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4
Perspectives on Deeper Learning
T
his chapter returns to the discussion begun in Chapter 2 about the
nature of deeper learning and 21st century skills. It opens with an
introduction that includes a brief discussion of the goals of deeper
learning and a brief discussion of the history of theory and research on
transfer. The second and longest section of the chapter discusses cognitive
perspectives on deeper learning, reviewing work in cognitive and educa-
tional psychology in support of our argument that deeper learning is the
process of developing durable, transferable knowledge that can be applied
to new situations. In the third section, we offer an example of a learning
environment that promotes the processes of deeper learning and develops
cognitive, intrapersonal, and interpersonal competencies. In the fourth
and fifth sections, we discuss the intrapersonal and interpersonal domains,
considering how 21st century competencies in these two domains support
the process of deeper learning. The sixth section briefly discusses the im-
plications of the research reviewed throughout the chapter for teaching of
deeper learning and 21st century competencies, and the chapter ends with
conclusions.
A CLASSIC CONCERN: LEARNING FOR TRANSFER
The committee views the broad call for deeper learning and 21st cen-
tury skills as reflecting a long-standing issue in education and training--
the desire that individuals develop transferable knowledge and skills.
Associated with this is the challenge of creating learning environments that
support development of the cognitive, intrapersonal, and interpersonal
69
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70 EDUCATION FOR LIFE AND WORK
competencies that enable learners to transfer what they have learned
to new situations and new problems. These competencies include both
knowledge in a domain and knowledge of how, why, and when to apply
this knowledge to answer questions and solve problems--integrated forms
of knowledge that we refer to as 21st century competencies and discuss
further below.
If the goal of instruction is to prepare students to accomplish tasks
or solve problems exactly like the ones addressed during instruction, then
deeper learning is not needed. For example, if someone's job calls for add-
ing lists of numbers accurately, that individual needs to learn to become
proficient in using the addition procedure but does not need deeper learning
about the nature of number and number theory that will allow transfer to
new situations that involve the application of mathematical principles. As
discussed in the previous chapter, today's technology has reduced demand
for such routine skills (e.g., Autor, Levy, and Murnane, 2003). Success in
work and life in the 21st century is associated with cognitive, intrapersonal,
and interpersonal competencies that allow individuals to adapt effectively
to changing situations rather than to rely solely on well-worn procedures.
When the goal is to prepare students to be able to be successful in solv-
ing new problems and adapting to new situations, then deeper learning is
called for. Calls for such 21st century skills as innovation, creativity, and
creative problem solving can also be seen as calls for deeper learning--
helping students develop transferable knowledge that can be applied to
solve new problems or respond effectively to new situations. Before turning
to a discussion of the relationship between deeper learning and 21st century
competencies in terms of theories and research on learning and knowing
and the implications for transfer, we briefly discuss some of the rich history
of work on the nature and extent of transfer.
Brief Historical Overview of Theory and Research on Transfer
Transfer was one of the first topics on the research agendas of both psy-
chology and education, and it has remained as perhaps the central topic in
the research on learning and instruction for more than 100 years. Research
to date suggests that despite our desire for broad forms of transfer, knowl-
edge does not transfer very readily, but it also illuminates instructional
conditions that support forms of transfer that are desirable and attainable.
Specific transfer is the idea that learning A affects one's learning of B
only to the extent that A and B have elements in common. For example,
learning Latin may help someone learn Spanish solely because some of
the vocabulary words are very similar and the verb conjugations are very
similar. In contrast, general transfer is the idea that learning A affects one's
learning of B because learning A strengthens general characteristics or
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PERSPECTIVES ON DEEPER LEARNING 71
knowledge in the learner that are broadly relevant (such as mental disci-
pline or general principles). On the general transfer side of the controversy
was the doctrine of formal discipline, which held that learning certain
school subjects such as Latin and geometry would improve the mind in
general (i.e., teach proper habits of mind) and thereby improve learning
and performance in other unrelated subjects. On the specific transfer side
of the controversy was E.L. Thorndike, largely recognized as the founder
of educational psychology, who sought to put the issue to an empirical test.
In a famous set of early studies, Thorndike and Woodworth (1901) found
that students who were taught a cognitive skill showed a large improvement
on the taught tasks but not on other tasks. Thorndike was able to claim
strong support for specific rather than general transfer: "Improvement in
any single mental function rarely brings about equal improvement in any
other function, no matter how similar" (Thorndike, 1903, p. 91).
This was not a good outcome for those dedicated to helping students
develop the ability to exhibit general transfer--that is, to apply what they
have learned in one situation to a novel situation. Subsequent work by
Judd (1908) offered some hope by showing that transfer to new situations
depended on the instructional method used during initial learning, with
some instructional methods supporting transfer to new situations and oth-
ers not. An important aspect of Judd's finding is that transfer was restricted
to new situations that required the same general principles as required in
the original task, although it could be applied to situations requiring dif-
ferent behaviors.
Judd's finding has been replicated in many contexts. For example,
Singley and Anderson (1989) report on an experiment designed to study
the acquisition and transfer of skills in text editing. A group of 24 young
women (aged 18-30) from a secretarial school were first taught to use either
one or two line editors (text editing software used to change individual
lines of text) and then a screen editor (text editing software used to scroll
throughout a page of text), while control groups spent similar amounts of
time either learning and using one of the screen editors or simply typing
a manuscript. The authors observed positive transfer, both from one line
editor to the next and from the line editors to the screen editor, as indicated
by reductions in total learning time, keystrokes, residual errors, and other
measures in comparison to the control groups. They proposed that the very
high level of transfer from one line editor to the next line editor was due
to the fact that, although the surface features of the commands used in the
two editors were different, the underlying principles were nearly identical.
In addition, they proposed that the moderate level of transfer from the line
editors to the screen editor reflected the fact that the procedures used in the
two line editors are largely different from those used by the screen editor.
Nevertheless, the two line editors and the screen editor do share several
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72 EDUCATION FOR LIFE AND WORK
decision rules, enabling the moderate level of transfer. It is important to
note that this research examined transfer within a single subject or topic
area--text editing. Research to date has not found evidence of transfer
across subjects or disciplines.
Although there is little support in the research literature for general
transfer in the broadest sense, there is encouraging evidence for what could
be called "specific transfer of general principles" within a subject area or
topic when effective instructional methods are used. Understanding how to
promote this type of specific transfer is a continuing goal of research. Much
of contemporary work continues to follow a line of thinking originally
developed by the gestalt psychologists (e.g., Katona, 1942; Wertheimer,
1959) working in the first half of the 20th century. They were the first to
propose a distinction between reproductive thinking (i.e., applying a previ-
ously learned procedure to solve a new problem) and productive thinking
(i.e., inventing a new solution method to solve a new problem). Insight--
moving from a state of not knowing how to solve a problem to a state of
knowing how to solve it--is at the heart of productive thinking and was a
major research theme of gestalt psychology (Duncker, 1945; Mayer, 1995).
The gestaltists also emphasized the distinction between rote learning (which
involved learning to blindly follow a procedure) and meaningful learning
(which involved deeper understanding of the structure of the problem and
the solution method), and they provided evidence that meaningful learn-
ing leads to transfer, whereas rote learning does not (Katona, 1940). For
example, Wertheimer showed that in learning to solve for the area of a
parallelogram, students could be taught how to apply the formula area =
height × base (learning by rote), or they could be shown that they could
cut off a triangle from one end and place it on the other end to form a rect-
angle (learning by understanding). According to Wertheimer, both kinds of
instruction enabled students to perform well on problems like those given
during instruction (i.e., retention tests), but only learning by understanding
could promote problem solving on unusually shaped parallelograms and
related nonparallelogram shapes (i.e., transfer tests).
Overall, one of the continuing goals of research and theory is to elu-
cidate what is meant by learning with understanding--the processes that
produce such learning as well as the outcomes in terms of knowledge
representations--as well as how the products of such "deeper learning"
processes lead to productive thinking in the context of transfer situations
(see, e.g., Schwartz, Bransford, and Sears, 2005). In the next section, we
consider the relationship between deeper learning and 21st century skills
from the perspective of contemporary research and theory on the nature of
the mental structures and cognitive processes associated with learning as
well as the sociocultural nature of learning and knowing.
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PERSPECTIVES ON DEEPER LEARNING 73
THE RELATIONSHIP BETWEEN DEEPER LEARNING
AND COGNITIVE COMPETENCIES
To clarify the meaning of "deeper learning" and illuminate its relation-
ship to 21st century competencies in the cognitive domain, the committee
turned to two important strands of research and theory on the nature of hu-
man thinking and learning, the cognitive perspective and the sociocultural
perspective, also referred to as the "situated" perspective (Greeno, Pearson,
and Schoenfeld, 1996). In contrast to the differential perspective discussed
in Chapter 2, which focuses on differences among individuals in knowledge
or skill, the cognitive perspective focuses on types of knowledge and how
they are structured in an individual's mind, including the processes that
govern perception, learning, memory, and human performance. Research
from the cognitive perspective investigates the mechanisms of learning and
the nature of the products--the types of knowledge and skill--that result
from those mechanisms, as well as how that knowledge and skill is drawn
upon to perform a range of simple to complex tasks. The goal is theory
and models that apply to all individuals, accepting the fact that there will
be variation across individuals in execution of the processes and in the
resultant products.
The sociocultural perspective emerged in response to the perception
that research and theory within the cognitive perspective was too nar-
rowly focused on individual thinking and learning. In the sociocultural
perspective, learning takes place as individuals participate in the practices
of a community, using the tools, language, and other cultural artifacts of
the community. From this perspective, learning is "situated" within, and
emerges from, the practices in different settings and communities. A com-
munity may be large or small and may be located inside or outside of a
traditional school context. It might range, for example, from colleagues in
a company's Information Technology department to a single elementary
school classroom or a global society of plant biologists.
Such research has important implications for how academic disciplines
are taught in school. From the sociocultural perspective, the disciplines are
distinct communities that engage in shared practices of ongoing knowledge
creation, understanding, and revision. It is now widely recognized that sci-
ence is both a body of established knowledge and a social process through
which individual scientists and communities of scientists continually create,
revise, and elaborate scientific theories and ideas (Polanyi, 1958; National
Research Council, 2007). In one illustration of the social dimensions of
science, Dunbar (2000) found that scientists' interactions with their peers,
particularly how they responded to questions from other scientists, influ-
enced their success in making discoveries.
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74 EDUCATION FOR LIFE AND WORK
The idea that each discipline is a community with its own culture,
language, tools, and modes of discourse has influenced teaching and learn-
ing. For example, Moje (2008) has called for reconceptualizing high school
literacy instruction to develop disciplinary literacy programs, based on
research into what it means to write and read in mathematics, history and
science and what constitutes knowledge in these subjects. Moje (2008)
argues that students' understanding of how knowledge is produced in the
subject areas is more important than the knowledge itself.
Sociocultural perspectives are reflected in new disciplinary frameworks
and standards for K-12 education. In science, for example, A Framework
for K-12 Science Education: Practices, Crosscutting Concepts, and Core
Ideas (hereafter referred to as the NRC science framework; National Re-
search Council, 2012) calls for integrated development of science practices,
crosscutting concepts, and core ideas. The Common Core State Standards
in English language arts (Common Core State Standards Initiative, 2010a)
reflect an integrated view of reading, writing, speaking/listening, and lan-
guage and also respond to Moje's (2008) call for disciplinary literacy by
providing separate English language arts standards for history and science.
Based on the view of each discipline as a community engaged in ongoing
discourse and knowledge creation, the NRC science framework and the
standards in English language arts and mathematics include expectations
for learning of intrapersonal and interpersonal competencies along with
cognitive competencies (see Chapter 5 for further discussion).
In the committee's view, and informed by both perspectives, the link
between deeper learning and 21st century competencies lies in the classic
concept of transfer--the ability to use prior learning to support new learn-
ing or problem solving in culturally relevant contexts. We define deeper
learning not as a product but as processing--both within individual minds
and through social interactions in a community--and 21st century compe-
tencies as the learning outcomes of this processing in the form of transfer-
able knowledge and skills that result. The transferable knowledge and skills
encompass all three domains of competency: cognitive, intrapersonal, and
interpersonal, in part reflecting the sociocultural perspective of learning as
a process grounded in social relationships.
To support our proposed definitions of deeper learning and 21st cen-
tury competencies, we first draw on concepts and principles derived from
work in cognitive psychology. Based on this review of the research, we
describe the nature of deeper learning and briefly discuss instruction that
supports deeper learning and transfer (we elaborate on teaching for transfer
in Chapters 5 and 6).
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PERSPECTIVES ON DEEPER LEARNING 75
MULTIMEDIA SENSORY LONG-TERM
MEMORY WORKING MEMORY MEMORY
PRESENTATION
selecting organizing Verbal
Words Ears words Sounds words Model
integrating
Prior
Prior
Knowledge
Knowledge
selecting organizing Pictorial
Pictures Eyes images Images images Model
FIGURE 4-1 An information processing model memory.
SOURCE: Mayer, Heiser, and Lonn (2001). Copyright 2001 by the American Psy-
chological Association. Reproduced with permission. The use of APA information
does not imply endorsement by APA.
Components of Cognitive Architecture1
One of the chief theoretical advances to emerge from research and
theory is the notion of cognitive architecture--the information processing
system that determines the flow of information and how it is acquired,
stored, represented, revised, and accessed in the mind. Figure 4-1 shows
the main components of this architecture. Research has identified the dis-
tinguishing characteristics of the various types of memory shown in Figure
MEDIA 4-1 and the mechanisms by which they interact with each other.
SENSORY LONG-T
MEMORY WORKING MEMORY MEMOR
NTATION
Working Memory
rds Working selecting
Ears memory is what
Sounds
organizing
people use Verbal
to process and act on information
words words Model
immediately before them (Baddeley, 1986). Working memory is a conscious
integrating
system that receives input from memory buffers associated with the various Prior
Prior
sensory systems. There is also considerable evidence that working memory Knowled
Knowled
can receive input from the long-term memory system.
selecting organizing Pictorial
ures The key variable
Eyes images for working
Images memory is capacity--how
images Model much informa-
tion it can hold at any given time. Controlled (also defined as conscious)
human thought involves ordering and rearranging ideas in working memory
and is consequently restricted by the finite capacity of working memory.
Simply stated, working memory refers to the currently active portion of
long-term memory. But there are limits to such activity, and these limits are
governed primarily by how information is organized. Although few people
can remember a randomly generated string of 16 digits, anyone with a
slight knowledge of American Figure
history4-1
is likely to be able to recall the string
1492-1776-1865-1945. ThisPortrait above
is just one example of an important concept:
Landscape below
1This section of the chapter draws heavily on National Research Council (2001, pp. 65-68).
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76 EDUCATION FOR LIFE AND WORK
namely, that knowledge stored in long-term memory can have a profound
effect on what appears, at first glance, to be the capacity constraint in
working memory.
Long-Term Memory
Long-term memory contains two distinct types of information--seman-
tic information about "the way the world is" and procedural information
about "how things are done." Unlike working memory, long-term memory
is, for all practical purposes, an effectively limitless store of information. It
therefore makes sense to try to move the burden of problem solving from
working memory to long-term memory. What matters most in learning
situations is not the capacity of working memory--although that is a factor
in speed of processing--but how well one can evoke the knowledge stored
in long-term memory and apply it to address information and problems in
the present.
Contents of Memory
Contemporary theories also characterize the types of cognitive con-
tent that are processed by the architecture of the mind. The nature and
organization of this content is extremely critical for understanding how
people answer questions and solve problems, and how they differ in this
regard as a function of the conditions of instruction and learning. An im-
portant distinction in cognitive content is between domain-general knowl-
edge, which is applicable to a range of situations, and domain-specific
knowledge, which is relevant to a particular problem area.
Domain-General Knowledge and Problem-Solving Processes
Cognitive research has shown that general problem-solving procedures,
not specific to a particular domain of knowledge, are generally slow and
inefficient. Newell and Simon (1972) developed a computer program to
test such general procedures, known as "weak methods," identifying their
limitations as follows:
ˇ Hill climbing: One solves a problem by taking one step at a time
toward the overarching goal or task. This approach is inflexible
and may be inefficient, as selecting whatever step takes one uphill
(or in a particular direction) may cause the problem solver to climb
a foothill, ignoring the much more efficient procedure of going
around it. More sophisticated problem-solving strategies, such as
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PERSPECTIVES ON DEEPER LEARNING 77
those used by expert chess players, require one to look ahead many
steps to see potential problems well in advance and avoid them.
ˇ Means-ends analysis: One solves a problem by considering the
obstacles that stand between the initial problem state and the goal
state. The problem solver then identifies subgoals related to the
elimination of each these obstacles. When all of the subgoals have
been achieved (all of the obstacles have been eliminated), then
the main goal of interest has been achieved. Because the subgoals
have been identified through a focus on the main goal, means-ends
analysis can be viewed as a strategy in which the long-range goal
is always kept in mind to guide problem solving. It is not as near-
sighted as other search techniques, like hill climbing.
ˇ Analogy: One solves a problem by using the solution of a similar
problem. However, evidence shows that, generally, people who
have learned to solve a first problem are not better at solving a
second problem analogous to the first. Even when given explicit
instructions about the relationship between the two problems, in-
dividuals do not always find it easier to solve the second problem.
ˇ Trial and error: One solves a problem by randomly trying out so-
lutions until one has reached the goal. Trial-and-error approaches
can be very inefficient, as many of the random solutions may be
incorrect, and there is no boundary to narrow the search for pos-
sible solutions.
Problem solvers confronted by a problem outside their area of expertise
use these weak methods to try to constrain what would otherwise be very
large search spaces when they are solving novel problems. In most situ-
ations, however, learners are expected to use strong methods--relatively
specific algorithms particular to the domain that will make it possible to
solve problems efficiently. Strong methods, when available, find solutions
with little or no search. For example, someone who knows calculus can
find the maximum of a function by applying a known algorithm (taking the
derivative and setting it equal to zero). As discussed further below, experts
are able to quickly solve novel problems within their domain of expertise
because they can readily retrieve relevant knowledge, including the appro-
priate, strong methods to apply. Paradoxically, although one of the hall-
marks of expertise is access to a vast store of strong methods in a particular
domain, both children and scientists fall back on their repertoire of weak
methods when faced with truly novel problems (Klahr and Simon, 1999).
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78 EDUCATION FOR LIFE AND WORK
Knowledge Organization: Schemas and Expert-Novice Differences2
Although weak methods remain the last resort when one is faced with
novel situations, people generally strive to interpret situations so that they
can apply schemas--previously learned and somewhat specialized tech-
niques (i.e., strong methods) for organizing knowledge in memory in ways
that are useful for solving problems. Schemas help people interpret complex
data by weaving them into sensible patterns. A schema may be as simple as
"Thirty days hath September" or more complex, such as the structure of a
chemical formula. Schemas help move the burden of thinking from working
memory to long-term memory. They enable competent performers to rec-
ognize situations as instances of problems they already know how to solve;
to represent such problems accurately, according to their meaning and
underlying principles; and to know which strategies to use to solve them.
The existence of problem-solving schemas has been demonstrated in a
wide variety of contexts. Extensive research shows that the ways students
mentally "represent" (form a mental model of) the information given in a
math or science problem or in a text that they read depends on the orga-
nization of their existing knowledge. As learning occurs, increasingly well-
structured and qualitatively different organizations of knowledge develop.
These structures enable individuals to build a representation or mental
model that guides problem solution and further learning, avoid trial-and-
error solution strategies, and formulate analogies and draw inferences that
readily result in new learning and effective problem solving (Glaser and
Baxter, 1999). The impact of schematic knowledge is powerfully demon-
strated by research on the nature of expertise.
Research conducted over the past five decades has generated a vast
body of knowledge about how people learn the content and procedures of
specific subject domains. Researchers have probed deeply the nature of ex-
pertise and how people acquire large bodies of knowledge over long periods
of time. Studies have revealed much about the kinds of mental structures
that support problem solving and learning in various domains ranging from
chess to physics; what it means to develop expertise in a domain; and how
the thinking of experts differs from that of novices.
The notion of expertise is inextricably linked with subject-matter do-
mains: experts must have expertise in something. Research on how people
develop expertise has provided considerable insight into the nature of
thinking and problem solving. Although every person cannot be expected to
become an expert in a given domain, findings from cognitive science about
the nature of expertise can shed light on what successful learning looks like
and guide the development of effective instruction and assessment.
2This section of the chapter draws heavily on National Research Council (2001, pp. 70-73).
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PERSPECTIVES ON DEEPER LEARNING 79
What distinguishes expert from novice performers is not simply general
mental abilities, such as memory or fluid intelligence, or general problem-
solving strategies. Experts have acquired extensive stores of knowledge and
skill in a particular domain, and perhaps more significantly, they have or-
ganized this knowledge in ways that make it readily retrievable and useful.
In fields ranging from medicine to music, studies of expertise have
shown repeatedly that experts commit to long-term memory large banks of
well-organized facts and procedures, particularly deep, specialized knowl-
edge of their subject matter (Chi, Glaser, and Rees, 1982; Chi and Koeske,
1983). Most important, they have efficiently coded and organized this informa-
tion into well-connected schemas. These methods of encoding and organizing
help experts interpret new information and notice features and meaningful
patterns of information that might be overlooked by less competent learn-
ers. These schemas also enable experts, when confronted with a problem,
to retrieve the relevant aspects of their knowledge.
Of particular interest to researchers is the way experts encode, or
chunk, information into meaningful units based on common underlying fea-
tures or functions. Doing so effectively moves the burden of thought from
the limited capacity of working memory to long-term memory. Experts can
represent problems accurately according to their underlying principles, and
they quickly know when to apply various procedures and strategies to solve
them. They then go on to derive solutions by manipulating those meaning-
ful units. For example, chess experts encode mid-game situations in terms
of meaningful clusters of pieces (Chase and Simon, 1973).
The knowledge that experts have cannot be reduced to sets of isolated
facts or propositions. Rather, their knowledge has been encoded in a way
that closely links it with the contexts and conditions for its use. Because
the knowledge of experts is "conditionalized," they do not have to search
through the vast repertoire of everything they know when confronted with
a problem. Instead, they can readily activate and retrieve the subset of
their knowledge that is relevant to the task at hand (Simon, 1979; Glaser,
1992). These and other related findings suggest that teachers should place
more emphasis on the conditions for applying the facts or procedures being
taught, and that assessment should address whether students know when,
where, and how to use their knowledge.
Practice and Feedback3
Every domain of knowledge and skill has its own body of concepts,
factual content, procedures, and other items that together constitute the
knowledge of that field. In many domains, including areas of literature,
3This section of the chapter draws heavily on National Research Council (2001, pp. 84-87).
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90 EDUCATION FOR LIFE AND WORK
provides evidence that motivation and related intrapersonal skills enhance
deeper learning (Yaeger and Walton, 2011). The authors found that rela-
tively brief interventions can lead to large and sustained gains in student
achievement, as students develop durable, transferable intrapersonal skills
and apply them to new learning challenges in a positive, self-reinforcing
cycle of academic improvement.
Some of the experiments target students' "attributions"--how they
explain the causes of events and experiences. Research in social psychology
shows that if students attribute poor school performance to traits they view
as fixed (such as general low intelligence or a more specific lack of aptitude
in mathematics), they will not invest time and effort to improve their per-
formance. This leads to an "exacerbation cycle" of negative attributions
and poor performance (Storms and Nisbett, 1970).
Wilson and Linville (1982, 1985) studied a brief intervention designed
to change attributions among college freshmen. They brought two groups
of struggling freshmen into the laboratory to view videos of upperclass-
men discussing their transition to the college. In the videos viewed by the
experimental group, upperclassmen said that their grades were low at first,
due to transient factors such as a lack of familiarity with the demands of
college, but that their grades improved with time. In the videos viewed by
the control group, upperclassmen talked about their academic and social in-
terests but did not mention first-year grades. One year later, students in the
treatment group had earned significantly higher grade point averages (0.27
percent higher) than students in the control group, and the effect increased
over the following semesters. Ultimately, students in the treatment group
were 80 percent less likely to drop out of college than the control group.
In another example, Blackwell, Trzesniewski, and Dweck (2007)
studied an intervention designed to change attributions among low-income
minority seventh-grade students in an urban school. In an 8-week period at
the beginning of the school year, the students took part in eight workshops
on brain function and study skills. Students in the experimental group were
taught that the brain can get stronger when a person works on challeng-
ing tasks, while those in the control group learned only study skills. At the
end of the academic year, the students in the experimental group earned
significantly higher mathematics grades than those in the control group (a
mean increase of 0.30 grade points), reversing the normal pattern of de-
clining mathematics grades over the course of seventh grade. Noting that
the effectiveness of interventions targeting attributions has been replicated
with different student populations, Yaeger and Walton (2011) observe that
these studies support the hypothesis that changes in attributions can lead
to a positive, self-reinforcing cycle of improvement. Students who attribute
a low grade to transitory factors, such as a temporary lack of effort, rather
than to a lack of general intelligence or mathematics ability, are more
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PERSPECTIVES ON DEEPER LEARNING 91
motivated to work harder in their classes. This leads to improved grades,
which, in turn, reinforce students' view that they can succeed academically
and make them less likely to attribute any low grades to factors beyond
their control.
Other experiments are designed to reduce "stereotype threat," the
worry that one is perceived as having low intelligence as a member of a
stereotyped group, which has been shown to negatively affect academic
performance. Yaeger and Walton (2011) describe an intervention based on
self-affirmation theory, which posits that people who reflect on their posi-
tive attributes will view negative events as less threatening, experience less
stress, and function more effectively than they otherwise would. Cohen
et al. (2006, 2009) asked white and black seventh-grade students to com-
plete a brief, 15-20-minute writing exercise at the beginning of the school
year. The experimental group wrote about why two or three values were
personally important to them, while the control group wrote about values
that were not personally important. By the end of the first semester, black
students in the experimental group had significantly higher grade point
averages than their peers in the control group, reducing the black-white
achievement gap by about 40 percent. With a few more of these exercises,
the black students' gain relative to the control group persisted for 2 years.
These brief interventions appear to work by engaging students as ac-
tive participants. For example, when students write about values that are
important, they are actually generating the self-affirmation intervention.
Although they are intentionally brief, to avoid conveying to students that
they need intensive help or remediation, the interventions "can induce deep
processing and prepare students to transfer the content to new settings"
(Yaeger and Walton, 2011, p. 284). The study findings showing that the
interventions have led to changes in students' academic trajectories demon-
strate transfer of students' learning to new school or college assignments.
The Importance of Metacognition
In his book on unified theories of cognition, Newell (1990) points out
that there are two layers of problem solving--applying a strategy to the
problem at hand, and selecting and monitoring that strategy. Good problem
solving, Newell observed, often depends as much on the selection and moni-
toring of a strategy as on its execution. The term metacognition (literally
"thinking about thinking") is commonly used to refer to the selection and
monitoring processes, as well as to more general activities of reflecting on
and directing one's own thinking.
Experts have strong metacognitive skills (Hatano, 1990). They monitor
their problem solving, question limitations in their knowledge, and avoid
simple interpretations of a problem. In the course of learning and problem
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92 EDUCATION FOR LIFE AND WORK
solving, experts display certain kinds of regulatory performance such as
knowing when to apply a procedure or rule, predicting the correctness
or outcomes of an action, planning ahead, and efficiently apportioning
cognitive resources and time. This capability for self-regulation and self-
instruction enables advanced learners to profit a great deal from work and
practice by themselves and in group efforts.
Studies of metacognition have shown that people who monitor their
own understanding during the learning phase of an experiment show better
recall performance when their memories are tested (Nelson, 1996). Similar
metacognitive strategies distinguish stronger from less competent learners.
Strong learners can explain which strategies they used to solve a problem
and why, while less competent students monitor their own thinking sporadi-
cally and ineffectively and offer incomplete explanations (Chi et al., 1989;
Chi and VanLehn, 1991).
There is ample evidence that metacognition develops over the school
years; for example, older children are better than younger ones at planning
for tasks they are asked to do (Karmiloff-Smith, 1979). Metacognitive skills
can also be taught. For example, people can learn mental devices that help
them stay on task, monitor their own progress, reflect on their strengths and
weaknesses, and self-correct errors. It is important to note, however, that
the teaching of metacognitive skills is often best accomplished in specific
content areas since the ability to monitor one's understanding is closely tied
to domain-specific knowledge and expertise (National Research Council,
1999).
Self-Regulated Learning and Self-Regulation
Student beliefs about learning, motivation, and metacognition are all
dimensions of the broader construct of self-regulated learning, which fo-
cuses on understanding how learners take an active, purposeful role in
learning, by setting goals and working to achieve them.
In a recent review of the research on self-regulated learning, Wolters
(2010) observes that, although there are several different models of such
learning, the most prominent is that developed by Pintrich and colleagues
(Pintrich, 2000, 2004). In this model, learners engage in four phases of
self-regulation, not necessarily in sequential order: forethought or planning
(setting learning goals); monitoring (keeping track of progress in a learn-
ing activity); regulation (using, managing, or changing learning strategies
to achieve the learning goals; and reflection (generating new knowledge
about the learning tasks or oneself as a learner). These phases overlap
substantially with the elements of Type 1 self-regulation included in our
proposed cluster of Work Ethic/Conscientiousness skills (see Table 2-2). As
the learner engages in the different phases of self-regulation, he or she may
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regulate one or more of several interrelated dimensions of learning, includ-
ing cognition (for example, by using cognitive and metacognitive learning
strategies); motivation and affect (for example, by planning to reward him-
self or herself after studying); learning behavior; and the learning context
or environment (such as deciding where to study, and who to study with).
Comparing these dimensions of self-regulated learning with a list of
21st century skills proposed by Ananiadou and Claro (2009), Wolters
found a high degree of conceptual overlap. The 21st century skills of ini-
tiation and self-direction were congruent with self-regulated learning, as
the ability to set learning goals and manage the pursuit of those goals is a
hallmark of a self-regulated learner. The 21st century skill of adaptability,
including the ability to respond effectively to feedback, is very similar (or
identical) to what the learner does in the monitoring and reflection phases
of self-regulated learning. Learners who are strong in self-regulated learn-
ing are seen as particularly adept at using different forms of feedback to
continue and complete learning activities. Earlier in this chapter, we noted
that development of expertise requires not only extensive practice but also
feedback. Accordingly, development of self-regulated learning skills should
aid development of expertise in a domain.
Wolters (2010) identified a moderate degree of overlap between self-
regulated learning and the interpersonal skills of collaboration and com-
munication. He notes that research on self-regulated learning has begun to
explore the interpersonal dimensions of this "intrapersonal" skill, finding
that the abilities and beliefs underlying self-regulated learning are developed
through social processes. In addition, self-regulated learners are effective at
seeking help from peers or teachers, working in groups, and other aspects of
collaboration (Newman, 2008). Wolters (2010) concluded that the concep-
tual similarities between 21st century skills and dimensions of self-regulated
learning lend support to the critical importance of competencies such as
self-direction, adaptability, flexibility, and collaboration, and suggested
drawing on the self-regulated learning research to improve understanding
of the 21st century skills.
The construct of self-regulated learning has been used to design in-
structional interventions that have improved academic outcomes among
diverse populations of students, from early elementary school through
college. These interventions have led to improvements in class grades and
other measures of achievement in writing, reading, mathematics, and sci-
ence (Wolters, 2010).
Further research is needed to more clearly define the dimensions of self-
regulated learning, the relationship between this construct and 21st century
skills, and how development of self-regulated learning influences academic
engagement and attainment for diverse groups of students (Wolters, 2010).
Longitudinal research or other research to improve our understanding of
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the developmental trajectory of different dimensions of self-regulated learn-
ing, such as time management and goal-setting, would help to determine
the age level at which students should begin to develop these dimensions.
In addition, research is needed to develop more unified assessments of self-
regulated learning. The currently available measures (using self-reports,
observational, and other methods) suffer from shortcomings and are not
fully aligned with current views of self-regulated learning.
Self-Regulation
Self-regulated learning is one facet of the broader skill of self-regulation,
which is related to conscientiousness. Self-regulation encompasses setting
and pursuing short- and long-term goals and staying on course despite in-
ternal and external challenges; it includes managing one's emotions (Hoyle
and Davisson, 2011). What an individual uses to overcome internal chal-
lenges, such as counterproductive impulses, or external challenges that may
arise in different situations requires a set of strategies that, taken together,
comprise self-regulation.
Research on self-regulation is growing rapidly, with hundreds of ar-
ticles and five major edited volumes published since 2000 (Hoyle and
Davisson, 2011). Reflecting the breadth of the construct, researchers have
studied self-regulation in various life contexts, such as emotion, chronic
illness, smoking, exercise, eating, and shopping (Wolters, 2010). To date,
there is no consensus in the research on how to define self-regulation. In
a review of 114 chapters in edited volumes, Hoyle and Davisson (2011)
found that some provided no definition at all, there was no evidence of a
common definition, and the same authors sometimes proposed different
definitions in different chapters. Because the different definitions include a
large number of behavioral variables, further research is needed to more
clearly delimit the construct and to exclude variables that are not a critical
element of self-regulation.
In the previous chapter, we summarized research indicating that atten-
tion, a dimension of self-regulation, is related to reading and math achieve-
ment. Attention is the ability to control impulses and focus on tasks (e.g.,
Raver, 2004), and plays an important role in avoiding antisocial behavior.
Specifically, we noted that attention, measured at school entry, predicts later
reading and mathematics achievement in elementary school ( Duncan et al.,
2007). In addition, children who are weak in self-regulation, as indicated
by persistently high levels of antisocial behavior across the elementary
school years, are significantly less likely to graduate from high school and
to attend college than children who never had these problems (Duncan
and Magnuson, 2011). Developmental psychologists have developed mea-
sures of self-regulation in young children that focus on the ability to delay
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PERSPECTIVES ON DEEPER LEARNING 95
gratification. Longitudinal studies have found that measures of this dimen-
sion of self-regulation in early childhood predict academic and social com-
petence in adolescence (Mischel, Shoda, and Peake, 1988; Shoda, Mischel,
and Peake, 1990). Conversely, children who lacked self-regulation in early
childhood are more likely at age 18 to be impulsive, to seek danger, to be
aggressive, and to be alienated from others (Arsenault et al., 2000).
Given the importance of self-regulation, greater consensus on how to
conceptualize this broad construct is needed. The current disagreement
in the literature about how to define the foundations, process, and con-
sequences of self-regulation poses a major barrier to the development of
accurate assessments of it (Hoyle and Davisson, 2011). As we discuss in the
following chapter, teaching for deeper learning and transfer begins with a
model of student learning, representing the desired outcomes, and includes
assessments to measure student progress toward these outcomes. Agree-
ment on definitions is an essential first step toward teaching and learning
of self-regulation.
THE INTERPERSONAL DOMAIN
The sociocultural perspective that learning is "situated" within unique
social contexts and communities illuminates the importance of the interper-
sonal domain for deeper learning. This domain encompasses a broad range
of skills and abilities that an individual draws on when interacting with
others. We have proposed in Chapter 2 that it includes two skill clusters:
ˇ Teamwork and collaboration (aligned with the personality factor of
agreeableness), including such skills as communication, collabora-
tion, teamwork, cooperation, interpersonal skills, and empathy
ˇ Leadership (aligned with the personality factor of extroversion),
including such skills as leadership and responsibility, assertive com-
munication, self-presentation, and social influence
This preliminary taxonomy of the interpersonal domain represents an
initial step toward addressing the problem of a lack of clear, agreed-upon
definitions of interpersonal skills and processes. Below, we discuss the role
of interpersonal skills in deeper learning, and then return to the definitional
problem.
Much of what humans learn, beginning informally at birth and con-
tinuing in more structured educational and work environments, is acquired
through discourse and interactions with others. For example, development
of new knowledge in science, mathematics, and other disciplines is often
shaped by collaborative work among peers (e.g., Dunbar, 2000). Through
such interactions, individuals build communities of practice, test their own
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theories, and build on the learning of others. Individuals who are using a
naive strategy can learn by observing others who have figured out a more
productive one. The social nature of learning contrasts with many school
situations in which students are often required to work independently. Yet
the display and modeling of cognitive competence through group participa-
tion and social interaction is an important mechanism for the internalizing
of knowledge and skill (National Research Council, 1999).
An example of the importance of social context can be found in the
1994 work of Ochs, Jacoby, and Gonzales. They studied the activities of
a physics laboratory research group whose members included a senior
physicist, a postdoctoral researcher, technical staff, and predoctoral stu-
dents. They found that workers' contributions to the laboratory depended
significantly on their participatory skills in a collaborative setting--that is,
on their ability to formulate and understand questions and problems, to
construct arguments, and to contribute to the construction of shared mean-
ings and conclusions.
Lave and Wenger (1991) proposed that much of knowledge is embed-
ded within shared systems of representation, discourse, and physical activ-
ity in "communities of practice" and that such communities support the
development of identity--one is what one practices, to some extent. In this
view, school is just one of the many contexts that can support learning.
Several studies have supported the idea that knowledge and skills are devel-
oped and applied in communities of practice. For example, some research-
ers have analyzed the use of mathematical reasoning skills in workplace and
other everyday contexts (Lave, 1988; Ochs, Jacoby, and Gonzales, 1994).
One such study found that workers who packed crates in a warehouse
applied sophisticated mathematical reasoning in their heads to make the
most efficient use of storage space, even though they may not have been
able to solve the same problem expressed as a standard numerical equation
(Scribner, 1984). The rewards and meaning that people derive from becom-
ing deeply involved in a community can provide a strong motive to learn.
Studies of the social context of learning show that, in a responsive so-
cial setting, learners observe the criteria that others use to judge competence
and can adopt these criteria. Learners then apply these criteria to judge
and perfect the adequacy of their own performance. Shared performance
promotes a sense of goal orientation as learning becomes attuned to the
constraints and resources of the environment. In school, students develop
facility in giving and accepting help (and stimulation) from others. Social
contexts for learning make the thinking of the learner apparent to teachers
and other students so that it can be examined, questioned, and built on as
part of constructive learning.
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Social Dimensions of Motivation and Self-Regulated Learning
Earlier in this chapter, we discussed interventions designed to change
students' beliefs about themselves as learners and also their motivation
for learning (Yaeger and Walton, 2011). Although these interventions
target intrapersonal skills and attitudes as a way to enhance cognitive
learning, they are based on research and theory from social psychology.
The interventions are carefully designed to tap into social communities
and relationships that are important and meaningful to the targeted audi-
ences. For example, the intervention by Wilson and Linville (1982, 1985)
used videos of upperclassmen to convey an important message to strug-
gling freshmen because upperclassmen are viewed as trusted sources of
information by freshmen. Similarly, we noted that the abilities and beliefs
underlying self-regulated learning are developed through social processes
and that self-regulated learners are effective at seeking help from peers or
teachers, working in groups, and other aspects of collaboration (Newman,
2008). In Chapter 3, we observed that children lacking interpersonal
skills, as reflected in persistent patterns of antisocial behavior over the
elementary school years, are significantly less likely to graduate from high
school and to attend college than children who never had these problems
(Duncan and Magnuson, 2011). Clearly, social and interpersonal skills
support deeper learning that transfers to new classes and problems, en-
hancing academic achievement.
IMPLICATIONS FOR INSTRUCTION
Findings from the research reviewed in this chapter have important
implications for how to organize teaching and learning to facilitate deeper
learning and development of transferable 21st century competencies. Here,
we briefly summarize some of the implications, and in Chapter 6, we dis-
cuss in greater detail how to design instruction to support deeper learning.
As summarized by a previous NRC committee, research conducted over
the past century has (National Research Council, 2001, p. 87):
clarified the principles for structuring learning so that people will be better
able to use what they have learned in new settings. If knowledge is to be
transferred successfully, practice and feedback need to take a certain form.
Learners must develop an understanding of when (under what conditions)
it is appropriate to apply what they have learned. Recognition plays an
important role here. Indeed, one of the major differences between novices
and experts is that experts can recognize novel situations as minor variants of
situations to which they already know how to apply strong methods.
Experts' ability to recognize familiar elements in novel problems allows
them to apply (or transfer) their knowledge to solve such problems. The
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98 EDUCATION FOR LIFE AND WORK
research has also clarified that transfer is also more likely to occur when
the person understands the underlying principles of what was learned. The
models children develop to represent a problem mentally, and the fluency
with which they can move back and forth among representations, are other
important dimensions of transfer that can be enhanced through instruction.
The main challenge in designing instruction for transfer is to create
learning experiences for learners that will prime appropriate cognitive
processing during learning without overloading the learner's information-
processing system. Research on learning with multimedia tools has led to
the development of the cognitive theory of multimedia learning (Mayer,
2009, 2011a), derived from the cognitive load theory (Sweller, 1999; Plass,
Moreno, and Brünken, 2010). This theory posits that learners experience
cognitive demands during learning, but their limited processing capacity
restricts the amount of cognitive processing they can engage in at any one
time. According to both theories, learning experiences may place three
different types of demands on learners' limited working memory: (1) ex-
traneous processing, (2) essential processing, and (3) generative processing
(Sweller, 1999; Mayer, 2009, 2011a; Plass, Moreno, and Brünken, 2010).
Extraneous processing does not serve the learning goals and is caused by
poor instructional design. Essential processing is necessary if a learner is to
mentally represent the essential material in the lesson, and it is required to
address the material's complexity. Generative processing involves making
sense of the material (e.g., mentally organizing it and relating it to relevant
prior knowledge) and depends on the learner's motivation to exert effort
during learning.
Depending on how it is designed, instruction may lead to one of three
types of cognitive processing: extraneous overload, essential overload, and
generative underuse (Mayer, 2011a). If instruction creates an extraneous
overload situation, the amount of extraneous, essential, and generative
processing required by the instructional task exceeds the learner's cognitive
capacity for processing in working memory. An appropriate instructional
goal for extraneous overload situations is to reduce extraneous processing
(thereby freeing up cognitive capacity for essential and generative process-
ing). If instruction creates an essential overload situation, the amount of
essential and generative processing required by the instructional task ex-
ceeds the learner's cognitive capacity, even though extraneous processing
demands have been reduced or eliminated. An appropriate instructional
goal for essential overload situations is to manage essential processing (as it
cannot be cut because it is essential for the instructional objective). Finally,
if instruction creates a situation of generative underuse, the learner does
not engage in sufficient generative processing even though cognitive capac-
ity is available. An appropriate instructional goal for generative underuse
situations is to foster generative processing.
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PERSPECTIVES ON DEEPER LEARNING 99
In Chapter 6, we discuss evidence-based instructional methods for re-
ducing extraneous processing, managing essential processing, and promot-
ing generative processing. That chapter describes examples of techniques
that have been successful in teaching for transfer, including findings from
specific educational interventions.
CONCLUSIONS
Deeper learning occurs when the learner is able to transfer what was
learned to new situations. Research on teaching for transfer, which pri-
marily reflects the cognitive perspective on learning, has a long history in
psychology and education. This research indicates that learning for transfer
requires knowledge that is mentally organized, understanding of the broad
principles of the knowledge, and skills for using this knowledge to solve
problems. Other, more recent research indicates that intrapersonal skills
and dispositions, such as motivation and self-regulation, support deeper
learning and that these valuable skills and dispositions can be taught and
learned. Sociocultural perspectives on learning illuminate the potential for
developing intrapersonal and interpersonal skills within instruction focused
on cognitive mastery of school subjects; such perspectives provide further
evidence that skills in all three domains play important roles in deeper
learning and development of transferable knowledge.
ˇ Conclusion: The process of deeper learning is essential for the
development of 21st century competencies (including both skills
and knowledge), and the application of transferable 21st century
competencies, in turn, supports the process of deeper learning in a
recursive, mutually reinforcing cycle.
In Chapter 3, the committee concluded that educational attainment is
strongly predictive of positive adult outcomes in the labor market, health,
and civic engagement. The research reviewed in this chapter indicates that
individuals both apply and develop intertwined cognitive, intrapersonal,
and interpersonal competencies in the process of deeper learning, includ-
ing the learning of school subjects. Through deeper learning, individuals
develop transferable 21st century competencies that facilitate improvements
in academic achievement and that increase years of educational attain-
ment. Thus the research reviewed in this chapter supports the argument
that deeper learning and 21st century skills prepare young people for adult
success.
At the same time, this chapter finds a lack of clear, agreed-upon defini-
tions of specific cognitive, intrapersonal, and interpersonal competencies.
This lack of shared definitions is greatest for competencies in the intraper-
sonal and interpersonal domains.
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