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 surveys that claim to show that frighteningly large portions of the American population are illiterate, ignorant, and incompetent should be viewed, to put it mildly, as suspect.

For instance, social scientists who observed young street vendors in Brazil doing their business in the real world found that these little-schooled, wayward children had informally developed their own calculating techniques well enough that the children could successfully solve 98 percent of marketplace math problems -- such as figuring total costs or making change. But when the children were presented with the same kinds of calculating tasks in the form of arithmetic problems stated verbally with only a symbolic description of the context of the problem, the children solved only 74 percent of the problems successfully. And when the problems were presented purely as mathematical operations with no descriptive context, the children’s success rate fell to 37 percent.[159]

These results are not unusual. In another study, scientists found that minimum-wage workers employed on the loading dock of a dairy who showed almost flawless math skills in dealing with often complex work tasks, such as filling orders and making out bills, nevertheless scored poorly on academic math tests with problems equivalent to those they were successfully solving on the job. Moreover, the dairy workers showed greater flexibility in adapting their calculating strategies to real problems than did math students in school.[160]

So what science reveals about all this out-of-context academic teaching and testing confirms what many of us suspected all along: The “best and the brightest” aren’t really so smart. And the “least and the dullest” aren’t really so dumb.

Myth 8. Facts are more important than skills.

Whatever the public as a whole may feel, it is an article of faith among many would-be education experts concerned with “cultural literacy” that the recent presumed failure of American schools can be blamed on a movement in teaching to emphasize higher-order thinking skills over the memorization of a “canon” of facts that “everybody should know.” The argument, in its simplest form, goes that you can’t be a good thinker if you don’t know anything.

There is, in truth, an element of scientific validity to that argument. But that element is only part of what science knows about effective thinking and learning, and taken by itself it leads to erroneous conclusions about what constitutes useful instruction.

Cognitive scientists recognize that intelligent behavior -- whether of a computer or a human or other brain -- requires knowledge. After all, intelligent thinking requires thinking about something. And at least some of the key strategies being pursued by researchers in artificial or applied intelligence presume that the functional “intelligence” of AI systems depends directly on the amount of knowledge they can encode and manage.[161]

But the status of “facts” as elements of knowledge has a different meaning in the world of cognitive and computer sciences than in the academic vision of effective schooling as an exhaustive game of Trivial Pursuit. In the perspective of science, data are not “knowledge” nor do they become knowledge simply by being stored and retrieved to and from memory.

As the anthropologist Gregory Bateson explained, data are messages, recorded as bits in some medium, whether a stone tablet or a magnetic disk. Information, in Bateson’s elegantly simple definition, is a message that makes a difference -- that is, it is perceived in a way that has an impact on whoever or whatever receives it. Knowledge, then, is information that makes a difference in meaning, that is, in the way other information is perceived. The difference between information and knowledge is the difference between perceiving and understanding.[162]

To illustrate these distinctions: Slapping a dog on the rump with a rolled newspaper sends a message. If the dog is awake, the message will be received and become information by making some difference in the dog’s brain that is likely to be observable in the dog’s physical reaction. But if the dog is under anesthesia, the message of the blow won’t convey any information to the dog’s brain; it just won’t make any difference. On the other hand, if you transmit the spank to an awake dog in the context of rubbing his nose in the wet spot he left on the carpet, a reasonably intelligent animal ought to acquire some knowledge about the rules of housebreaking.

In short, data become information and information becomes knowledge through the processes of communicating, thinking, and learning. In research on the phenomena of intelligence in both artificial and living brains, the “facts” of interest are not mere data points but propositions -- basically, facts about facts.

If anything simple can be said about the awesome complexity of intelligence it may be that intelligence lies in connecting -- not only connecting information to information to weave the elemental fabric of knowledge, but connecting knowledge to action and to experience.

For instance, here’s what E.D. Hirsch, Jr., author of Cultural Literacy and other tomes of triviaphilia, thinks is “what every American should know” about a common academic test item, the Battle of Hastings:

A battle in southeast ENGLAND in 1066. Invaders from the French province of NORMANDY, led by WILLIAM THE CONQUEROR, defeated English forces under Kind Harold. William declared himself King, thus bringing about the NORMAN CONQUEST of England.[163]

This is an epitome of the kind of feckless factoid the cognitive scientists call “decontextualized” -- scientific jargon for borrrrrrring. It is about as interesting to an AI programmer, or even his computer, as it is to you, me, or the average high school student.

There’s no obvious reason why “every American” should know anything about the Battle of Hastings. But there is something quite interesting, even memorable, about the battle that is unmentioned by (perhaps unknown to) Hirsch and his associates.

What’s really important about the Battle of Hastings, as James Burke reveals in his book with the not-coincidental title, Connections, is that it was a milestone in the power of technology to transform society. Burke uses a picture from the Bayeux Tapestry, which recorded the battle eleven years later, to expose the decisive technology, a simple device that gave England to the Normans and changed the shape of history:

The device itself is hard to see on the tapestry, but its presence is apparent from something else that can only be there because of it: the kite-shaped shield carried by a rider whose right arm is occupied holding a lance, and who is therefore too busy to protect his vulnerable left leg. The fact that the shield is long enough to protect the entire length of the body reveals the extent to which the right arm is busy, and the only thing that would keep it so busy is a lance. And the lance is there only because of the device in question: the stirrup.[164]

You still may not know why Hastings is a big deal, or why you should care, but odds are that Burke has made you a lot more curious than Hirsch has. Even in these few sentences, he shows the power of connections made by truly intelligent thinking, in contrast to Hirsch’s row of Teflon trivialities that offer nothing to stick to each other or to comprehension.

Assuming that all knowledge is human knowledge -- and so far we have no evidence of other beings that think consciously -- it follows that all knowledge is connected to all other knowledge. This is not to say that each human knows everything but that humanity as a whole knows all that is known. Or to put it another way that sounds almost trivial: There is no unknown knowledge -- because data become knowledge through the processes of knowing, thinking, learning. Much of the history and cultural legacy of ancient Egypt was recorded as data but was unknown until someone found the Rosetta stone, which permitted the information encoded in hieroglyphics to be connected to the knowledge of living people.

So the proper “map” of the universe of all knowledge would be something like the surface of a sphere (actually a hypersphere, with many more than just three dimensions), one that is also expanding, like a balloon. Every “fact” is a point that is connected to every other point.

The key thing is that there is no center on that sort of map. In point of fact, any fact or group of facts claiming to be the center or source of all other knowledge would be, well, pointless. Cambridge has as much claim to being the center of knowledge as to being the center of the earth or the whole universe for that matter. It can believe that if it wants, and maybe even convince others to agree, but it can’t find support for such a claim in science.

The conclusion from all this is that there is no universal canon of facts whose memorization is prerequisite to advanced skills of thinking or know-how. Intelligence does need to think about something. But intelligence creates knowledge from data. Data don’t create anything but the costs of storage. Actually, one of the most essential skills of intelligent thinking is learning what to think about.

Take the Battle of Hastings. I wouldn’t be surprised if you’ve not been too tuned in to my profound explanation of the meaning of knowledge because what you really wanted to know is: What was so important about that darned stirrup?

Okay. The stirrup had trickled into Europe from somewhere in Asia, but Hastings was its first really important implementation in battle. William became “The Conqueror” because the French had figured out before the English that the stirrup allowed cavalry to be used as a shock-troop to break the lines of the opponent’s infantry: The Bayeux Tapestry shows the Norman knights charging the English lines at full gallop with lances held horizontally, a maneuver that’s feasible only if you have stirrups to keep you from getting knocked off your horse. The effect of a spear point propelled with the momentum of about a ton of horse and rider behind it turned out to be profoundly disheartening to foot soldiers.

With the stirrup, the ever more heavily armored knight dominated the battlefield with as devastating effect as his lineal descendant, the main battle tank of today. No monarch can afford to let his adversaries get that kind of competitive advantage, as hapless Harold demonstrated.

So Hastings unleashed an arms race that changed the shape of European society, and ultimately therefore the world. Knights were essential to national security, but very expensive. Armor and weapons got bigger and heavier and more costly. More, bigger, and stronger horses were needed. Each knight required an entourage of staff for maintenance and repair, and a baggage train of supplies, extra weapons, spare parts, and such.

Kings met the knights’ need for wealth with grants of large estates. To protect the estates from attack by other knights, expensive fortresses and then castles were needed, which required even more land and peasants to support them. To try to keep the potent knights in line, kings promoted codes of chivalry (another name for cavalry). Nevertheless, the English barons got particularly uppity one night in 1215 and forced King John at swordpoint to sign a document called the Magna Carta -- basically a promise to say “May I?” a lot.

So the whole structure of European feudalism, and then aristocracy, flowed directly from the simple stirrup. And the eventual toppling of that social structure began three-and-a-half centuries after the Battle of Hastings when another dominant technology, the Welsh longbow, was employed by a small army commanded by the English king Henry V to virtually eradicate the French aristocracy at the Battle of Agincourt, returning William’s favor by conquering France. But that’s another story.

Unless you are a jockey or fox hunter or mounted police officer (or Prince Charles), you still may not see any particular connection between the Battle of Hastings and anything that’s important in your life. If you are deeply concerned with linguistics or English literature, the Norman Conquest may be notable because its infusion of French speakers to England transformed the old English language to something close to its modern form. But actually, there may be no reason why you “should” know anything about the Battle of Hastings. On the other hand, the next time you see a stirrup or even just someone stepping onto a horse, you may have a hard time not remembering Hastings.

The point of all this is: Knowing what data are worth thinking about, like the effectiveness of thinking and learning themselves, depends on the context. If the context is scoring points on an academic exam, the brain-dead “facts” about Hastings that Hirsch and his ilk peddle may be worth memorizing for about two weeks, and then forgetting once the test is over -- exactly what most students wisely learn from that kind of artificial context. If you’re interested in warfare, horseback riding, language, or especially the impact of technology on culture, the Battle of Hastings acquires a more potent and durable meaning in any of those contexts, and a somewhat different meaning in each.

Still, it might seem that at least within the framework of a given academic subject, say physics, there certainly would be a canon of crucial facts or knowledge that must be mastered. But I can tell you from personal experience that “knowing physics” in the context of a standardized test involves a very different mass of knowledge and skill from what’s required in the context of doing research in a laboratory, designing an electric generator, fixing the busted whoozits on your VCR, or playing racquetball.

It has taken our nation’s school establishment about two decades to figure the obvious out: It doesn’t make sense to spend weeks of class time forcing students to memorize multiplication tables or interpolate logarithms in a world where electronic calculators come built into rulers, pens, watches, and key rings. Schooling is simply too inert to adapt to an imminent HL world where an unlimited supply of “facts” is available instantly, anytime, anywhere -- and where, within a generation or so, you will be able to carry around an entire lifetime’s supply of information in an object that will fit in the palm of your hand.

In the context of a knowledge age society where humongous volumes of data can be generated, stored, and communicated with lightning speed to just about anyone, anywhere, anytime, know-how clearly has become more important than know-what. But what that requires in practice is learning how to combine knowledge with skills to achieve your goals in a real-life context.

When you consider the almost stifling cornucopia of sources and media of knowledge that confronts us, the several kinds of intelligence or skill all humans have in varying measure, the richness of human aspirations, and the immense diversity of real-life situations people care about, it’s clear that nurturing the hunger for know-how is no mean task. In fact, it’s a task that cannot possibly be mastered in childhood -- it must be a lifelong occupation.

Myth 9: Learning is solitaire.

In America at least, schools are designed on the model of the learner as a strictly independent operator, and learning as an entirely private action. Academia’s way of accounting for learning focuses on tests of individual performance in strict, austere isolation from cooperation with others or use of resources or tools outside the learner’s head. The role of collaboration or technology in learning is placed in the category of cheating.

This mythical and misguided vision is, fortunately, being eroded by a mass of social and cognitive science findings that there are limits to what can be learned alone, and that the most effective and useful learning is a shared enterprise. Over eighty studies by brothers David Johnson and Roger Johnson of the University of Minnesota show that students not only master subject matter better in cooperative settings than they do working in isolation, but they develop better social skills and self-esteem.[165]

Contrary to the popular vision of the isolated “hacker,” HL technology only reinforces the imperative for cooperative learning. As noted earlier, with information mushrooming beyond the capacity of any one human mind, expertise being absorbed into networks, and productivity in most economic arenas increasingly dependent on teamwork, the necessity or even possibility of isolated learning is rapidly diminishing in the mindcraft economy.

The technologies of individualized instruction and cooperative learning are not contradictory or mutually exclusive. Intelligent tutors and other automated teaching systems are about as cost-effective as the cooperative practice called “peer tutoring” -- peers teaching one another.[166] The practices are complementary and tend to occur naturally in HL environments like video game arcades and high-tech workplaces where, as mentioned above, apprenticing seems to be almost self-generated. Many successful applications of automated instruction group students in clusters of two, three, or four sharing a tool such as a computer. Computers and other smart tools joined in networks, especially when enhanced by groupware, inherently draw the individual user through gateways to collaboration.

Whether the inherent sociology of academic institutions will allow would-be educational innovators to succeed in displacing the traditional divide-and-conquer ethos of schooling with cooperative learning in practice is another matter entirely. Engineering students at MIT were recently caught in this whipsaw. The students were urged to work on a term project in teams -- the idea, appropriately, was to cultivate the teamwork skills increasingly required in a Total Quality Management industry environment. But the same students then were sanctioned for cheating when the project reports turned in by each of the members of the same team were, unsurprisingly, virtual verbatim clones of each other.[167]

Myth 10: Schooling is good for socialization.

While many of the audiences and friends with whom I’ve discussed these myths of schooling over the last several years are willing to recognize the many paradoxes if not hypocrisies of what passes for academic teaching, they often still cling tenaciously to the faith that school as an institution is needed for what they’ve been told is “socialization.” People generally are far more willing to discuss reforming schools than to seriously ponder the reality that school is an obsolete institution whose time has come and gone, and that is ready for extinction and replacement.

This nostalgia for schooling as cultural ritual, as a rite of passage, as a way of life, is as understandable as it is costly. Many of us justly have rich memories of school as a positive force for our development, maturity, and fulfillment -- shared experiences with lifelong friends, the exhilaration and hoopla of athletic triumphs, dating rituals and first-time benchmarks in sexual coming of age, and even the occasional special teacher or coach who befriended us and piloted us past the reefs of adolescence toward a voyage of achievement.

But what we commonly overlook or misremember is what Star Wars guru Obiwan Kenobi called “the dark side of The Force.” Every yin has its yang. If we look honestly at what scientific study reveals about the dark of side of schooling’s “socialization,” we should conclude that the benefits can be obtained in other ways that are far less costly.

One of the keenest yet often unappreciated insights of modern thinking was Marshall McLuhan’s maxim that “the medium is the message.” No mere slogan, McLuhan’s observation expresses the key finding of cognitive science: Learning is empowered by its context. One grave flaw of schooling, noted already, is that, by design, it disconnects learning from the real-life contexts that give learning meaning and value.

But it’s a mistake to assume that school simply deprives learning of any context. Rather schooling replaces the contexts of real life with the context of school life. Learners do not simply stop learning in an artificial context -- they proceed to learn artificial lessons taught by the context in which they find themselves. To clarify McLuhan’s warning, the message and the medium are always learned together; they can’t be separated. In practice, school creates a context that often makes learning not just sterile but hazardous.

For instance, whatever it has done for test scores, academic education following the European “liberal arts” tradition also has served to reinforce feudal class structures and ethnic/national division in Europe and the Orient. In America, the same academic conceit has bred what the late Herman Kahn labeled a “New Class” of credentialed experts infected with “educated incapacity.” The cultural bias of “liberal” academia against manual labor, commerce, and even capitalism has contributed to Europe’s festering unemployment and to America’s flagging industrial competitiveness.

And if academia has been a mixed blessing to human development in Europe and America, in the Third World the disdain for work and productivity bred into the European-style schools inherited from colonial masters has been an economic and social catastrophe. In countries such as Zimbabwe and Sri Lanka, the overdose of academic education has bred a socially disruptive class of overeducated unemployed. Charting the same phenomenon in Indonesia, Nathan Keyfitz concluded: “To sell education to the public as a means to upward mobility ultimately risks disillusionment.”[168]

e of the major hazards is social polarization. Penelope Eckert, a sociologist at the Institute for Research on Learning, has found in her studies that a major social impact produced by the normal schooling context, culminating in high school, is to divide youth into lifelong cultures of winners and losers. “While curricular tracking has come and gone in the American public schools, adolescent social categories remain as an enduring and uncontrolled social tracking system,” Eckert observes. “It is largely as a result of the polarization between the Jocks and the Burnouts that people are thrown into a choice between two set patterns of behavior on the basis of a variety of unrelated interests and needs....”[169]

Moreover, this pernicious form of socialization is the result not of school quality or administration or location but of the inherent structure of the institution itself. In particular, Eckert finds that “the segregation of adolescents in an age-graded institution, isolated from the surrounding community, focuses their attention on the population, the activities and the roles that are available within the school,” instead of those of what we commonly call the real world.

Eckert saw that the effect was particularly destructive to the losers or “Burnouts,” who both value and need communal ties to a social network for their human and economic development. Schools not only ignore but actively oppose the Burnouts’ ties to poor, lower class, or minority communities that academia treats as inferior to the culture of school itself.

An academic culture that makes college and even graduate credentials the ultimate measure of social value and success creates far more losers than winners. In the American education system, the most democratically open and progressive in the world, 75 to 85 percent of students will not enter the Valhalla of what academia considers “educated” persons. Without a breakthrough of consciousness, the academic underclass will bear the undeserved burden of wounded self-esteem and social and economic inferiority for the duration of their lives.

The polarization inherent in schooling increasingly is harmful to the Jocks as well as the Burnouts. Even most of the winners will wind up feeling like losers when they fail to be anointed or treated like the “best and brightest” of their class. School handicaps the Jocks with a delusion of superiority that has little tangible basis in the real world. There never has been any significant association of academic success with success in working and living in the real world outside the ivory tower. As a number of education critics have accurately observed, school is vocational education for the job of college professor. The recent disillusion of the MADMUPs and the rejected “overqualified” reflects the growing disutility of academic success to the real economic opportunities offered by a mindcraft economy.

The persistence of the polarizing effect of scholastic socialization is almost stunning. When I attended my twenty-fifth high school reunion a few years ago, I was struck by the ironic contrast between the actual life achievements of the members of my class and the attitudes of social status that were re-enacted at the gathering. When we were students, academic and vocational “tracking” was still a common practice. But the U.S. economy is truly more concerned with effort, ambition, enterprise, and a big dose of luck than about such scholastic distinctions: So among the participants at our reunion I found “voc ed” students who had risen to high executive positions in major corporations as well as former academic “stars” who, at the age of forty-something, were back in graduate school, still struggling to “find themselves.” Nevertheless, you could still see and hear the vice president of one of the country’s biggest communication companies, a successful husband, father, and pillar of his prosperous suburban community, acting deferential to his penurious, Ivy League, bohemian intellectual classmate, who had yet to partner, parent, pillar, or produce much. There they were still playing the game of Jock and Burnout, “college-bound” and “work-bound,” smart and dumb, winner and loser a quarter century after what should have been the final gun.

Polarization is just one of numerous subversive messages buried in the medium of school culture. Some of them are recalled by an incident from my experience as a young physics teacher in the same high school I had graduated from five years earlier.

One of my unorthodox practices was to have my students present each of their homework problems on the blackboard in front of the class. My motive was partly laziness -- correcting homework is no fun. But my whole approach to teaching (based on zero time in ed school but twenty years as a student) was to give my students maximum responsibility for teaching themselves, and minimum dependence on me for anything but encouragement, pacing, and refereeing. The whole point of this and everything else I did was to overcome that lethal fear of failure that now as then has young Americans dropping out of math and science in droves. Doing homework on the board was practice in teamwork, helping each other, being active learners, and most important recognizing that mistakes are not failures but just stepping stones toward the truth.

Anyway, one of my students was a black girl -- a precious resource not often found in physics classes then or now -- who had a particularly hard time with this exercise. Whenever it was her turn to do a homework problem for the class, her immediate reaction was to protest, “I can’t Mr. Perelman, I’m not good at math.” And I would have to goad her, step by step, to pick up her textbook, come to the front of the room, open the book, and, finally, start by reading the problem to the class. All the while her broken record is clicking, “I can’t, I’m not good at math.”

Okay, write the first step of the solution on the board. (By now you can guess her response.)

Now I’m playing cheerleader and coach: Don’t worry. Just write something. It doesn’t matter whether it’s right or wrong. There’s no grade on this. You can do it. Just write anything at all.

So she writes some math stuff on the board and, the class all agrees, it’s right. That’s fine, I say. Now let’s see the next step.

“I can’t, I’m not good at math” became a kind of mantra, like “Amen” in church except a denial instead of an affirmation of faith. And so it would go, step by step, each punctuated by her inverted prayer for failure, always unanswered because she always got everything exactly right.

That, sadly, is not the end of the story. After a few months, she suddenly dropped the course. She was an excellent student, not just in the win-lose accounting of grades (I had an even more unorthodox policy on that) but absolutely: She was mastering physics, as nearly all of my students were -- I believed then as now that everyone is supposed to get an A, and if they didn’t that meant I wasn’t doing my job well enough.

I guess I didn’t do it well enough for her. I was shocked and perplexed. Twenty-plus years later, I’m still furious about what I learned from that experience. Why would someone run from success toward failure? It punctured a number of our -- my -- schooling myths, especially the ones about smart being better than dumb, winner better than loser.

With time, reflection, and insights from my students, I think I figured it out, at least partly. That girl learned, through the context of seventeen years of life and schooling, that blacks, females, and therefore especially black females are not, cannot, and must not be “good at math.” There were much higher stakes in her vulnerable adolescent heart than just another grade on a transcript. Unwittingly and with the best intentions, I had thrown her into what was virtually a life and death struggle.

Adolescence is more than anything a quest for identity: What am I going to be when I grow up? is really Who am I going to be when I grow up? This talented girl had been told all her life by teachers, friends, family, and maybe the whole country who and what she was, and whatever that might be, it had to include “not good at math.” And there I was, eager beaver young Harvard brain-jock, proving beyond a shadow of a doubt to her, her friends, her family, and the whole world that that was a lie.

It didn’t take me too long to figure out what a heavy message that was: Her friends, her parents were finding out that she was not the person they thought she was. She was at real risk of having some costly social stigmas stuck on her: uppity, acting white, nerd, unfeminine. Would they still love her? Would they even accept her? If not, what other community would embrace her? Eventually I suppose she decided the price of that academic success was too high, too scary. It probably would have helped open economic as well as intellectual opportunities for her, but quite possibly at the cost of great loneliness.

I wasn’t wrong to try to help my students fulfill their abilities. I can’t say she was wrong to react the way she did. We were caught in cultural rituals imposed by a larger system, like two enslaved gladiators who were forced to combat in an ancient Roman circus that cared nothing about what either of them wanted, needed, or felt.

There’s nothing unique about her story. It’s happened millions of times before and since, and is still going on today. I’ve been through similar rituals with friends’ daughters in recent years who are still being brainwashed by schools and parents to not only doubt but suppress their talents.

These experiences are echoes of a growing body of research showing that girls in all-female schools maintain high levels of self-esteem and accomplishment in all fields including math and science, while the confidence and performance of girls in coed schools take a steep and steady nosedive after about grade seven or eight. Detailed analyses of video records of classroom behavior show that teachers or professors, both male and female, persistently call on, praise, and encourage males several times more frequently than they do females. The teachers rarely realize they are being biased; they are reflexively acting out rituals deeply embedded in the culture of schooling.[170]

The medium of schooling delivers similarly subversive lessons to various minorities in America. Some minority cultures, some blacks and some Hispanics, seem more vulnerable to these assaults than others -- notably Asians, particularly from societies with Confucian traditions.

Just because some groups and individuals have endured and overcome these insults does not mean that the insults should be tolerated, and the victims blamed. It also does not mean that the victimization should be perpetuated by telling the victims of school-manufactured failure that they have accomplished what they have not, that they know what they do not, and that they have been released from the responsibility for learning -- which is foremost a responsibility to themselves.

In reality, the people who “flunk” academic tests or “drop out” of school do not stop learning -- they just learn other, generally undocumented and uncredited, but often sophisticated, things. Prisons provide exemplary models of spectacular learning where novice lawbreakers learn the art and culture of crime.

These are just a few facets of the dark side of the force that school passes off as socialization. “[B]y the time students reach high school,” Eckert observes, “the Jocks and the Burnouts are all too generally perceived as representing good and bad, cooperation and rebelliousness, success and failure, intelligence and stupidity.” For the losers, the lessons of socialization become articles of surrender -- Eckert finds that “[r]ather than asking themselves how they can succeed in spite of the school, Burnouts discard goals along with the means to achieve them.”[171]

The myth of the decline of schooling is that our students are failing to learn. The real outrage of schooling is that our students are learning to fail.

A corollary to the myth of school as a constructive medium of socialization is the myth that “technology” -- computers and other modern multimedia -- is a negative influence on socialization. For instance, there is a craving (popular even among some educational researchers) to believe that Nintendo and other video games promote anti-social, even violent behavior.

While game and computer markets are now so huge and fast-changing that they defy the capacity of social science to track accurately, virtually all the available evidence indicates that the effects of these tools on social behavior range from harmless to highly positive. While some observers worried about the violent content and male gender bias of an earlier generation of games, no tangible harm was scientifically demonstrated. Today’s popular games -- such as SimCity, the Carmen Sandiego series, geometric puzzles like Tetris, graphically stunning computerized versions of various card and board games, and even Super Mario Brothers -- are far more intellectually diverse and challenging, and more likely to appeal to female as well as male interests.

As noted earlier, simulation games generally cultivate both individual and team skills that are far more relevant to the mindcraft workplace than the behaviors demanded by traditional classrooms. Rather than isolation, games and computers inherently tend to encourage self-organizing communities of practice, as observed by psychologist Robert Kubey of Rutgers University. Memphis psychiatrist Joseph Cassius believes games can boost children’s self-esteem by encouraging “a sense of proficiency without fear of conflict.” As for game-playing being a waste of time, studies by Gary Creasey of Illinois State University found that children’s involvement with video games increased at the expense of time taken from only one other activity, watching television.[172]

Modern communications technology further is expanding opportunities for students to develop and practice social skills in realistic contexts, getting timely feedback and intrinsic rewards -- shown by research noted earlier to be essential characteristics for productive learning. Steven Pinney, a seventh-grade writing teacher in California, developed Kids 2 Kids, a learning program carried on both state and national electronic mail networks. Rooted in the notion that students best develop skills that are useful to them, Kids 2 Kids pairs students with distant writing partners for several writing projects, each of which requires both an anthology and a newsletter written and produced by students. Al Rogers, executive director of the FrEd Mail Foundation, suggests that e-mail may be the most practical context for learning writing skills since, he believes, “electronic text may be the last frontier for written discourse in the age of information.”

Similarly, John Wollstein, a former foreign language specialist for the Hawaii Department of Education, wondered, “How motivated are students to know that ten years from now they’re going to use their French in a French restaurant?” Realizing that using foreign language skills in a realistic context immediately would be far more motivating, Wollstein started a program that now has equipped every one of the 250 schools in the state with a “Luma Phone” -- a speaker phone that also can send and receive still video pictures of the speakers. After using this “Teleclass” setup to converse with kids in Japan, students in high school teacher June Kuwubara’s Japanese classes show the gains Wollstein aimed for: “A high school student’s greatest fear in class is to make a mistake in front of his peers,” she says. “But I’ve noticed that students who do the Teleclass exchanges no longer have this fear.”[173]

While these examples of socialization by way of telelearning come out of current classroom experiences, there’s nothing about these technological opportunities that requires the mediation or even existence of a school. Rather, they represent intermediate steps toward the dissolution of school-as-box altogether. The point is that learning through such HL media is not merely compatible with the goal of socialization --rather, institutions that deny or obstruct access to such media create a context for socializing that is downright contrary to the mores and competencies of a knowledge age society.

Still, many parents and others will ask, as a friend of mine did: How can HL technology replace the function of schools to teach children the basics of living values: work, ethics, morality, wisdom, character, strength, courage, civics, sacrifice, self-control, self-reliance, love, life, and so forth?

The answer is that the young will learn such virtues and behaviors the same they always have: from observing and adapting to the normal behaviors of their parents and the other adults who comprise the society they grow into. The question itself is rooted in the myth that schools are engines of culture. What social science knows is that schools are no more than expressions of culture. When communities of families are free to choose or control the schools that serve their children, the school staff are likely to demonstrate and encourage behaviors that are consistent with the community standards.[174] Schools reflecting cultural norms is a far cry from schools producing values and virtues.

The impotence of schools as culture factories is glaringly demonstrated by the experience of the defunct Soviet empire. A public education system backed by a cloak of censorship and the sinister threats of the secret police, gulags, and “psychiatric” institutions of a ruthlessly totalitarian regime worked for half to three-quarters of a century to manufacture a population imbued with a dogmatically definite set of socialist virtues, including atheism, internationalism, socialist realism, and disdain for material acquisitiveness. The effort failed utterly: Within days or even hours of the release from state repression, churches were filled, national banners were unfurled, “modern” artists burst from the woodwork, and the unvarnished greed of the Communist nomenklatura was exposed to public display.

What schooling cannot depose it cannot impose either. Cultures, values, ethics, and mores are created and renewed by communities of people, not by schools. Socialization is a function of societies, not of classrooms.

Beyond Mythology

Considering the incorrect assumptions we bring to teaching and learning and the new realities science has uncovered about the mismatch between traditional school instruction and the requirements for effective learning, it’s no wonder that the learning environment still found most frequently in today’s classrooms -- where teachers largely dictate abstracted knowledge to students only passively involved in the learning process -- has produced results that are disappointing to policymakers, parents, students, teachers, employers, and society at large.

The reality behind the myths is that “school” -- as a technology, as an institution, as a process -- simply does not work as a way to organize learning for living in the real world. Schooling and learning are at odds -- more of one means less of the other.

Science reveals that schooling is disconnected from the needs of working and living -- and HL technology is rapidly widening that disconnection to an unbridgeable chasm. That disconnection of school from real life is a matter of inherently inappropriate system design, not of curriculum or training or equipment.

More schooling -- longer days, more days, more years -- cannot be a solution to the conflict between school teaching and real learning, and promises only to make all the problems of that conflict worse. Similarly, reforms pursuing academic “excellence” -- trying to impose the myths of effective schooling with even greater rigor -- are doomed to be counterproductive exercises in “suboptimization,” a term economist Kenneth Boulding once defined as “trying to do well what should not be done at all.”

The good news is that science shows the way -- and HL technology now provides the means -- to empower every learner to learn whatever is needed to realize that person’s real-life goals, and to do so with passion and confidence instead of drudgery and fear. But the ecology of the new hyperlearning enterprise that is needed -- and is already emerging -- to replace the outworn infrastructure of schooling will bear little resemblance to what most people conventionally think of as “education.”

In the new, posteducation HL enterprise, learning is not only for the real world, it is of the real world. Not sequestered in the box of a classroom, learning takes place as close as possible to the real-life contexts in which people want learning to be useful. The hyperlearning enterprise is a wide-open community of practice, where learning is by doing, the roles of apprentice and expert are continually shifting with the demands of the problem at hand, learning is self-paced and custom-styled by the individual learner, and passionate -- sometimes “spectacular” -- learning is motivated by the natural drive of the human brain freed of the fear of failure.

Even as a highly cooperative community of practice, the new hyperlearning enterprise does not exclude the reality and importance of competition. As in most sports, cooperation and teamwork are means to competitiveness. HL provides opportunities for both group and individual competition. Competitive activities give learners the chance to identify strengths and weaknesses, and more specifically, to spot errors and improve performance.

But competition in the new hyperlearning enterprise is not in the contrived academic framework of tests and grades. Instead, learning to meet the demands of real-life competition and performance takes place in real-life contexts. For instance, one of Eckert’s key recommendations is that so-called extracurricular activities such as sports should be anchored in real, nonacademic community organizations rather than bound to the pseudocommunity of the school campus. Students will be better prepared for the rough-and-tumble of business competition by participating in an organization like Junior Achievement -- which engages young students in actually planning, creating, and operating a real business -- or developing their basic and “higher order” skills through telecourses while working in a real workplace, than they will by competing for grades on a paper-and-pencil economics test in a classroom.

The gathering momentum of hyperlearning technology, guided by the revealed truth of the science of learning, spells the inevitable extinction of schooling and the turgid bureaucracy of education. But how this transformation is achieved will make a great difference in its ultimate cost and value to current generations of students, families, employers, and taxpayers.

Technology is power and power means politics. The education establishment is fighting to preserve the status quo. Most education “reformers” are working to reinforce academia, not to retire it. The new learning enterprise demands a new politics of learning.[175]

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[128]. Ellen M. Pechman, The Child as Meaning Maker: The Organizing Theme for Professional Practice Schools, unpublished paper commissioned by American Federation of Teachers, 1990.

[129]. John Seeley Brown, Allan Collins, and Paul Duguid, "Situated Cognition in the Culture of Learning," Educational Researcher (Jan.-Feb. 1989).

[130]. Sylvia Farnham-Diggory, Schooling (Cambridge, MA: Harvard University Press, 1990).

[131]. A. Collins, J.S. Brown, and S. Newman, "Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics," in L.B. Resnick, ed., Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser (Hillsdale, NJ: Erlbaum, 1989).

[132]. C.P. Childs and P.M. Greenfield, "Informal Modes of Learning and Teaching: The Case of the Zinacanteco Learning," in N. Warren, ed., Studies in Cross-Cultural Psychology, Vol. 2 (New York: Academic Press, 1980).

[133]. Al Shanker, American Federation of Teachers editorial advertisement, The New York Times (7/8/90). See also T.N. Carraher, D.W. Carraher, and A.D. Schliemann, "Mathematics in the Streets and in Schools," British Journal of Developmental Psychology (3, 1985); and Thomas Sticht, "Adult Literacy Education," in E.Z. Rothkopf, ed., Review of Research in Education, 1988-89, Vol. 15 (Washington, DC: American Educational Research Association, 1989).

[134]. A general discussion of this issue is in Howard Gardner, The Unschooled Mind (New York: Basic Books, 1991). Also see: J. Clement, "Student Preconceptions of Introductory Mechanics," American Journal of Physics (No. 50, 1982); M. McCloskey, A. Caramazza, and B. Green, "Curvilinear Motion in the Absence of External Forces: Naive Beliefs about the Motion of Objects," Science (No. 210, 1980); and A.A. diSessa, "Phenomenology and the Evolution of Intuition," in D. Gentner and A. Stevens, eds., Mental Models (Hillsdale, NJ: Erlbaum, 1983).

[135]. Lauren Resnick, "Learning in School and Out," Educational Researcher (XVI, 9; 1983).

[136]. James Herndon, "How to Survive in Your Native Land (New York: Simon & Schuster, 1971); as quoted in Jean Lave, Cognition in Practice (Cambridge: Cambridge University Press, 1988).

[137]. Roy D. Pea, Socializing the Knowledge Transfer Problem, Report No. IRL89-0009 (Palo Alto, CA: Institute for Research on Learning, 1989).

[138]. M. Pressley, B.L. Snyder, and T. Cariaglia-Bull, "How Can Good Strategy Use Be Taught to Children?" in S.M. Cormier and J.D. Hagman, eds., Transfer of Learning (New York: Academic Press, 1987); D.N. Perkins and Gavriel Salomon, "Are Cognitive Skills Context-Bound?" Educational Researcher (Jan.-Feb. 1989). See also J.R. Hayes and H.A. Simon, "Psychological Differences among Problem Isomorphs," in N.J. Castellian, Jr., D.B. Pisone, and G.R. Potts, eds., Cognitive Theory (Hillsdale, NJ: Erlbaum, 1977).

[139]. Jim Schefter, "Air Traffic Training Gets Real," Popular Science (July 1991).

[140]. "Coming: Embedded Training Systems," Electronics (12/17/91); Greg Kearsley, "Embedded Training: The New Look of Computer-Based Instruction," Machine-Mediated Learning (V. 1, No. 3; 1985).

[141]. Jean Lave, Cognition in Practice (Cambridge: Cambridge University Press, 1988).

[142]. Jean Lave, Cognition in Practice (Cambridge: Cambridge University Press, 1988).

[143]. See, for instance, Anthony P. Carnevale, Leila J. Gainer, and Ann S. Meltzer, Workplace Basics: The Skills Employers Want (Alexandria, VA: American Society for Training and Development, 1988).

[144]. M. Hass, "Cognition-in-Context: The Social Nature of the Transformation of Mathematical Knowledge in a Third Grade Classroom," Social Relations Graduate Program, University of California, Irvine (no date); Jean Lave, Steven Smith, and Michael Butler, "Problem Solving in Everyday Practice," in Learning Mathematical Problem Solving, Report No. IRL88-0006 (Palo Alto, CA: Institute for Research on Learning, 1988).

[145]. Sylvia Farnham-Diggory Schooling (Cambridge, MA: Harvard University Press, 1990).

[146]. Jean Lave, Steven Smith, and Michael Butler, "Problem Solving as an Everyday Practice," in Learning Mathematical Problem Solving, Report No. IRL88-0006 (Palo Alto, CA: Institute for Research on Learning, 1988).

[147]. Senta A. Raizen, Reforming Education for Work: A Cognitive Science Perspective (Berkeley, CA: National Center for Research in Vocational Education, 1989); Barbara Y. White, "Sources of Difficulty in Understanding Newtonian Dynamics," Cognitive Science (7, 1983).

[148]. Roy D. Pea, Socializing the Knowledge Transfer Problem, Report No. IRL89-0009 (Palo Alto, CA: Institute for Research on Learning, 1989); J.D. Bransford, B.S. Stein, R. Arbitman-Smith, and N.J. Vye, "Three Approaches to Improving Thinking and Learning Skills," in S.F. Chipman, J.W. Segal, and R. Glaser, eds., Thinking and Learning Skills, Vol. 1 (Hillsdale, NJ: Erlbaum, 1985).

[149]. Gregory Bateson, Steps to an Ecology of Mind (New York: Ballantine, 1972).

[150]. Thomas Kuhn, The Structure of Scientific Revolutions (Chicago: University of Chicago Press, 1970).

[151]. Brigitte Jordan, Modes of Teaching and Learning: Questions Raised by the Training of Traditional Birth Attendants, Report No. IRL87-0004 (Palo Alto, CA: Institute for Research on Learning, 1987); Jean Lave, Steven Smith, and Michael Butler, "Problem Solving as an Everyday Practice," in Learning Mathematical Problem Solving, Report No. IRL88-0006 (Palo Alto, CA: Institute for Research on Learning, 1988); Jean Lave and Etienne Wenger, Situated Learning: Legitimate Peripheral Participation, IRL Report No. IRL90-0013 (Palo Alto, CA: Institute for Research on Learning, Feb. 1990).

[152]. Jean Lave and Etienne Wenger, Situated Learning: Legitimate Peripheral Participation, IRL Report No. IRL90-0013 (Palo Alto, CA: Institute for Research on Learning, Feb. 1990).

[153]. Employment and Training Administration, Work-Based Learning: Training America's Workers (Washington, DC: U.S. Dept. of Labor, Nov. 1989).

[154]. "Making a Mint on Mario," The Washington Post (12/15/91).

[155]. U.S. Bureau of the Census, Statistical Abstract of the United States 1991 (Washington, DC: 1991).

[156]. July 3, 1989; p. 54.

[157]. "The Ways We Learn," The Washington Post (10/7/91).

[158]. Jean Lave, Cognition in Practice (Cambridge: Cambridge University Press, 1988).

[159]. T.N. Carraher, D.W. Carraher, and P. Duguid, "Situated Cognition and the Culture of Learning," Educational Researcher (Jan.-Feb. 1989).

[160]. Sylvia Scribner and E. Fahrmeir, Practical and Theoretical Arithmetic: Some Preliminary Findings, Industrial Literacy Project, Working Paper No. 3 (New York: City University of New York, Graduate Center, 1982).

[161]. M. Mitchell Waldrop, "The Necessity of Knowledge," Science (3/23/84).

[162]. Gregory Bateson, Steps to an Ecology of Mind (New York: Ballantine, 1972).

[163]. E.D. Hirsch, Jr., Joseph F. Kett, and James Trefil, The Dictionary of Cultural Literacy (Boston: Houghton Mifflin, 1991).

[164]. James Burke, Connections (Boston: Little Brown, 1978).

[165]. Edward B. Fiske, Smart Schools, Smart Kids: Why Do Some Schools Work? (New York: Simon & Schuster, 1991).

[166]. Henry M. Levin and Gail Meister, "Is CAI Cost-Effective?" Phi Delta Kappan (June 1986).

[167]. "Scandal Over Cheating At M.I.T. Stirs Debate On Limits of Teamwork," The New York Times (5/22/91).

[168]. Nathan Keyfitz, "Putting Trained Labour Power to Work: The Dilemma of Education and Employment," Bulletin of Indonesian Economic Studies, December 1989.

[169]. Penelope Eckert Jocks & Burnouts (New York: Teachers College Press, 1989).

[170]. Susan McGee Bailey, How Schools Shortchange Girls , report commissioned by American Association of University Women (Wellesley, MA: Wellesley College Center for Research on Women, 1992).

[171]. Penelope Eckert, Adolescent Social Categories, Information, and Science Learning, IRL Report No. IRL89-0012 (Palo Alto, CA: Institute for Research on Learning, Mar. 1989).

[172]. "Video Angst," The Washington Post (12/10/90); "Mario's A Big Man On Campus," The Washington Post (3/25/92).

[173]. Therese Margeau, "Teaching and Learning On-line," Electronic Learning (Nov./Dec. 1990).

[174]. See James S. Coleman and Thomas Hoffer, Public and Private High Schools: The Impact of Communities (New York: Basic Books, 1987); James S. Coleman, Thomas Hoffer, and Sally Kilgore, High School Achievement: Public, Catholic, and Private Schools Compared (Basic Books, 1982). Also, John E. Chubb and Terry M. Moe, Politics, Markets & America's Schools (Washington, DC: Brookings Institution, 1990).

[175]. This chapter was based on extensive material contributed by Sue Berryman, director of the Institute for Education and the Economy at Columbia University. The material also benefitted from the editorial assistance of Janet Topolsky.