Science Lessons: Beyond the “Effective School” Myths

Lewis Perelman

Going to school is clearly one of the most broadly shared of American social experiences. It powerfully frames the public’s idea of what learning environments should look like -- whether in a seventh-grade classroom, a college lecture hall, an adult literacy course, or a corporate training seminar. As a result, the design and practices of our childhood schoolrooms tend to be reproduced in most education and training settings, even those that aspire to be nontraditional or “radically” innovative. Despite decades of experience with models, demonstrations, and experimental programs, the “New American School” persistently gravitates back to our familiar models of school, classroom, and teaching. In reality, these widely shared stereotypes of effective schools routinely and profoundly violate what scientists have come to know about how people learn most effectively, and the conditions under which people apply their knowledge best in new situations. A powerful knowledge base called “cognitive science” provides the evidence. “Cognitive” refers to perceiving and knowing, and cognitive scientists work to understand how we think, remember, understand, and learn. Their research is very diverse. They observe children learning mathematics or experienced workers handling on-the-job knowledge and judgment demands. They program computers to do complex problem solving or to simulate intelligent behavior. They analyze the very nature of meaning.

In recent years, cognitive research findings have challenged much of the conventional wisdom about how teaching and learning work. Distilled, this research contradicts a number of popular myths that, at great cost, continue to shape the bulk of education and training efforts not only in America but around the world.

Myth 1: People learn best in school.

In reality, the vast majority and most productive share of human learning takes place in real-world settings outside of schools. Moreover the traditional design and practices of even “excellent” schools are either divorced from or contradictory to the natural learning abilities most people are born with.

Cognitive research shows that learning in context is essential to acquiring knowledge and skills that are truly useful to working and living. Context turns out to be critical for understanding and, thus, for learning. The importance of context lies in the meaning that it gives to learning through the workings of the human’s natural learning system. Human beings -- even the small child -- are quintessentially sense-making, problem-solving animals. As a species, we wonder, we are curious, we want to understand.[128]

Context is evidently critical in using and understanding language. Most of us readily can recognize that words have meaning in relation to the context in which they are appear. Consider, for instance, the use of the word saw in “Jim saw the wood” and “Saw the wood, Jim.”

Cognitive scientists believe that all knowledge, like language, is inseparably linked to its context. Ideas, concepts, or skills are tools whose meaningcan be understood only in relation to the circumstances in which they are used.[129] If you are from an isolated society that has no experience with a “cork” or a “bottle” (much less wine), you probably won’t know how to use a corkscrew, or even what it is. In the context of Chinese culture, nitrate explosives were prized as entertaining “fireworks” -- in the European context the same technology was appreciated more as “gunpowder.”

The human brain is designed and developed to learn through experience. And experience has no meaning except in relation to some context. For instance, we are taught in driving school that a red light means “stop.” After years of driving experience, stopping when you see a red light becomes a reflexive habit. But we go about our business every day surrounded by all kinds of red lights -- in a store window, on a Christmas tree, in a TV picture, on a toaster, and so forth -- without suddenly stopping dead in our tracks. We’ve learned to stop at a red light only in the proper context: driving a car down the street facing a special kind of red light in a device like a traffic light or taillight that is designed to be a signal.

This learning in context doesn’t take any great genius. A pigeon or even a roundworm -- whose whole nervous system has just a couple of hundred cells -- learns the same way. That’s what learning is.

The inventions of spoken language to represent experience and then of abstract, written symbols to represent words and numbers enabled humans to learn from experiences that were remote in space or time. The impact of these innovations on human society of course was explosive. But learning through the transmission of abstract, printed symbols works in direct proportion to the degree that the symbolic representations hook up to real human experience. The more disconnected symbolic communication becomes from the context of authentic, personal human experience -- your own experience, not someone else’s long ago and far away that you’ve never seen or felt -- then the more meager learning becomes until it degrades to no more than mere memorization or, at worst, total confusion.

Yet education generally just wants us to be “right.” Typically, academia evaluates the success of school learning by counting the number of correct responses on tests. The public has been misled to believe that answering a predetermined number of questions correctly is the scientific way to measure learning achievement. In fact, an entire industry is now in the business of making these tests more sophisticated and “elegant.”

Unfortunately, as long as learning continues to be mismeasured by tests that mandate “right” and “wrong” facts for answers, educators are prompted -- almost forced -- to break complex tasks and ideas out of the productive context in which those elements are applied. The seamless fabric of knowledge instead is shredded into school subjects that can be “taught” and “learned” separately. But cognitive science knows that people who are forced to learn disconnected subroutines, items, and subskills wind up losing comprehension of the bigger picture, the natural context that gives their actions meaning.[130]

Real-life experiences, and therefore knowledge, do not come chopped up in discrete subjects but are invariably “interdisciplinary.” Disciplines such as chemistry, economics, calculus, finance, Spanish literature, fine arts, and American history are arbitrary domains defined for the convenience of researchers, academic administrators, or would-be specialists. Dennis Meadows, head of the Institute for Policy Studies at the University of New Hampshire, has pointed out that we would think it bizarre for a geographer to specialize in knowing everything about the surface of the earth between eight hundred and nine hundred feet above sea level and nothing else -- but that’s pretty much the way the academy attempts to organize and transmit knowledge.

This is not to deny that learners must perform simple “subtask” operations from time to time in school and in life. Indeed, studies of apprentices show that novices start with simple tasks. Nonetheless, they perform these simple tasks in context. By observing how the master executes different but related subskills to achieve the end process or product, apprentices develop a conceptual map of the target task, an advanced “organizer” that helps when they first attempt to execute the complex skill themselves.[131]

For instance, Zinacanteco Indian girls in Mexico are introduced to weaving at an early age by first simply observing their mothers, then later helping with basic tasks such as boiling yarn and dyeing wool. With considerable maternal guidance, the girls begin to do their own weaving around age eight, and within three or four years they develop enough skill to be able to weave almost independently.[132]

Context-connected learning should not be confused with simple notions of practical or applied instruction as in the idea of “vocational” education. A relevant context for learning comes out of specific, real-life situations of the learner; vocational curricula often fall short by attempting to transmit skills for use in specific situations which may or may not be relevant to the learner’s real-life experience.

There are examples of educational institutions that have adapted their practices to the necessity of learning in context. At McMaster University School of Medicine in Ontario, students start their medical studies immediately with clinical problems, meeting regularly with each other and a resource person in tutorial groups. Tutorials are organized around major biomedical problems that cannot be solved unless students understand physical, biological and behavioral principles and know how to collect data and evaluate evidence. The students bear the responsibility to determine, with faculty help, what they need to know and then to learn it.

The medical school in Maastricht, in the Netherlands -- where the government assigns students to medical school by lottery -- uses a similar approach. Despite having had no formal instruction, Maastricht students on average score higher on tests of anatomy than do residents trained in the Netherlands’s traditional lecture-approach medical schools. After seven years in school, 88 percent of the Maastricht school’s 1974 class had received diplomas, versus 21 percent of students in other schools.[133]

These examples also suggest, however, the limitation of conventional schools and colleges to adopt authentic-context learning as their standard practice. Medical and other vocational schools have a specific, real-world context of professional practice to which they can justifiably connect the total learning experience they offer.

But general education institutions serving general populations face the daunting mission of connecting learning to virtually the total spectrum of human motives, experiences, and real-life contexts. That only can be done by customizing instructional services to the needs and goals of each individual learner. HL technology now can provide that degree of fine-tuning, and can bring the sight, sound, and even physical experience of real-world contexts to the learner. The traditional academic technology of classroom, textbook, and teacher cannot.

Myth 2: School is preparation for working in the real world.

The point of going to school, we have been told, is to prepare students for effective and responsible functioning outside of school, especially in the workplace. We assume that students will automatically transfer and apply the concepts, skills, and knowledge they acquire in the classroom to other life and work situations.

Cognitive researchers, however, have found that the conditions in which knowledge transfers are neither obvious nor common. Researchers do know that people routinely apply basic skills -- the “three Rs” -- to new situations with some success. But extensive cognitive research, spanning decades, keeps finding circumstances in which people don’t predictably apply knowledge learned in one situation to another. Three situations where it seems knowledge should transfer -- but doesn’t -- stand out.

From school to life. Educators and reformers routinely praise our traditional school curriculum because the knowledge and concepts it teaches are general. In their view, general concepts and knowledge are valuable because students can use them in a wide variety of situations beyond the classroom. But are the knowledge, skills, and strategies acquired in formal education in fact being used appropriately in everyday practice?

The answer, according to the best research addressing this question, is no. For instance, researchers have found that when college students who have mastered solving “book” problems in physics courses are asked to analyze ordinary phenomena, the students revert to intuitive explanations that violate Newton’s laws. In one study, 70 percent of the engineering students who had taken a mechanics course incorrectly claimed that a coin, after being flipped up in the air, was still acted on by some “upward force” -- in fact, only the force of gravity affects the coin after it is released. Another study found that nearly half the students who studied physics argued that an object expelled from a curved tube would continue moving in a curved path -- rather than in a straight line, as Newton’s law of inertia dictates. Significantly, the students in these studies get high scores on standardized tests and honor grades in physics courses. They simply have not made much connection between academic knowledge and the real world.[134]

Likewise, studies of expert radiologists, electronic troubleshooters, and lawyers all reveal a surprising lack of transfer of theoretical principles, processes, or skills learned in school to professional practice.[135] Ask your own physician or attorney whether the courses they took in medical or law school have much use in the work they do every day and the chances are very high they will tell you: not much.

From life to school. Psychologists have recognized for a long time that intelligence is built out of interaction with the environment. Indeed, all human waking experience (and maybe even some part of sleeping experience) is directly connected to the natural process of learning. Experience from which a person learns or remembers nothing arguably can be said not to be “experienced” at all.

People learn outside of school all the time, but once they walk in the school doors, what do people do with what they learn in life outside the classroom? Does sound, everyday practice get applied to school learning?

Once again, scientific research tells us no. In a well-known research study, a middle school teacher working with students who had failed in mainstream classrooms discovered that one of his students had a regular job scoring for a bowling league. The work demanded fast, accurate, complicated arithmetic. Other students had similar arithmetic success calculating their paper route change or shopping mall purchases.

But after lecturing the students on how smart they were in practice, and devising problem sets from which the students could choose bowling score, paper route, or shoe purchase problems to solve, the teacher reported a startling result: The students seemed incapable of arriving at correct answers when their real-world expertise was tested through classroom paper exercises. “Kids immediately rushed me yelling ‘Is this right?’ ‘I don’t know how to do it!’ ‘What’s the answer?’ ‘This ain’t right, is it?’ and ‘What’s my grade?’” teacher James Herndon reported. “The brilliant league scorer couldn’t decide whether two strikes and a third frame of eight amounted to eighteen or twenty-eight or whether it was one hundred and eight and a half.”[136]

From school subject to school subject. Do the skills, strategies, concepts and other knowledge learned in one school subject at least transfer across the traditional curriculum into other school learning?

Yet again, researchers conclude with a no. Not so long ago, it was still common practice for students to study Latin and mathematics not only for the subjects’ utility, but for the “mental discipline” and logical constructs that were presumed to be useful in learning other things. Yet at the turn of the century psychologist Edward Thorndike conducted many studies of the transfer of such learning from one school subject to another, and his “negative findings ... devastated the discipline hypothesis,” as psychologist Roy Pea recalls.[137] More recent research shows that teaching children to use general thinking and learning strategies produces no clear benefits outside the specific subject areas in which they are taught.[138]

The fact that knowledge does not transfer in these situations means that simply increasing the amount of knowledge relayed in our schools will not enhance students’ ability to apply knowledge beneficially in real-life situations. While more needs to be learned about how knowledge and skill can be transferred to practice, the surest way to achieve that objective is to reduce the need for “transfer” by bringing learning as close to the real context of practice as possible.

Indeed, “embedded training” and the developing designs for “just-in-time” (JIT) learning are using advanced HL technologies such as simulation and virtual reality to erase the boundary between learning and doing altogether.

The irresistible allure of simulations for learning lies in their ability to provide experiences that would be too dangerous, costly, or just plain impossible to get any other way. For example, at the Federal Aviation Administration Academy in Oklahoma City, student airport tower controllers are learning their jobs in a Logicon Tower Operator Training System (TOTS) simulator whose advanced computer graphics and voice recognition and response make it almost indistinguishable from reality. The $10 million system’s graphics are so precise that the simulated aircraft the student operator sees on the 23-foot screen outside the TOTS window look real even when viewed through binoculars. A disaster in early 1991 at Los Angeles International Airport -- where thirty-three people died when a USAir 737 struck a commuter plane a controller had cleared onto the wrong runway -- shows the incomparable value of this kind of simulation training: Students can learn from their mistakes before anyone gets hurt.[139]

Simulation training systems like TOTS mimic the context of real work closely, but are still separate from it. As the CAMS story suggests, training and learning more and more are becoming embedded in the smart systems used to perform the work itself (and even those used for entertainment, housekeeping, and other social functions).

The military, as is often the case, has been in the lead in using advanced simulation methods to embed more training and learning into operating systems. One obvious advantage is saving the cost of dedicated training systems (such as TOTS) as well as the considerable cost of personnel leaving their workplaces to go somewhere to get trained. And training embedded in the real work setting necessarily means more and better hands-on learning.[140]

An outstanding example of this kind of HL technology is the U.S. Navy’s Aegis Combat Training System. ACTS is a training and learning capability that was built into the billion-dollar Aegis combat radar and weapons control system of the Navy’s most advanced class of guided missile cruisers.

Because the Aegis Combat Information Center demands that an entire team of more than a dozen people perform flawlessly under extreme pressure, continual training and honing of skills is essential. The very nature of shipboard service requires that the learning be done in the same place the work is to be performed.

ACTS uses the same displays and consoles of the CIC to run authentic combat simulation scenarios lasting anywhere from a brisk half hour to a grueling five hours. ACTS can allow the combat teams on several ships to work on the same exercise at the same time -- an even closer and more valuable simulation of real conflict conditions.

The same kind of embedded learning is needed and being designed into a wide range of civilian work environments. Like the Aegis CIC, more and more high-tech workplaces demand high levels of human skill to be exercised in relatively infrequent crisis situations. A modern airliner, for instance, is quite capable of taking off, flying, and landing itself -- except when something goes wrong. Studies by the commission on the Three Mile Island nuclear power station accident in 1979 found a problem common to many modern industrial operation control centers: Most of the work, most of the time is routine, constant, and boring. The operator’s key job is to solve unusual and thus unpredictable problems, and to manage the rare but critical disaster. The commission concluded that operators needed not only better designed operating displays, but continual practice with simulators to hone crisis-management skills. Similar needs exist in complex air traffic control, electric and gas and telecommunications grid management, and chemical and other industrial operations systems.

One implication of the increasingly JIT learning environment of work is that Nintendo and similar “game” products are doing more to cultivate the skills needed in the mindcraft workplace than most schools or colleges are. As the above examples suggest, the human role in a growing number of work environments is ever more focused on three functions: creativity, crisis management, and continual learning in a real or closely simulated work context. To be effective in these settings, workers need not only the skills developed by a particular simulation “game” but what we might call “metagame skills” -- reflecting the higher order skills needed to master new games from scratch. That new games ability is crucial to working in environments that require anticipating a virtually infinite number of crisis scenarios and that present continually upgraded operating technology.

Learning to play one particular game well is not enough to acquire those meta-skills, just as mastering a single song on the piano is not enough to qualify you as a “musician.” Rather, you develop the gaming metaskills by learning-to-learn to play a substantial number and variety of games, including play that requires both individual skill and teamwork. That depth and diversity of virtuoso competence is built up from being an active consumer of many products in the game market, whether at home, in the mall arcade, or walking around with a device like the Nintendo Game Boy or Atari Lynx.

Myth 3: The teacher is the fountain; the learner is the bowl.

There may be no more common and erroneous stereotype than the image of instruction as injecting knowledge into an empty head. Whether in a typical schoolroom, or a congressional hearing, or a corporate training session, the same one-way process is acted out. In each, the teacher or expert faces the learners, taking on the critical role of “fountain of knowledge.” The learner plays the “receiver of wisdom,” passively accepting the intelligence being dispensed, like an empty bowl into which water is poured. Education, in this view, is merely a conveyance of what experts already know to be true, rather than a process of personal inquiry, discovery, and wonder. The image of teaching as knowledge-injection emanates from the assumption that education’s basic purpose is to transmit a society’s culture from one generation to the next.[141]

But this model of instructional practice, often called passive learning, contradicts what cognitive science researchers now know about how people learn most effectively. Chief among the objections to passive learning methods are four significant research findings.

Passive learning reduces or removes chances for exploration, discovery, and invention. As noted earlier, students today typically study by subject in fragmented and highly delineated studies that leave virtually no room for the learner to explore, invent, and construct ideas in the natural way any normally curious small child constantly employs. Consequently, students come to regard learning as a thing, not a process; something received, not discovered; part of a body of knowledge, rather than a form of activity, argumentation, and social discourse. To learn effectively, students need chances to engage in choice, judgment, control processes, and problem formulation. They need the chance to be discoverers, not just sponges.

Passive learning creates a lopsided learning dependency that impedes skill development. Passive learning encourages learners to become dependent on teachers for guidance and feedback, undercutting the students’ trust in their own abilities to make sense of what they observe and experience. People can experience the world in basically two ways, as subjects or as objects. Outside of school in their recreational and daily living, free people can be in control of their activities, able to seize opportunities, explore their own interests, and generate solutions to the problems that concern them. In contrast, as researcher Jean Lave points out, school creates “contexts in which [students]...experience themselves as objects, with no control over problems or choice about problem-solving processes.”[142] Because of this, passive learning undercuts the development of the “higher order” skills of creativity, problem-solving, and initiative that increasingly are being demanded of all workers in the mindcraft economy.[143]

Passive learning reduces learning motivation, creating “crowd control” problems. Teachers commonly complain about certain student behaviors that frustrate both the teacher’s ability to teach and the students’ ability to learn. A leading culprit is the bored student who “spaces out” during a teacher’s presentation. Authors of comic strips like “Calvin and Hobbes,” “Shoe,” and “Peanuts” have a virtual annuity in the inexhaustible subject of the bored and fractious student. Outside the funny papers, students of different ages and from different countries go into the same “waiting it out” and other self-defense behaviors during a standard lecture -- a situation that typifies passive learning.[144] And unfortunately, as teachers know so well, motivation problems turn into crowd-control problems when noninvolved students band together to act out their frustration. Research indicates that students become deeply engaged, more motivated, and less a discipline problem when their learning tasks are organized as collaborative discovery projects.[145]

Passive learning encourages the veneer rather than the reality of accomplishment. Passive learning places a premium on the ability of the learner to reproduce the learned word. This translates into sounding and testing “right” within the school system, often without the achievement of effective, useful learning -- the ability to apply concepts and skills in new situations. Anyone who has crammed arduously to prepare for pivotal multiple-choice qualifying exams like the SAT or GRE, only to forget the thousands of key “learned” facts short weeks after taking it, will recognize this phenomenon. In the case of American students, this has been called the “veneer of accomplishment”: Students look like they grasp what teachers and schools want them to learn, but in reality they do not.[146]

Typical curriculum design is based on a conceptual analysis of the subject matter that ignores -- or assumes -- what is already in the learner’s head. But learners rarely come new and empty to what is being taught. Research on students learning science offers many examples of how the learner’s prior knowledge and concepts affect new learning. As researcher Senta Raizen points out, both younger and older students bring to the learning of science their own concepts of natural phenomena like light, heat and temperature, electricity, or physical and chemical transformations. These ideas may be personal, constructed out of the person’s own interpretations of experience, and coherent in their own terms. Or the ideas may come from partially understood or inappropriately applied previous school learning.[147]

Unfortunately, the limited time devoted to specific lessons in the typical classroom, coupled with the passive nature of classroom learning, hardly suffices to disclose, let alone change, the ideas and assumptions that individuals bring to the lesson. The result? Students make mistakes that arise from undetected ideas they bring into the classroom. Or they play back newly memorized “knowledge” and concepts, but return to their own ideas when confronted with unfamiliar questions or nonroutine problems. In short, the challenge of teaching and learning is to confirm, disconfirm, modify, replace, and add to what is already written on a student’s slate -- and that requires the active participation of the student in the process.

Myth 4: More or less academic achievement means more or less learning.

In reality, the human brain is genetically programmed and born to learn -- constantly, from experience. The design of the brain predates the introduction of academic institutions by several million years. Everything that humans learned to make the birth of civilization possible was learned before and without schools, and many civilizations grew and thrived without significant participation in schooling.

Over 99 percent of what the average American will learn in the course of a lifetime will not be learned in a classroom. In fact, most situations of “spectacular learning” -- in which the individual picks up knowledge and skills rapidly and with little apparent effort -- occur out of school. Chief among these situations is the first five years of life when, as children, we acquire concepts, language, and motor, spatial, and social skills through normal interaction with parents and other people, but with little or no classroom instruction.

Researchers Roy Pea and J.D. Bransford and their colleagues analyzed the conditions in which a young child learns “spectacularly,” to provide three reinforcing clues for designing more effective learning opportunities:

Spectacular learning takes place in context. Children learn during the first five years in the midst of culturally meaningful or practical ongoing activities, and they receive continual feedback on the results of their actions.

It is often effectively mediated. Parents, friends, and peers not only serve as models for imitative learning, but help the children learn by providing structure to and connections between the children’s experiences. They highlight information in the situation that will particularly help the child carry out a task. They let them take on “part” activities in completing a whole task, such as measuring out sugar, flour, and other ingredients in the process of making a cake.

The learning is functional. Context and mediation help children understand how information is used to solve problems. Children acquire concepts and skills as tools that can be used for many different purposes. The functions of new knowledge -- and new learning -- are not only shown, but often are explicitly stated.[148]

Of course, what children learn is not always positive. Children exposed to abuse and dysfunctional families are spectacular learners too, and what they learn -- lack of self-esteem, for example -- often winds up haunting them to the grave.

Myth 5: You have to learn to walk before you can learn to run.

It has become commonplace in speaking and writing about the workplace demands of a high-tech economy to talk about “basic” skills and “higher order” thinking, as if to imply one must (chrono)logically come before the other. But, in truth, the type of skill or knowledge being acquired does not dictate the order in which it should be learned.

There are three distinct reasons for this. The first rises from common sense about context: Real-world situations require people to use and learn different kinds of skills all at the same time. Children do not learn first to walk and then to communicate -- they pursue the motor and mental abilities at the same time. Likewise, a baseball player does not need to master bunting before hitting, and then baserunning before throwing, and that before catching. None of these skills is more “basic” to playing baseball than others, and, except for American League pitchers and designated hitters, players need to develop and practice all the skills simultaneously.

Second, modern research on the nature of thinking concludes that people at “advanced” levels of development are not the only ones who engage in the activities associated with higher order thinking -- that is, making judgments, attaching meaning to things, finding multiple solutions, or sorting through conflicting information. Advanced thinking is an intimate part of learning at any level from elementary school to postretirement.

Finally, simply because each human being is truly unique, how learning happens -- and what learning happens when -- is different for different people. People learn by experience, but no two people share the same experience. As anthropologist Gregory Bateson observed, each learner is inherently free to “punctuate” in his own way the stream of experienced events into the elements of stimulus, response, and reinforcement that comprise the most fundamental learning processes.[149] The practical implication is that there is no way to separate learning from what may be called personality, or attitude, or simply emotion. It is no more possible to define a certain behavior conclusively as either a “basic” or “advanced” skill than it is to settle once and for all the question of whether a glass is half empty or half full.

The “walk before run” metaphor actually says more about the peculiarities of the human body than it does about the learning process. Humans happen to be born top-heavy -- a human baby’s head is too big and heavy for its undeveloped body and limbs to support either walking or running. Four-legged animals with small brains, like gazelle or wildebeest, are able to run within minutes after birth -- any of their ancestors who couldn’t do that became instant lion snacks.

School curricula reinforce the impression that logical subjects like math and science require starting with basics and progressively adding more sophisticated conclusions and applications. But the very nature of logical laws makes it equally feasible to work backward from conclusions, or observations, to hypotheses. Deduction and induction are entirely complementary.

In reality, scientists and mathematicians do not do their crafts in the linear, progressive way their subjects are usually taught. Practitioners commonly start with a flash of insight (the stereotypical light bulb igniting), a hunch, a dream, a guess, an elaborate hypothesis or postulate, and then work backward, forward, and around it to try to make it fit with established knowledge. Physicists or engineers commonly try using complex mathematical gadgets to solve the problems that interest them without knowing or caring how the math was logically derived. Experimenters tinker in laboratories and make surprising discoveries that theoreticians then labor to try to explain logically. Alternatively, theorists like Einstein come up with wild new theories like relativity that experimenters may have to struggle for decades to find a way to test and prove. Scientific knowledge does not grow incrementally down a predictable track. Rather it grows volcanolike, sometimes oozing in patient rivulets, sometimes erupting in fiery ferment, and occasionally exploding, blowing away the rock of established truth.[150]

Pedantic, linear teaching rarely conveys the true drama and mystery of the human quest for knowledge. School plods where human imagination naturally leaps.

Myth 6: Education is different from training.

The scientists who study learning increasingly recognize that apprenticeship is a powerful way of organizing learning-in-context for any purpose. In contrast to the traditional view of academic learning as different from or even superior to vocational learning, scientists now speak of “cognitive apprenticeship” as the key to acquiring the higher order thinking skills that, in turn, are increasingly needed for working and living in the knowledge age.

So any kind of learning that aims to be relevant to the real world can benefit from the following characteristics that Jean Lave, Brigitte Jordan, and other researchers have observed in traditional apprenticeships.

Apprentice learning focuses on doing rather than just talking. Apprenticeship is concerned with the ability to do rather than the ability to talk about doing something. The apprenticing process arranges opportunities for practice, whereas school curricula -- where the focus is typically on verbal and abstract information -- tend to be a specification of practice. Apprenticeship learning comes through the practice of skills. The master is less likely to talk than to guide by modeling, assigning tasks, overseeing, and critiquing. Indeed, it may be quite difficult to get craft masters and apprentices to articulate what it is they know how to do. The division between academics and apprentices goes back to the classical age of ancient Greece, when the “liberal arts” curriculum was originally designed as vocational education for politics. The first and foremost goal of such instruction was apprenticeship in the skills of rhetoric, in preparation for the craft of political argumentation. So in early academia the ability to do and the ability to talk about were the same thing. But with the vast expansion of academic institutions since the early nineteenth century, rhetoric as an end became mistaken for a means of teaching. As a result, the rhetorical methods of academic vocationalism have been increasingly misapplied to a wide range of nonpolitical crafts and skills which, to be learned effectively, need doing and talking about to be separated.

Apprenticeship is a way of life. Apprenticeship happens in the course of daily life. In fact, apprentices seldom sense any separation between activities of daily living and learning of “professional” skills. Rather, the apprentice is exposed to a certain environment, is socially engaged with a community that shares some common interest, participates in sets of work-relevant activities, handles (plays with) certain tools, and is trained in the sphere of specialist work the same way a child is in the home environment.

Work is the driving force. Masters and apprentices engage in activities that are driven by the requirements of the work to be accomplished: Pots must be fired, a shawl woven, trousers manufactured. Whatever teaching or learning may happen is coincidental to the overriding concern of the work to be done. Consequently, the apprentice values progressive mastery of tasks not so much as a step toward a distant, symbolic goal (like a diploma), but for its immediate benefit. Apprentices are not practicing for the real thing -- they are doing it.

Apprentices acquire skills in a meaningful order. While on the surface apprenticeship may seem to impose a “walk before run” orderliness, in effective apprenticing the order derives from the organic structure of the work and its real context, rather than from an artificial model of cognitive difficulty. Apprentices commonly are directed to start with skills that are relatively easy, where mistakes are least costly. For example, young tailor apprentices, rather than constructing a garment from start to finish, first experiment with parts of the production process that are least costly in terms of wasted materials, like sewing garments from pieces someone else has cut. Working from the “sidelines” of a complex task toward its center stands in contrast to the ways that knowledge is usually transferred in formal schooling. In a formal classroom, things are usually learned in chronological or some other arbitrary order divorced from learning in context. The components are treated as equally important, and it is assumed that they must be acquired in a linear way -- one after another. But apprentices acquire skills in bunches or bundles that fit together to solve a practical problem. Much of the learning is “just in time” -- immediately connected to a problem that has to be solved now for the work to proceed.

Performance and competence evaluations are implicit, embedded in the work environment. For an apprentice, expert execution of a task is obvious and easily observable -- in the master’s performance. Judgment about the apprentice’s competence is likewise obvious and needs no commentary: It emerges naturally and continuously as work is accomplished, rather than occurring as a specially marked event, like a test. In fact, to a great extent, the person who judges the apprentice’s performance is the apprentice. Having observed the work sequence many times, the apprentice knows what remains to be learned. Moving on to acquire the next skill is largely up to the apprentice, rather than under the master’s control. Apprenticeship is inherently individualized. The master promotes and assigns apprentices as their talents and limitations are demonstrated in practice.

Teachers and teaching are largely invisible. In apprenticeship learning -- as well as informal on-the-job training in modern workplaces -- it may look as if very little teaching is occurring. Whatever instruction the apprentice receives originates not from a “teacher” who is doing teaching but from another worker doing his or her work, which the apprentice observes.[151]

In apprenticeship learning, the apprentice is being inducted into a community of expert practice. The community is not limited to the local “studio” but extends across space and time, joined by a variety of professional associations and by the formal history and informal folklore of the craft. Apprenticeship learning illustrates the distinction between doing and waiting it out, between an active and a passive environment.

Moreover, contrary to what may appear at first glance as an orthodox ritual, apprenticeship learning is neither static nor simply concerned with the one-way transmission of tried-and-true practices from masters to novices. In effective apprenticing there is a healthy, dynamic friction between preservation and renovation of expertise. “Change is a fundamental property of communities-of-practice and their activities,” researchers Jean Lave and Etienne Wenger observe. And they note that “inexperience is an asset to be exploited,” not just a vacuum to be filled. Precocious and irreverent apprentices challenge and inspire masters as well as follow them. Members of a community of practice all learn from each other.[152]

Only a fraction of a percent of U.S. workers -- mostly in construction trades -- are trained through the traditional, formal kinds of apprenticeship programs that are common in other countries, and that are especially widespread in Germany.[153] But apprentice learning is becoming such an inherent feature of the mindcraft economy that more formal programs may not be needed.

With expertise ever more embedded in networks and smart tools, rather than personal “masters,” the features of apprentice learning are becoming almost universal in the HL environment. Most apprenticing now is going on almost invisibly through the use of expert systems like CAMS, Magic, and others mentioned earlier, through simulation and embedded training systems, and through the collaborative relationships forged through communication network utilities such as groupware, electronic mail, and bulletin board systems (BBS).

For instance, here’s another example of how video games may be more relevant to the needs of a mindcraft economy than classrooms are: There hardly could be a more graphic and energetic example of a “community of expert practice” in action than the community of video and computer game players, whose numbers in the U.S. alone may represent a fifth or more of the national population.

At Nintendo’s U.S. headquarters in Redmond, Washington, a “faculty” of five hundred game masters takes one-hundred-fifty thousand telephone calls a week from apprentice gamers seeking guidance to hone their playing skills.[154] The game community is further linked by a host of magazines, books, newsletters, clubs, meetings, and tournaments. Visit any shopping mall game arcade and you will see self-organized apprenticeship in action: Clusters of players of diverse age, gender, race, creed, color, and national origin arc around the player currently engaged with a machine, observing play and exchanging comments and tips on tactics and strategy. Mastery is relative to the composition of the ad hoc group and the particular game -- the master-apprentice roles continually shift among the members of the community.

This kind of self-organizing HL community of practice is as typical of the mindcraft workplace as of the world of knowledge age entertainment. The same kind of collaborative user groups and telecommunicated help and guidance have enabled over 60 million Americans to become handy with personal computers -- the great majority without attending any school or classes. With four out of ten U.S. workers now using computers at work,[155] individual ability to join and work with a self-created apprenticing community is becoming ever more crucial to organizational performance and national productivity.

And this “Nintendo-izing” of work-connected learning is not limited just to the white collar office, as an anecdote from a Fortune magazine story of a few years ago illustrates. Computer experts who were starting to teach a steelworker to program a new inventory control system at an American Steel & Wire mill in Cleveland ran out of time and decided to postpone the training to the next day. “That night, the worker went home, sat down with his kids at a computer, and figured the program out,” the magazine reported. “The next morning he went in and taught it to other workers. By the time the computer experts came back to complete the training, the guys in the mill already had the system up and running.”[156]

Myth 7. Some people are smarter than others.

The rigid, uniform, lockstep pattern of instruction followed in most schools and colleges nurtures the popular illusion that there is a one-dimensional spectrum of human intelligence that ranges from dumb to smart. The common experience of schooling has convinced most people that students who get higher grades and test scores are “better” students because they are “smarter,” and have a higher “IQ.” Americans are more prone to this belief than Asians such as the Japanese, who are more likely to attribute superior academic achievement to greater effort and discipline than to natural talent. Still there is a broad tendency to believe that people who have accomplished more academically are more capable in general.

But scientists know that human performance is far more diverse and complex than the linear sorting that arises from a schooling process designed for standardization. First, it is well established that learners have as many as a dozen different “styles” learning. That is, some people learn best by reading words, others by seeing pictures, listening to sounds, touching things, or moving around -- or by particular combinations of these various modes of sensing and doing.[157]

Thanks in important measure to over two decades of study of human ability by Howard Gardner of Harvard University, we know that human talents are more diverse, focused, and variable than a single lump-sum measure like IQ can capture. Gardner’s interest in the nature of human talent began when, as a young researcher at Harvard Medical School, he was struck by the diverse effects of brain damage -- victims might lose only a very specific skill, such as the ability to match words and pictures, or alternatively might demonstrate extraordinary skill in a specific task such as performing complex arithmetic calculations despite severe disabilities of other brain functions. Gardner’s years of subsequent study of these and related phenomena, reported in six books and numerous other publications, probably has done more than the work of any other researcher to dispel the notion of a single, simple measure of human competence.

In particular, Gardner has identified seven different areas of mental ability or “intelligence,” each of which is endowed or developed more or less independently of the others.

· language

· logical-mathematical analysis

· spatial representation

· musical thinking

· use of the body to solve problems or to make hings

· understanding of other individuals

· understanding of ourselves

Different people have their own combinations of strengths and weaknesses in each of these areas. The several intelligences contribute in all sorts of ways to the great variety of human achievement that extends far beyond the range of mere scholastic standards: People can be “geniuses” in many ways other than those normally credited by schools.

And, while the pioneering studies by Swiss psychologist Jean Piaget suggested that children’s brains tend to develop certain abilities in progressive stages at about the same ages, researchers now observe wide individual variations around these average generalizations for the population as a whole. Particular children may be years apart in their “readiness” to take on certain mental tasks successfully. And Gardner concludes that Piaget’s findings, at least after age six or seven, are the result of cultural influences -- schooling in particular -- not inherent stages of brain development. Moreover, even if some abilities, like learning language, are easier to acquire during childhood, that doesn’t mean those abilities can’t still be developed, perhaps with more time and effort, in later life.

In short, what science knows is that human abilities are diverse in their form, range, and development and are intensely individual -- they do not match the mass-production “standards” of schooling.

The insistence on smart-dumb standards inevitably requires reliance on standardized testing and grading practices. But testing acts as a double-edged sword against learning in context.

Besides its tendency to encourage teaching and learning topics outside their natural applications, testing in and of it itself is out of context: it does not reflect how problems are solved and success is gauged in the workplace or any other real-life environment. Researcher Jean Lave observes, “Classroom tests...serve as the measure of...‘out of context’ success, for the test taker must rely on memory alone and may not use books, classmates, or other resources for information.”[158] As the twenty-first-century workplace and technology increasingly employ teamwork and complex problem-solving processes that no longer produce right or wrong answers -- just worse or better thinking -- the results of rote testing will be less and less able to signify a well-prepared learner, a person capable of working, managing, and building successfully.

The reverse cut of testing’s two-edged sword is that academic testing demonstrates not only phony competence but false incompetence as well. In particular the flurry of national tests and