I was not going to be able to get it back to my desk without spilling it all over. Notice that while I could in principle have figured that out in advance, it was actually pretty unlikely that I would happen to think of it. Once the failure focused my attention, however, it was trivial to understand what was wrong. That’s why expectation failure in general plays such a vital role in learning: expectation failures show you where to focus your thinking in order to improve your understanding.
So we can summarize our theory so far in something like this way: Learning is based on the ability to remember our experiences and recall them in similar circumstances. When an experience is repeated, our mind creates a schema to represent what we can expect in the typical instance of that experience. We use the expectations from this schema to guide our subsequent behavior. When an expectation fails, we seek an explanation, and based on that explanation we revise the expectations in our schema. Through the action of this process over time, our schema become more accurate and more detailed, and we will become better able to function in the environment that it describes. We might summarize this schematically as follows:
EXPERIENCE -> RECALL -> EXPECT -> FAIL -> FIX
(Where “fix” means explain and then revise the schema.)
The last point I want to make about learning is that in general, people seem to learn much more from doing things themselves than from watching others do them. As it stands, the model I’ve outlined here doesn’t do much to explain that difference. After all, you can form expectations and revise them in response to failures whether or not you are involved in whatever activity is going on. I think there are three big reasons why you learn better by doing:
- First, when you do something and the expected result doesn’t occur, that almost by definition means that one of your goals is thwarted. That, in turn, as I discussed above, means that you are more likely to learn and remember the lesson that when your goals are unaffected.
- Second, when you have to make a decision, you attend to a lot of things you might not attend to when you aren’t involved. For example, you can watch people fish all day without really paying attention to when they cast their lines, how far they cast them, how they use their equipment, and so on. When you are forced to make decisions, you are forced to notice a lot of things that you wouldn’t have noticed otherwise, and thus you have a much richer memory of the experience
- Third, to make a decision, you have to settle on a theory of what you expect to happen, even if it is only a wild guess. When you are just observing, you don’t have to have any theory at all. So, for example, when you watch someone fish you don’t need to think about how hard they should pull on the line once they get a strike. But when you are fishing, you are forced to make some kind of guess about this, and your guess will be either confirmed or refuted by what happens next. In other words, your guess becomes an expectation, which, even if wildly off base, can start you on the cycle of expectation failure and explanation that will lead you to a better theory.
Taking all this into account, we are most likely to learn effectively when we make a decision in service of a goal, which forces us to adopt expectations, which can then be revised through experience and failure as described above. We might render this as:
GOAL -> EXPERIENCE -> DECIDE -> FAIL -> FIX
In other words, we start with a goal we are trying to achieve, we experience an episode in which we are trying to achieve that goal, we are forced to make a decision about how to address the goal, and that decisions is based on expectations that may fail and require fixing. Of course, this model is really a cycle–we would go around the cycle numerous times in any complex situation, pursuing multiple goals, making multiple decisions, and so on.
So that is my answer to the question “how does learning work.”
There are a number of things to say about this model, but probably the most important is that in this model we learn by interacting with the world and refining out expectations about it, especially in the course of practicing the skills we need to learn. This model looks nothing like a lot of what goes on in a typical classroom—there is no room in it for lectures or for rote learning. The appropriate role for a teacher based on this would be, first, to ensure that this sort of experience occurs, and, second, to be the coach when an expectation failure occurs.
Although this is a very simple model, it tells us a surprising amount about how an effective instructional design should look. If you believe that that learning works this way, even approximately, then the goal of training should be to drive learners through this cycles as efficiently as possible. To do this, we must:
- Give the learner a motivating mission (the goal).
- Put the learner in an appropriate context where he or she can act to achieve that goal in a realistic environment.
- Force a challenging decision that may lead to an expectation failure, then help the learner fix the expectation by explaining the failure.
- There are then two key things we can do to help the learner explain a failure:
- One is to show the consequences of the failure, which allows the learner to get feedback on what went wrong as they would in a natural environment.
- The other is to provide coaching, which helps the learner to understand expert theories and models that can help explain what is going on.
We can summarize the instructional design model that best fits our model of how learning works thusly:
MISSION -> CONTEXT -> CHALLENGE -> CONSEQUENCES & COACHING
If you agree with this model of how learning works, then this is the kind of training you should want to design. If you don’t agree… then it’s time to get to work on your own theory.
Learning is not preparation for life; learning is life. — John Dewey
Making correct predictions in pursuit of a goal is a pretty good definition of “intelligence”. — Steven Pinker
Mistakes are, after all, the foundations of truth, and if a man does not know what a thing is, it is at least an increase in knowledge if he knows what it is not.— Carl Jung
Give me a fruitful error any time, full of seeds, bursting with its own corrections. You can keep your sterile truth for yourself. — Vilfredo Pareto
That which does not kill us makes us stronger. — Friedrich Nietzsche
I have a radical opinion about instructional design: I think it should start from a clear understanding—or at least, a clear theory—of how people learn. This didn’t seem radical to me when I first migrated from my original field of artificial intelligence into training and education, but I quickly discovered that when I nattered on about how learning works in instructional design sessions, people tended to look at me as though I had gone off on a tangent about metaphysics or something. The topic of how people learn just doesn’t come up much in mainstream instructional design, and when it does come up, it is usually indirectly, through rules of thumb like “people learn better when you help them relate your lesson to their experience.”
The main reason, I think, is that instructional design is often seen as a kind of trade, almost like, say, plumbing. No one expects plumbers to be whizzes at hydrostatics. Similarly, a lot of instructional designers seemingly prefer to be left alone to get on with their work without having to be responsible for messy theories of how the brain works.
The problem is that learning is not a solved problem in the sense that building construction is. Following standard practices in the construction industry gets you a building that works; following standard practices in instructional design often gets you a course that doesn’t. To do better, I believe that instructional designers need to develop a better understanding of how learning actually happens, and use this as a guide to produce better designs.
So in this column I want to do my part to to try to make that happen by giving my answer to the question, how do people learn?
I want to begin by considering an example. Because I think discussions like this often go wrong from the start due to a modern tendency to see learning as something that happens in a classroom—and also just for fun–I’ve decided to set my example in a time before formal education. So, for the sake of argument, imagine that you are a homo erectus living in the Indus river valley in, oh, say, about 500,000 BC. Let’s say that you are walking home from the fishing hole in the twilight when suddenly, in a blur of motion, a tiger leaps from the brush directly into your path.
There’s of course a good chance you won’t survive this encounter. In this case the removal of your possibly maladapted genetic strain from the gene pool may help your species adapt through natural selection. It’s cold comfort to you, but over time, as tigers take their toll, the surviving population will come to feature traits that are better adapted to a tiger-rich environment—being able to run fast, say, or being smart enough to avoid tiger-rich places and times.
But let’s be optimistic, and assume you escape. In this case, there is better news: rather than sacrificing yourself to help your species adapt, you will instead get the chance to adapt as an individual. You can’t suddenly get faster, of course, or grow claws or wings, or anything like that. But you will change your behavior. For example, it is more than likely that you won’t dawdle at the fishing hole in the evening any more. You may choose a different path home as well. And so on. This is learning.
The key to learning lies in your ability to remember your experience. You will remember the tiger episode for the rest of your life, and recall it at times when it may help you to make better decisions, like, perhaps, the next time the sun starts going down when you are at the fishing hole. As far as we know, if your brain is healthy, it records every episode that you experience, but only a fraction of those episodes seem to be remembered for the long term. A complete theory of what causes a particular episode to be retained in memory doesn’t exist, but two factors are known to have a big impact: surprise and emotion. Clearly, the tiger episode has plenty of both, which is why we can be certain that you will remember it.
I’ll return to the issue of why surprise matters in a bit, but for now let’s think about why emotion would matter. The best answer seems to be that the things that evoke emotions tend to be things that effect, or threaten to effect, our goals, either positively or negatively. Encountering a tiger generates a lot of emotion because it threatens the goal of self-preservation. It makes sense to remember episodes in which one of your goals was threatened, because those memories can help you identify situations you want to avoid in the future. Likewise, it makes sense to remember episodes in which a goal was furthered—say, for example, a time when you tried a new fishing spot and caught an unusual number of fish—because those memories help you identify situations that you want to recreate.
Now let’s think in more detail about what happens when you remember a past episode. First of all, it is important to note that due to a remarkable property of your memory, you typically recall past situations that are very similar to the one in which you presently find yourself. This matters because—at the risk of stating a tautology—you can expect similar things to happen in similar situations. If you go fishing, it makes sense to expect that it will be like the last time you went fishing; if you are headed down a particular trail it makes sense to expect it to be like the last time you walked down that trail; if you are talking to a particular person it makes sense to expect it to be like the last time you talked to that person; and so on. The notion that you are constantly recalling the last time you did something similar to what you are doing now seems obvious, but when you think about it, it is quite amazing that you are able to do this. Your memory for events is like a version of Google that actually works.
If you observe very young children you’ll notice that they apply expectations from past episodes in a very simple and literal way—they basically expect their second experience in a particular situation to be just like the first. This can create an occupational hazard for parents. When my daughter was young, for example, I used to cringe whenever someone gave her a treat—at the dentists office, say, or at an event at a store or restaurant—because I knew she would expect the same treat the next time, and be unhappy when it wasn’t forthcoming.
As people get older, however, they get more sophisticated about teasing apart the elements of a situation and hypothesizing their individual causes, and this complicates the picture somewhat. For example, if the last time you went fishing it was cold and there was ice on the river, you may be able to reason out that because it is warm this time, you should not expect ice. How exactly people do this sort of reasoning is a big open question—it’s the sort of thing I worked on when I was in artificial intelligence—but this shouldn’t obscure the essential fact that you remember past episodes and extract from them expectations about the present.
Expectations are useful because anticipating something that might happen allows you to take action to ensure that that thing either does or does not actually happen. If you remember that the last time you walked down a particular trail you slipped on a mossy rock, for example, that allows you to step more carefully this time, while if you remember that the last time you went picking strawberries you found some particularly juicy ones in one corner of the patch, you can be sure to go straight to that place the next time. This takes us back to where we started, with encounters with tigers being one of the more obvious sorts of things you might want to avoid.
To be continued…
My daughter started taking piano lesson right around her fourth birthday, which is much younger than anyone would have considered reasonable when I was a kid. Today, though, it is not uncommon, and this change is largely due to one man: Shin’ichi Suzuki, a Japanese who, rather affectingly, according to Wikipedia, “desired to bring some beauty to the lives of children in his country after the devastation of World War II.” Suzuki arrived at the notion that he could teach four-year-olds to play the violin by reasoning that if they are smart enough to learn language, they are smart enough to learn music. He was right, and he proved it when he stunned the world by producing a corps of pre-school violin virtuosos.
Today, if you take a young child for music training almost anywhere in the world, she will probably be trained through some version of the Suzuki Method. The central tenet of this approach is that kids learn to play by ear. In traditional music lessons, kids start by learning the formal machinery of musical notation along with “music theory–as embodied in learning scales and so on—which is a tedious process that seems far removed from what is attractive about music in the first place. This approach puts an enormous obstacle in front of the learner right at the beginning of her education. It’s no wonder that younger children are generally unable to learn this way—even many older kids who signed up for traditional lessons never got past the theory.
In the Suzuki approach, students learn to play songs that they can enjoy from the start, rather than waiting until they have mastered the written notation. They learn to compare what they play to the way the song sounds on a recording, or their memory of it. In other words, they are put in a position to observe the outcomes of their actions directly, and to learn from this.
There has been a little bit of backlash against the Suzuki approach due to the claim that kids who learn this way may struggle to learn musical notation later on, and so many kids, my daughter included, are now taught through a “modified” Suzuki method in which notion is introduced early even as the student is learning to play songs by ear. This approach starts learners out with an extremely simplified notation that they use as a mnemonic device while playing, and gradually adds complexity to the notation over time. The progression goes something like this:
1. Initially, the student plays with her hand in a fixed position on the piano while looking at a series of numbers telling her which fingers to use in what sequence (1 to 5 corresponding to the numbers on each hand from thumb to pinkie).
2. The numbers are then juxtaposed with whole-note symbols that move up and down on the page vertically to indicate whether the notes in the song are going up or down.
3. Rests, half notes and whole notes are introduced in turn, as the students learn pieces that have different rhythms.
4. The notes begin to appear on a traditional staff, but still with the familiar finger numbers next to them.
5. Numbers are only given for notes at the start of phrases.
6. The numbers go away.
There are two things about this approach that I think are clever. The first is that the notation is made useful. Young students need help remembering the pieces they are meant to play, and the notation provides this. As a result, the student sees the notation as an aid, rather than as something difficult that must be learned. Traditional music lessons, like a lot of old-fashioned education, push students to learn a lot of things in advance of being able to put them to use, and the lesson here is that it is better to wait until the utility is manifest.
The second, related point, which is somewhat more subtle, is that nothing is introduced in the notation that the student has not first learned by ear. For example, the student learns to hear and play half notes before they turn up in the notation, so that when the new notation is introduced, it relates to something they already understand. This seems to make all the difference in comprehension and ability to put theory into practice. It may be the case that, in effect, Self 2 can benefit from some technique, but only when that technique relates directly to something Self 2 has already learned through practice.
When she was younger, my daughter took gymnastics lessons at a training center in southern California that is, strangely enough, one of the best educational institutions I’ve ever seen. (Sadly, we’ve moved away from the area, or she would probably still be taking lessons there.) Some of the reasons why are identical with what I’ve said above: students spend most of their time performing skills, instructors keep discussions of technique to a minimum, and so on, with all of the same beneficial results. But there are a couple of other things about it that I think are worth noting as well.
The first is that in gymnastics, form is a major issue, and the instructor is generally the only one with the expertise on hand to judge form. This means that the instructor has to provide the feedback as well as the coaching, and in general when that is the case there is an enormous risk of mixing the two roles up. For example, if the coach wants to build a child’s confidence, there is a real temptation to tell her she executed a skill well even if she didn’t. What I find really interesting is that the instructors at my daughter’s gymnastics center never do that. Instead, they invariably give the kids straight feedback. That takes a lot of discipline, but the net result is that the kids, who can tell real feedback from fake at a very early age–are extremely motivated by the praise they get, because they know they earned it. To be a sympathetic coach while at the same time providing objective feedback is a serious skill that the instructors in my daughter’s gymnastics program somehow seemed to have gotten exactly right.
My other point is that gymnastics is notable because it consists of complex skills that seem to have an “all or nothing” quality to them. If you attempt a back flip, for example, you’d better complete it, or you risk breaking your neck. This makes it problematic to learn how to do such maneuvers in the first place. It turns out that the instructors have a lot of tricks up their sleeves to allow kids to practice partial or simplified versions of complex skills. These include the use of trampolines, ultra-bouncy floors, foam-rubber-filled pits, reduced-size apparatus, and so on. My personal favorite is their collection of large vinyl-covered foam rubber pillows, with which the instructors are especially ingenious. For example, if you want to learn to do a back somersault, you start by putting your hands over your head and bending backwards over a large, hexagonally shaped pillow, which your instructor rolls slightly at exactly the right moment to help you over. Vygotsky called such devices scaffolding—learning aids that allow students to attempt a skill that is otherwise beyond their ability. I think that is a pretty useful term. A lot of detailed thought that has gone into providing such scaffolding for gymnastics, and that has a lot to do with why that training is so successful. The reason this is so important is that it allows the gymnastics instructors, like the tennis and piano instructors I’ve talked about, to get learners right into performing real skills.
Some of the principles that can be abstracted out of the lessons we looked at are:
1. Be ambitious about what can be learned. Suzuki thought four year olds could play an instrument well. Gallwey thought beginners could hit solid tennis shots in their first lesson. By rejecting conventional wisdom about the limits of what could be done through training, they both made huge advances.
2. Allow students to learn by doing. All of my daughter’s lessons are centered on having the learner practice the skills in question. Everything else that happens, including coaching and presentation of information, is designed to support the learner’s activity.
3. De-emphasize “success” and “failure”. When learners get their egos too involved, they get in their own way. Powerful natural learning mechanisms are thwarted when the learner is obsessed with instant success, or angry about apparent failure. In the old-school sports training that I got as a kid, coaches made success and failure as personal as possible, deriding those who failed and lauding those who succeeded. This approach is hugely destructive to learning.
4. Avoid overloading learners with technique. This distracts them from the fundamental mechanism of learning, which is trial and error.
5. Help learners focus on one or two key feedback areas at a time. When learners can practice with an intense focus on the results that matter, their natural learning mechanisms ensure steady improvement.
6. Deliver simple coaching inputs just when learners are ready to implement them, and not before. This approach guards against overloading learners with technique.
7. Be skeptical of the idea that people have to master “fundamental skills” before moving on. Suzuki realized that the traditional insistence on teaching musical notation before anything else had created a roadblock for learners. By eliminating this roadblock, he revolutionized the training.
8. Break down skills and provide scaffolding for learners. Many important skills are too complex to learn all at once, yet not easy to break into component parts. Overcoming this requires a lot of detailed effort, but the result it worth it.
Out of all this, a general picture of a modern, effective lesson emerges. It is hands on and fast moving, with minimal presentation of information, clever preparation of scaffolding, a deliberate de-emphasize of ego, an intense (but not emotionally loaded) emphasis on observing outcomes, and a central role for highly skilled coaching. This, I think—and hope–is what the future of education looks like.
Is there a common thread that has caused instruction in so many different fields to converge on a similar, highly effective approach? My guess is that it is mostly just the economics of the marketplace. Lessons like these are relatively expensive luxuries—no one has to take them.
Furthermore, as econpomists have observed, many modern parents have, in broad historical terms, lots of money and leisure time but few offspring, which puts them in a position to be obsessive with regard to almost everything about their children’s upbringing. In that situation, the approaches that work, whatever they might be, will rapidly win out in the free market.
So I think that is the bottom line. The approach to education that we are seeing emergent in my daughter’s lessons is what works.
Knowledge is not skill. Knowledge plus ten thousand repetitions is skill–Shin’ichi Suzuki
“Our knowing is ordinarily tacit, implicit in our patterns of action ….. It seems right to say that our knowing is in our action.”–Donald Schon
“What we pay attention to and how we pay attention determine the content and quality of life.” –Mihaly Csikszentmihalyi
“The greatest efforts in sports come when the mind is as still as a glass lake.”–Timothy Gallwey
There’s been a lot of criticism in the media lately of parents who “over schedule” their children by enrolling them in too many structured activities. My six year old daughter takes lessons in gymnastics, piano, ballet, swimming and tennis, so as you can imagine I’m a little sensitive about this. My only excuse for my overzealous parenting is that herlessons are so good that I would hate to take her out of any of them.
I disliked virtually every organized educational experience I had before graduate school, so I’m actually pretty surprised about the seeming abundance of high-quality training for kids these days. Apparently there has been some kind of revolution in extra-scholastic education since I grew up, and no one bothered to tell me about it. In this column I try to figure out why my daughters lessons are so good, and what we as instructional designers can learn from that.
My first tennis lesson consisted of a long lecture about “eastern” versus “western” grips—a distinction that escapes me to this day—plus a bunch of weird drills like rolling a ball around in circles on the face of the racquet for ten minutes straight. Actual tennis did not occur until several lessons later. I’m still bitter about that, in case you can’t tell.
In contrast, my daughter got to start hitting tennis balls, which were bounced to her by an instructor, ten minutes after the start of her first session of her beginning tennis camp. Throughout the camp, she and her fellow students spent most of their time with racquets in hand, hitting real tennis strokes. Instructors kept them focused on simple outcomes, while being careful to avoid ratcheting up the pressure. Results were dealt with dispassionately—data points to be considered, not indicators of individual success and failure. There was no detailed presentation of theory about stance, grip, swing, and so on–instruction was brief and to the point, and coaching inputs were confined mostly to specific, individualized suggestions that learners could put into practice immediately, and that slowly built on one another over time. By the end of the camp, which was about four hours total, most of the kids could hit a variety of surprisingly solid shots including ground strokes, serves and volleys. My daughter emerged feeling confident about the skills she’d learned. “I think I’m going to be better than you at tennis,” she confided to me in a whispered aside. I think so too, and I couldn’t be happier about it.
I think the improvement in how tennis lessons are taught is to a significant extent due to the influence of one man: Timothy Gallwey, a tennis playing Harvard graduate who in 1974 wrote a book called The Inner Game of Tennis.
Gallwey describes the travails of the tennis novice as being the result of a struggle between two inner selves: Self 1—“the teller”—and Self 2—“the doer”. Self 2 learns physical skills through trial and error, building competence reliably, gradually and unconsciously. Self 1, on the other hand, is good with theory, but not with execution. Skills like tennis are the natural province of Self 2, but Self 1 has a bad habit of losing confidence in Self 2, and trying to take over. This usually results in a significant degradation of performance. Learners in the grip of their Self 1, Gallwey observes, tend to be stiff and awkward, attempting through sheer force of will to make their bodies do the right thing. Learners being guided by their Self 2, in contrast, find that successful execution seems to come naturally, almost without their having to make any conscious effort.
Gallwey’s argument is presumably meant to be taken as a metaphor, but it may be closer to the literal truth than anyone would have thought. Daniel Willingham, a Psychologist at the University of Virginia, has done a series of experiments showing that subjects playing a simple video game can learn to improve their performance in two different ways: by being explicitly taught the underlying patterns that control the game, or by implicitly learning those patterns through practice. What is interesting is that these two modes of learning seem quite separate and distinct, as evidenced by the fact that subjects who learned implicitly often cannot describe the patterns to which they are clearly responding. What is even more interesting is that the brain activity of subjects who are playing the game using implicit knowledge is different in location and character from the brain activity of subjects playing the game using explicit knowledge. In other words, Gallwey’s Self 1 and Self 2 may turn out to describe real components of the brain. Of course, a careful scientist would feel compelled to attach a long list of caveats to a statement like that, but I prefer, for the moment, to just enjoy the crazy long-shot possibility that a pop-psych metaphor might actually turn out to be an accurate psycho-physiological theory.
Gallwey’s general prescription for success at learning tennis is all about learning how to “quiet” Self 1 and “trust” Self 2. Three key things need to be done, according to Gallwey, in order to accomplish this goal.
The first requirement is to de-emphasize technique, which is the verbal description of how to perform a skill. Technique is the province of Self 1. In a traditional tennis lesson, the learner is loaded up with technique right from the start: Keep your wrist locked! Keep your elbow straight! Turn sideways to the ball! Get your racquet back early! Keep your eye on the ball! Transfer your weight as you swing! Swing low to high! Follow through! Self 1, in possession of all of this theory, wants to put it into practice immediately. Self 2, however, will go through a gradual process of learning from experience with or without the theory. So Self 1 gets impatient and tries to take over from Self 2, and that’s where the trouble starts. Gallwey’s conclusion is that the less Self 1 knows, the less likely this is to happen.
The second requirement is to de-emphasize ego. The involvement of the ego creates pressure. We’ve all experienced this: When you become focused on the need to succeed–especially when others are watching you and you realize that failure would be embarrassing–you suddenly begin to feel as though even the simplest, most familiar actions have become very difficult to execute. In sports, this effect is called “choking”. According to Willingham, choking is what happens when people lose faith in their unconscious abilities and revert to their conscious, explicit mode of control–in other words, when Self 1 takes over from Self 2. Gallwey’s advice to the learner is to cultivate a Zen-like detachment from the ego (“letting go of the self” and so on).But not everyone is able to suppress their own ego in this way. What is of more practical import, in my opinion, is Gallwey’s prescription for the coach, which is to avoid expressing too much focus on success and failure. This is important because it is more or less the opposite of traditional coaching wisdom.
The third requirement is to pay attention to the outcomes of actions. Most people think they do this already, but for the most part they are wrong. One of Gallwey’s most interesting observations is that novices often fail to observe what happens when they hit a bad shot, apparently because it is too painful to watch. Sometimes they literally cover their eyes when they miss. The trouble with this is that the feedback from where that shot lands is critical to Self 2’s gradual, feedback-based learning process. This is where a coach can help, setting up exercises in such a way that learners are highly focused on seeing outcomes clearly regardless of whether they are “good” or not.
So that is Gallwey’s theory, or at least my slightly fractured version of it. The real question is, does it actually work? Well, it happens that Harry Reasoner did a sixty minutes piece many, many years ago that makes a pretty good case for the effectiveness of Gallwey’s approach. (If you look on youtube, you may be able to find the video—if so it’s well worth watching.) Reasoner actually recruited people who had never played sports to come and take a lesson from Gallwey, which he filmed.
In the video, a student, who is holding a racquet in her hands for the first time, stands to the side as Gallwey and a partner hit the ball back and forth. Gallwey instructs the student to say the word “bounce” whenever the ball bounces, and “hit” whenever the partner hits it. When she has done this for some time, Gallwey kicks the partner off the court and puts the student in his spot. Gallwey continues to hit balls with the same location and pace, and instructs the student to keep saying “bounce-hit, bounce-hit”, with the word “hit” now being uttered when the partner would have hit the ball. A few more balls go by, and Gallwey then suggests that, if the student feels like it, she can try swinging her racquet, but he cautions her to keep focused on maintaining an accurate “bounce-hit” cadence—what happens when she swings is not important. The student does indeed swing the racquet, and, having by now internalized the precise timing of the ball’s arrival, she is able contact it squarely on the first try. When you see this on the video, it’s utterly amazing.
When the student has begun hitting the ball reliably for a while, Gallwey starts giving her tasks to do that involve focusing attention on the ball. One is to describe the sound the ball makes when it hits her raquet. Another–not captured in the video–is to ask the learners to call out fhow far each shot lands from the base line of the court.Gallwey assures the learner that it doesn’t matter where the shots land—he just wants an accurate distance estimate. But an interesting thing happens: the shots start landing closer and closer to the baseline, without the learner making any conscious effort to get them closer.
I’ve seen this video several times and I’m always amazed by it. Even knowing what I know about learning, I’m still surprised to see that Gallwey’s technique works as well as it does.
I believe that Gallwey’s key points—to de-emphasize ego and technique, and to emphasize focus on outcomes—have been incorporated to a significant extent into modern tennis lessons, and that they are the central reasons why these lessons are much more effective than the ones I took.