How People Learn:
Brain, Mind,
Experience, and School
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Learning: From Speculation to
Science
The essence of matter,
the origins of the universe, the nature of the human mind--these are the
profound questions that have engaged thinkers through the centuries.
Until quite recently, understanding the mind--and the thinking and
learning that the mind makes possible--has remained an elusive quest, in
part because of a lack of powerful research tools. Today, the world is
in the midst of an extraordinary outpouring of scientific work on the
mind and brain, on the processes of thinking and learning, on the neural
processes that occur during thought and learning, and on the development
of competence.
The revolution in the
study of the mind that has occurred in the last three or four decades
has important implications for education. As we illustrate, a new
theory of learning is coming into focus that leads to very different
approaches to the design of curriculum, teaching, and assessment than
those often found in schools today. Equally important, the growth of
interdisciplinary inquiries and new kinds of scientific collaborations
have begun to make the path from basic research to educational practice
somewhat more visible, if not yet easy to travel. Thirty years ago,
educators paid little attention to the work of cognitive scientists, and
researchers in the nascent field of cognitive science worked far removed
from classrooms. Today, cognitive researchers are spending more time
working with teachers, testing and refining their theories in real
classrooms where they can see how different settings and classroom
interactions influence applications of their theories.
What is perhaps
currently most striking is the variety of research approaches and
techniques that have been developed and ways in which evidence from many
different branches of science are beginning to converge. The story we
can now tell about learning is far richer than ever before, and it
promises to evolve dramatically in the next generation. For example:
- Research from cognitive psychology has increased understanding
of the nature of competent performance and the principles of knowledge
organization that underlie people's abilities to solve problems in a
wide variety of areas, including mathematics, science, literature,
social studies, and history.
- Developmental researchers have shown that young children
understand a great deal about basic principles of biology and physical
causality, about number, narrative, and personal intent, and that these
capabilities make it possible to create innovative curricula that
introduce important concepts for advanced reasoning at early ages.
- Research on learning and transfer has uncovered important
principles for structuring learning experiences that enable people to
use what they have learned in new settings.
- Work in social psychology, cognitive psychology, and
anthropology is making clear that all learning takes place in settings
that have particular sets of cultural and social norms and expectations
and that these settings influence learning and transfer in powerful
ways.
- Neuroscience is beginning to provide evidence for many
principles of learning that have emerged from laboratory research, and
it is showing how learning changes the physical structure of the brain
and, with it, the functional organization of the brain.
- Collaborative studies of the design and evaluation of learning
environments, among cognitive and developmental psychologists and
educators, are yielding new knowledge about the nature of learning and
teaching as it takes place in a variety of settings. In addition,
researchers are discovering ways to learn from the "wisdom of practice"
that comes from successful teachers who can share their expertise.
- Emerging technologies are leading to the development of many new
opportunities to guide and enhance learning that were unimagined even a
few years ago.
All of these
developments in the study of learning have led to an era of new
relevance of science to practice. In short, investment in basic
research is paying off in practical applications. These developments in
understanding of how humans learn have particular significance in light
of changes in what is expected of the nation's educational systems.
In the early part of
the twentieth century, education focused on the acquisition of literacy
skills: simple reading, writing, and calculating. It was not the
general rule for educational systems to train people to think and read
critically, to express themselves clearly and persuasively, to solve
complex problems in science and mathematics. Now, at the end of the
century, these aspects of high literacy are required of almost everyone
in order to successfully negotiate the complexities of contemporary
life. The skill demands for work have increased dramatically, as has
the need for organizations and workers to change in response to
competitive workplace pressures. Thoughtful participation in the
democratic process has also become increasingly complicated as the locus
of attention has shifted from local to national and global concerns.
Above all, information
and knowledge are growing at a far more rapid rate than ever before in
the history of humankind. As Nobel laureate Herbert Simon wisely
stated, the meaning of "knowing" has shifted from being able to remember
and repeat information to being able to find and use it (Simon, 1996).
More than ever, the sheer magnitude of human knowledge renders its
coverage by education an impossibility; rather, the goal of education is
better conceived as helping students develop the intellectual tools and
learning strategies needed to acquire the knowledge that allows people
to think productively about history, science and technology, social
phenomena, mathematics, and the arts. Fundamental understanding about
subjects, including how to frame and ask meaningful questions about
various subject areas, contributes to individuals' more basic
understanding of principles of learning that can assist them in becoming
self-sustaining, lifelong learners.
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FOCUS: PEOPLE, SCHOOLS, AND THE POTENTIAL TO
LEARN |
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The scientific
literatures on cognition, learning, development, culture, and brain are
voluminous. Three organizing decisions, made fairly early in the work
of the committee, provided the framework for our study and are reflected
in the contents of this book.
- First, we focus primarily on research on human learning (though
the study of animal learning provides important collateral information),
including new developments from neuroscience.
- Second, we focus especially on learning research that has
implications for the design of formal instructional environments,
primarily preschools, kindergarten through high schools (K-12), and
colleges.
- Third, and related to the second point, we focus on research
that helps explore the possibility of helping all individuals achieve
their fullest potential.
New ideas about ways to
facilitate learning--and about who is most capable of learning--can
powerfully affect the quality of people's lives. At different points in
history, scholars have worried that formal educational environments have
been better at selecting talent than developing it (see, e.g., Bloom,
1964). Many people who had difficulty in school might have prospered if
the new ideas about effective instructional practices had been
available. Furthermore, given new instructional practices, even those
who did well in traditional educational environments might have
developed skills, knowledge, and attitudes that would have significantly
enhanced their achievements.
Learning research
suggests that there are new ways to introduce students to traditional
subjects, such as mathematics, science, history and literature, and that
these new approaches make it possible for the majority of individuals to
develop a deep understanding of important subject matter. This
committee is especially interested in theories and data that are
relevant to the development of new ways to introduce students to such
traditional subjects as mathematics, science, history, and literature.
There is hope that new approaches can make it possible for a majority of
individuals to develop a moderate to deep understanding of important
subjects.
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DEVELOPMENT OF THE SCIENCE OF LEARNING |
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This report builds on
research that began in the latter part of the nineteenth century--the
time in history at which systematic attempts were made to study the
human mind through scientific methods. Before then, such study was the
province of philosophy and theology. Some of the most influential early
work was done in Leipzig in the laboratory of Wilhelm Wundt, who with
his colleagues tried to subject human consciousness to precise
analysis--mainly by asking subjects to reflect on their thought
processes through introspection.
By the turn of the
century, a new school of behaviorism was emerging. In reaction to the
subjectivity inherent in introspection, behaviorists held that the
scientific study of psychology must restrict itself to the study of
observable behaviors and the stimulus conditions that control them. An
extremely influential article, published by John B. Watson in 1913,
provides a glimpse of the behaviorist credo:
. . . all schools of psychology except that of
behaviorism claim that "consciousness" is the subject-matter of
psychology. Behaviorism, on the contrary, holds that the subject matter
of human psychology is the behavior or activities of the human being.
Behaviorism claims that "consciousness" is neither a definable nor a
useable concept; that it is merely another word for the "soul" of more
ancient times. The old psychology is thus dominated by a kind of subtle
religious philosophy (p. 1).
Drawing on the
empiricist tradition, behaviorists conceptualized learning as a process
of forming connections between stimuli and responses. Motivation to
learn was assumed to be driven primarily by drives, such as hunger, and
the availability of external forces, such as rewards and punishments
(e.g., Thorndike, 1913; Skinner, 1950).
In a classic
behaviorist study by Edward L. Thorndike (1913), hungry cats had to
learn to pull a string hanging in a "puzzle box" in order for a door to
open that let them escape and get food. What was involved in learning
to escape in this manner? Thorndike concluded that the cats did not
think about how to escape and then do it; instead, they engaged in
trial-and-error behavior; see Box
1.1. Sometimes a cat in the puzzle box accidentally pulled the
strings while playing and the door opened, allowing the cat to escape.
But this event did not appear to produce an insight on the part of the
cat because, when placed in the puzzle box again, the cat did not
immediately pull the string to escape. Instead, it took a number of
trials for the cats to learn through trial and error. Thorndike argued
that rewards (e.g., food) increased the strength of connections between
stimuli and responses. The explanation of what appeared to be complex
problem-solving phenomena as escaping from a complicated puzzle box
could thus be explained without recourse to unobservable mental events,
such as thinking.
A limitation of early
behaviorism stemmed from its focus on observable stimulus conditions and
the behaviors associated with those conditions. This orientation made
it difficult to study such phenomena as understanding, reasoning, and
thinking--phenomena that are of paramount importance for education.
Over time, radical behaviorism (often called "Behaviorism with a Capital
B") gave way to a more moderate form of behaviorism ("behaviorism with a
small b") that preserved the scientific rigor of using behavior as data,
but also allowed hypotheses about internal "mental" states when these
became necessary to explain various phenomena (e.g., Hull, 1943; Spence,
1942).
In the late 1950s, the
complexity of understanding humans and their environments became
increasingly apparent, and a new field emerged--cognitive science. From
its inception, cognitive science approached learning from a
multidisciplinary perspective that included anthropology, linguistics,
philosophy, developmental psychology, computer science, neuroscience,
and several branches of psychology (Norman, 1980,1993; Newell and Simon,
1972). New experimental tools, methodologies, and ways of postulating
theories made it possible for scientists to begin serious study of
mental functioning: to test their theories rather than simply speculate
about thinking and learning (see, e.g., Anderson, 1982, 1987; deGroot,
1965,1969; Newell and Simon, 1972; Ericsson and Charness, 1994), and, in
recent years, to develop insights into the importance of the social and
cultural contexts of learning (e.g., Cole, 1996; Lave, 1988; Lave and
Wenger, 1991; Rogoff, 1990; Rogoff et al., 1993). The introduction of
rigorous qualitative research methodologies have provided perspectives
on learning that complement and enrich the experimental research
traditions (Erickson, 1986; Hammersly and Atkinson, 1983; Heath, 1982;
Lincoln and Guba, 1985; Marshall and Rossman, 1955; Miles and Huberman,
1984; Spradley, 1979).
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Learning with Understanding |
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One of the hallmarks of
the new science of learning is its emphasis on learning with
understanding. Intuitively, understanding is good, but it has been
difficult to study from a scientific perspective. At the same time,
students often have limited opportunities to understand or make sense of
topics because many curricula have emphasized memory rather than
understanding. Textbooks are filled with facts that students are
expected to memorize, and most tests assess students' abilities to
remember the facts. When studying about veins and arteries, for
example, students may be expected to remember that arteries are thicker
than veins, more elastic, and carry blood from the heart; veins carry
blood back to the heart. A test item for this information may look like
the following:
1. Arteries
a. Are more elastic than veins
b. Carry blood that is pumped from the heart
c. Are less elastic than veins
d. Both a and b
e. Both b and c
The new science of
learning does not deny that facts are important for thinking and problem
solving. Research on expertise in areas such as chess, history,
science, and mathematics demonstrate that experts' abilities to think
and solve problems depend strongly on a rich body of knowledge about
subject matter (e.g., Chase and Simon, 1973; Chi et al., 1981; deGroot,
1965). However, the research also shows clearly that "usable knowledge"
is not the same as a mere list of disconnected facts. Experts'
knowledge is connected and organized around important concepts (e.g.,
Newton's second law of motion); it is "conditionalized" to specify the
contexts in which it is applicable; it supports understanding and
transfer (to other contexts) rather than only the ability to remember.
For example, people who
are knowledgeable about veins and arteries know more than the facts
noted above: they also understand why veins and arteries have
particular properties. They know that blood pumped from the heart exits
in spurts and that the elasticity of the arteries helps accommodate
pressure changes. They know that blood from the heart needs to move
upward (to the brain) as well as downward and that the elasticity of an
artery permits it to function as a one-way valve that closes at the end
of each spurt and prevents the blood from flowing backward. Because
they understand relationships between the structure and function of
veins and arteries, knowledgeable individuals are more likely to be able
to use what they have learned to solve novel problems--to show evidence
of transfer. For example, imagine being asked to design an artificial
artery--would it have to be elastic? Why or why not? An understanding
of reasons for the properties of arteries suggests that elasticity may
not be necessary--perhaps the problem can be solved by creating a
conduit that is strong enough to handle the pressure of spurts from the
heart and also function as a one-way valve. An understanding of veins
and arteries does not guarantee an answer to this design question, but
it does support thinking about alternatives that are not readily
available if one only memorizes facts (Bransford and Stein, 1993).
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Pre-Existing Knowledge |
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An emphasis on
understanding leads to one of the primary characteristics of the new
science of learning: its focus on the processes of knowing (e.g.,
Piaget, 1978; Vygotsky, 1978). Humans are viewed as goal-directed
agents who actively seek information. They come to formal education
with a range of prior knowledge, skills, beliefs, and concepts that
significantly influence what they notice about the environment and how
they organize and interpret it. This, in turn, affects their abilities
to remember, reason, solve problems, and acquire new knowledge.
Even young infants are
active learners who bring a point of view to the learning setting. The
world they enter is not a "booming, buzzing confusion" (James, 1890),
where every stimulus is equally salient. Instead, an infant's brain
gives precedence to certain kinds of information: language, basic
concepts of number, physical properties, and the movement of animate and
inanimate objects. In the most general sense, the contemporary view of
learning is that people construct new knowledge and understandings based
on what they already know and believe (e.g., Cobb, 1994; Piaget, 1952,
1973a,b, 1977, 1978; Vygotsky, 1962, 1978). A classic children's book
illustrates this point; see Box
1.2.
A logical extension of
the view that new knowledge must be constructed from existing knowledge
is that teachers need to pay attention to the incomplete understandings,
the false beliefs, and the naive renditions of concepts that learners
bring with them to a given subject. Teachers then need to build on
these ideas in ways that help each student achieve a more mature
understanding. If students' initial ideas and beliefs are ignored, the
understandings that they develop can be very different from what the
teacher intends.
Consider the challenge
of working with children who believe that the earth is flat and
attempting to help them understand that it is spherical. When told it
is round, children picture the earth as a pancake rather than as a
sphere (Vosniadou and Brewer, 1989). If they are then told that it is
round like a sphere, they interpret the new information about a
spherical earth within their flat-earth view by picturing a pancake-like
flat surface inside or on top of a sphere, with humans standing on top
of the pancake. The children's construction of their new understandings
has been guided by a model of the earth that helped them explain how
they could stand or walk upon its surface, and a spherical earth did not
fit their mental model. Like Fish Is Fish, everything the
children heard was incorporated into that pre-existing view.
Fish Is Fish is
relevant not only for young children, but for learners of all ages. For
example, college students often have developed beliefs about physical
and biological phenomena that fit their experiences but do not fit
scientific accounts of these phenomena. These preconceptions must be
addressed in order for them to change their beliefs (e.g., Confrey,
1990; Mestre, 1994; Minstrell, 1989; Redish, 1996).
A common misconception
regarding "constructivist" theories of knowing (that existing knowledge
is used to build new knowledge) is that teachers should never tell
students anything directly but, instead, should always allow them to
construct knowledge for themselves. This perspective confuses a theory
of pedagogy (teaching) with a theory of knowing. Constructivists assume
that all knowledge is constructed from previous knowledge, irrespective
of how one is taught (e.g., Cobb, 1994)--even listening to a lecture
involves active attempts to construct new knowledge. Fish Is
Fish (Lionni, 1970) and attempts to teach children that the earth is
round (Vosniadou and Brewer, 1989) show why simply providing lectures
frequently does not work. Nevertheless, there are times, usually after
people have first grappled with issues on their own, that "teaching by
telling" can work extremely well (e.g., Schwartz and Bransford, in
press). However, teachers still need to pay attention to students'
interpretations and provide guidance when necessary.
There is a good deal of
evidence that learning is enhanced when teachers pay attention to the
knowledge and beliefs that learners bring to a learning task, use this
knowledge as a starting point for new instruction, and monitor students'
changing conceptions as instruction proceeds. For example, sixth
graders in a suburban school who were given inquiry-based physics
instruction were shown to do better on conceptual physics problems than
eleventh and twelfth grade physics students taught by conventional
methods in the same school system. A second study comparing
seventh-ninth grade urban students with the eleventh and twelfth grade
suburban physics students again showed that the younger students, taught
by the inquiry-based approach, had a better grasp of the fundamental
principles of physics (White and Frederickson, 1997, 1998). New
curricula for young children have also demonstrated results that are
extremely promising: for example, a new approach to teaching geometry
helped second-grade children learn to represent and visualize
three-dimensional forms in ways that exceeded the skills of a comparison
group of undergraduate students at a leading university (Lehrer and
Chazan, 1998). Similarly, young children have been taught to
demonstrate powerful forms of early geometry generalizations (Lehrer and
Chazan, 1998) and generalizations about science (Schauble et al., 1995;
Warren and Rosebery, 1996).
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Active Learning |
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New developments in the
science of learning also emphasize the importance of helping people take
control of their own learning. Since understanding is viewed as
important, people must learn to recognize when they understand and when
they need more information. What strategies might they use to assess
whether they understand someone else's meaning? What kinds of evidence
do they need in order to believe particular claims? How can they build
their own theories of phenomena and test them effectively?
Many important
activities that support active learning have been studied under the
heading of "metacognition," a topic discussed in more detail in Chapters 2 and 3.
Metacognition refers to people's abilities to predict their performances
on various tasks (e.g., how well they will be able to remember various
stimuli) and to monitor their current levels of mastery and
understanding (e.g., Brown, 1975; Flavell, 1973). Teaching practices
congruent with a metacognitive approach to learning include those that
focus on sense-making, self-assessment, and reflection on what worked
and what needs improving. These practices have been shown to increase
the degree to which students transfer their learning to new settings and
events (e.g., Palincsar and Brown, 1984; Scardamalia et al., 1984;
Schoenfeld, 1983, 1985, 1991).
Imagine three teachers
whose practices affect whether students learn to take control of their
own learning (Scardamalia and Bereiter, 1991). Teacher A's goal is to
get the students to produce work; this is accomplished by supervising
and overseeing the quantity and quality of the work done by the
students. The focus is on activities, which could be anything from
old-style workbook activities to the trendiest of space-age projects.
Teacher B assumes responsibility for what the students are learning as
they carry out their activities. Teacher C does this as well, but with
the added objective of continually turning more of the learning process
over to the students. Walking into a classroom, you cannot immediately
tell these three kinds of teachers apart. One of the things you might
see is the students working in groups to produce videos or multimedia
presentations. The teacher is likely to be found going from group to
group, checking how things are going and responding to requests. Over
the course of a few days, however, differences between Teacher A and
Teacher B would become evident. Teacher A's focus is entirely on the
production process and its products--whether the students are engaged,
whether everyone is getting fair treatment, and whether they are turning
out good pieces of work. Teacher B attends to all of this as well, but
Teacher B is also attending to what the students are learning from the
experience and is taking steps to ensure that the students are
processing content and not just dealing with show. To see a difference
between Teachers B and C, however, you might need to go back into the
history of the media production project. What brought it about in the
first place? Was it conceived from the start as a learning activity, or
did it emerge from the students' own knowledge building efforts? In one
striking example of a Teacher C classroom, the students had been
studying cockroaches and had learned so much from their reading and
observation that they wanted to share it with the rest of the school;
the production of a video came about to achieve that purpose (Lamon et
al., 1997).
The differences in what
might seem to be the same learning activity are thus quite profound. In
Teacher A's classroom, the students are learning something of media
production, but the media production may very well be getting in the way
of learning anything else. In Teacher B's classroom, the teacher is
working to ensure that the original educational purposes of the activity
are met, that it does not deteriorate into a mere media production
exercise. In Teacher C's classroom, the media production is continuous
with and a direct outgrowth of the learning that is embodied in the
media production. The greater part of Teacher C's work has been done
before the idea of a media production even comes up, and it remains only
to help the students keep sight of their purposes as they carry out the
project.
These hypothetical
teachers--A, B, and C--are abstract models that of course fit real
teachers only partly, and more on some days than others. Nevertheless,
they provide important glimpses of connections between goals for
learning and teaching practices that can affect students' abilities to
accomplish these goals.
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Implications for Education |
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Overall, the new
science of learning is beginning to provide knowledge to improve
significantly people's abilities to become active learners who seek to
understand complex subject matter and are better prepared to transfer
what they have learned to new problems and settings. Making this happen
is a major challenge (e.g., Elmore et al., 1996), but it is not
impossible. The emerging science of learning underscores the importance
of rethinking what is taught, how it is taught, and how learning is
assessed. These ideas are developed throughout this report.
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An Evolving Science |
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This volume synthesizes
the scientific basis of learning. The scientific achievements include a
fuller understanding of: (1) memory and the structure of knowledge; (2)
problem solving and reasoning; (3) the early foundations of learning;
(4) regulatory processes that govern learning, including metacognition;
and (5) how symbolic thinking emerges from the culture and community of
the learner.
These key
characteristics of learned proficiency by no means plumb the depths of
human cognition and learning. What has been learned about the
principles that guide some aspects of learning do not constitute a
complete picture of the principles that govern all domains of learning.
The scientific bases, while not superficial in themselves, do represent
only a surface level of a complete understanding of the subject. Only a
few domains of learning have been examined in depth, as reflected in
this book, and new, emergent areas, such as interactive technologies
(Greenfield and Cocking, 1996) are challenging generalizations from
older research studies.
As scientists continue
to study learning, new research procedures and methodologies are
emerging that are likely to alter current theoretical conceptions of
learning, such as computational modeling research. The scientific work
encompasses a broad range of cognitive and neuroscience issues in
learning, memory, language, and cognitive development. Studies of
parallel distributed processing, for example (McClelland et al., 1995;
Plaut et al., 1996; Munakata et al., 1997; McClelland and Chappell,
1998) look at learning as occurring through the adaptation of
connections among participating neurons. The research is designed to
develop explicit computational models to refine and extend basic
principles, as well as to apply the models to substantive research
questions through behavioral experiments, computer simulations,
functional brain imaging, and mathematical analyses. These studies are
thus contributing to modification of both theory and practice. New
models also encompass learning in adulthood to add an important
dimension to the scientific knowledge base.
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OVERVIEW OF THE BOOK |
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Figure 1.1 illustrates the organization of this book.
This chapter (Chapter 1) represents the framework
of the committee's study. We then focus on what is known about learners
and learning (Chapters 2-5), followed by the implications of this research
for the design of effective learning environments, including roles for
technology, while emphasizing the key role of teachers (Chapters 6-9). We end with a
set of conclusions and recommendations for future research (Chapter 10).
It is important to keep
in mind that there has been a longer history of rigorous research on
issues of learners and learning (Chapters 2-5) than on the design of learning environments and
the implications of technology (Chapters 6-9). Research on classroom-based learning and
teacher learning (especially the many opportunities for informal
learning) often use newer qualitative methodologies, such as ethnography
and case-study analysis, to capture the richness of learning in context.
A rigorous methodology has been developed for conducting such studies
(e.g., see Erickson, 1986; Hammersly and Atkinson, 1983; Heath, 1982;
Lincoln and Guba, 1985; Marshall and Rossman, 1955; Miles and Huberman,
1984; Spradley, 1979).
Chapter 2, on expertise, discusses lessons learned
from studies of people who have become experts in areas such as chess,
physics, mathematics, or history. What is known about experts is
important not because all students are expected to become experts, but
because the knowledge of expertise provides valuable insights into what
the results of effective learning look like.
Chapter 3 moves from what is known about experts to
an examination of processes of learning that underlie effective
knowledge acquisition. Special emphasis is placed on understanding the
kinds of learning experiences that lead to transfer--the ability to use
what was learned in one setting to deal with new problems and events.
Chapter 4 extends the examination of learning to
infants and young children. Data show that children's early
competencies in areas such as causal relationships, numbers, and
language are much more sophisticated than was previously believed.
These competencies provide the foundations for important concepts and
ideas that children build on in later learning.
Chapter 5 explores new developments in neuroscience,
while providing some cautionary advice about a number of popular myths
that should not influence education. Neuroscience provides converging
evidence about processes of learning and development and enriches
understanding of learning by explicating the mechanisms by which
learning occurs.
Chapter 6 explores general principles for the design
of effective learning environments that are suggested by the science of
learning. It explores the degree to which environments are learner
centered, knowledge centered, assessment centered, and community
centered. These components must be brought into alignment in order for
effective change to occur.
Chapter 7 presents examples of effective teaching
practices that are consistent with new knowledge about learning. We
present contrasting illustrations of effective teaching in history,
mathematics, and science. Effective teaching practices vary across
subjects because knowledge in different subjects is organized
differently and based on different ways of knowing (epistemologies).
Chapter 8 explores teacher learning--which includes
both practicing teachers and college students studying to be teachers.
The science of learning has important implications for helping teachers
continue to learn throughout their lives.
Chapter 9 presents promising new developments in
technology that have the potential to provide new possibilities for
enhancing learning. We discuss data on technology and learning when
they exist, but we also discuss new possibilities that future research
should explore.
Chapter 10 concludes our study with a summary of
the major findings of the study on learners and learning, teachers and
teaching, and learning environments and recommends new areas of
research.
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