Analysis of teaching expertise.

1. A Prototype View of Expert Teaching
ROBERT J. STERNBERG JOSEPH A. HORVATH
We call for a reconceptualization of teaching expertise, one
grounded in a psychological understanding of how (a) experts
differ from nonexperts, and (b) people think about expertise as
they encounter it in real-world settings. To this end, we propose
that teaching expertise be viewed as a category that is structured
by the similarity of expert teachers to one another rather than by
a set of necessary and sufficient features. A convenient way of
thinking about such categories is in terms of a central exemplar
or prototype (Rosch, 1978), and we believe that a prototype view
can contribute in important ways to a dialogue on expert teach-
ing. Most importantly, a prototype view provides a way of think-
ing about expertise that incorporates standards (such that not
every experienced practitioner is an expert) but also allows for
variability in the profiles of individual experts. In this article, we
outline a rudimentary model of prototype-based categorization
and identify possible features, drawn from psychological re-
search, on which the family resemblance among expert teachers
may befounded. We discuss several implications of the prototype
view for understanding and fostering expertise among teachers.
Educational Researcher, Vol. 24, No. 6, pp. 9-17
The question of what it means to be an expert teacher
has taken on some urgency in the nationwide effort to
reform public education. If American public schools
are to become centers of excellence, then their most impor-
tant human resource (i.e., teachers) must be effectively de-
veloped. To know what we are developing teachers
toward, we need a model of teaching expertise. Further, if
teachers are to be compensated and promoted on the basis
of merit rather than seniority, then we need a model of
teaching expertise with which to inform our performance
standards—to distinguish those teachers who are expert at
teaching students from those who are merely experienced
at teaching students.
In this article we call for a reconceptualization of teach-
ing expertise, one grounded in a psychological under-
standing of how (a) experts differ from nonexperts, and (b)
people think about expertise as they encounter it in real-
world settings. Recently, education researchers have
tended either to define teaching expertise restrictively (e.g.,
as a disposition toward reflective practice) or to describe
teaching expertise in an ad hoc fashion (i.e., as a list of ob-
served differences between experienced and less-experi-
enced teachers). In this article we seek a middle ground
between definitional and ad hoc descriptions of teaching
expertise.
A premise of our argument is that there exists no well-
defined standard that all experts meet and that no nonex-
perts meet. Rather, experts bear a family resemblance to
one another and it is their resemblance to one another that
structures the category "expert." A convenient way of talk-
ing about such categories is in terms of a prototype that rep-
resents the central tendency of all the exemplars in the cat-
egory (Rosch, 1973, 1978). As its name suggests, a
prototype embodies the typical exemplar of a category
and, as such, serves as a basis for judgments about cate-
gory membership. We propose that teaching expertise be
viewed as a similarity-based category with something like
a prototype as its summary representation.
We believe that a prototype view can contribute in im-
portant ways to the dialogue on expert teaching. First, a pro-
totype view allows us to adopt a fuller, more inclusive
understanding of teaching expertise without falling into the
trap of making everyone a presumptive expert. Second, a
prototype view provides a basis for understanding apparent
"general factors" in teaching expertise. Finally, the proto-
type view provides a basis for understanding and anticipat-
ing social judgments about teaching expertise. We elaborate
onJeach of these points at the conclusion of this article.
In sketching out a prototype view of teaching expertise,
we employ a simple, featural model of similarity-based
categorization. The elements of this model are taken from
prior, empirical work on the psychology of categorization.
We propose three clusters of features, derived from psy-
chological research, on which the family resemblance
among experts may be founded. Each feature cluster con-
sists of cognitive mechanisms and/or abilities thought to
be related to expert performance. Finally, we explore sev-
eral implications of a prototype view for understanding
and fostering expert teaching. Throughout the article, we
draw heavily on the work of others, many of whom do not
share our theoretical orientation. Our goal is not to present
a comprehensive review, but, rather, to offer a synthetic
framework that may stimulate research and debate.
The Categorization Model
A category is a set of objects that are perceived to be simi-
lar—objects that "seem to go together" (Smith, 1990; Smith
& Medin, 1981). For example, a robin, a cardinal, and a
wren seem to go together, as do a tuba, a piano, and a trom-
bone. For present purposes, similarity may be considered
to be an increasing function of shared features and a de-
ROBERT J. STERNBERG is the IBM Professor of Psychology and
Education at Yale University, Box 208205, New Haven, CT
06520-8205. His areas of specialization are intelligence, creativ-
ity, and styles of thinking.
JOSEPH A. HORVATH is an Associate Research Scientist at Yale
University, Box 208205, New Haven, CT 06520-8205. His areas
of specialization are practical intelligence, learning in the work-
place, and leadership.
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2. creasing function of nonshared features. For example, a
trombone and trumpet share many features (made of metal
tubing, flared at one end, hand held) and are judged to be
highly similar to one another.
Unlike categories based on all-or-nothing definitions,
similarity-based categories tend to be "fuzzy" on the issue
of whether particular objects are valid category members.
For example, is a bell a musical instrument? What about an
empty coffee can? As these examples show, similarity-
based categories exhibit a graded structure wherein some
category members are "better" or more typical exemplars
of the category than are others. For example, a violin is
considered to be a better or more typical example of a mu-
sical instrument than is a bell.
One way of capturing the fuzziness of similarity-based
categories is to postulate a central or "prototypical" cate-
gory member that serves as a summary representation of
the category (Rosch, 1978). The prototype may be thought
of as the central tendency of feature values across all valid
members of the category. Thus, because the size of musical
instruments ranges, roughly, from that of a grand piano to
that of a harmonica (with most instruments somewhere in
the middle), the prototypical musical instrument is likely
to be of intermediate size—that of a trumpet or violin. Ac-
cording to a prototype model, judgments about category
membership are made by computing the similarity be-
tween the object in question and the prototype of the cate-
gory. The higher the similarity, the higher the subjective
probability that the object belongs to the category.
In addition to their probabilistic nature, prototype-cen-
tered categories have three properties that bear special
mention. First, different members of a category may re-
semble the category prototype on different features. For ex-
ample, if we assume that the prototype musical instrument
is something like a violin, then a trumpet may resemble the
prototype in size and the cello may resemble the prototype
in material composition. As this example suggests, the
pairwise similarity between any two category members
needn't necessarily be high, even when their typicality or
"goodness" with respect to the category is comparable.
A second important property of a prototype model is the
differential weighting of features in the computation of
overall similarity to the prototype. For example, in assign-
ing an object to the musical-instrument category, color
would be weighted much less heavily than, say, the pres-
ence of a sound-producing cavity. Because features are
weighted differently, an object may be judged to be a cate-
gory member by virtue of relatively few shared features (if
those features are heavily weighted). An implication of this
rule is the possibility of a feature that is necessary but not
sufficient for category membership. For example, an object
that cannot be used to produce a sound (e.g., a stone carv-
ing of a violin) would not be classified as a valid member
of the musical instrument category, no matter how many
other features it shared with the category prototype.
Finally, the features that make up a category prototype
may be correlated—they may occur together in category
members at a level greater than chance. For example, in-
struments that have reeds tend also to be composed of
wood. Because features are often correlated with one an-
other, a smaller number of factors or components may be
identified that explain the similarity among category mem-
bers. For example, the oboe, bassoon, and clarinet tend to
share a cluster of features and we denote the underlying
"factor" with the subordinate-category term woodwind.
Having presented a thumbnail sketch of a prototype
model, several disclaimers are in order. First, the sketch is
incomplete in its specifications. There are many issues,
ranging from the composition of the prototype representa-
tion, the computation of similarity, the nature of a decision
rule for category assignment, and, indeed, even the precise
nature of a "feature," that our treatment glosses over. A
second disclaimer concerns the types of categories for
which prototype models give a reasonable account. Briefly,
prototype models can account for a number of effects ob-
served in studies of human learning and reasoning about
similarity-based categories. Prototype models give a much
less adequate account of learning and reasoning about
classes of highly dissimilar objects (e.g., the class of all
things weighing less than 3 pounds) and about ad hoc cat-
egories (e.g., the category of things you might grab while
fleeing a house fire).
Content of the Expert Prototype
We now turn to the question of content. Specifically, what
are the features that make up the prototype expert teacher?
Note that here we are asking what should be the content of
the prototype—a defensible prototype rather than a com-
mon or widely held prototype. To answer this question we
Jpok to psychological research on expert performance in a
Variety of domains. From this research, we identify three
basic ways in which experts differ from novices. The first
difference pertains to domain knowledge. Experts bring
knowledge to bear more effectively on problems within
their domains of expertise than do novices. The second dif-
ference pertains to efficiency of problem solving. Experts do
more in less time (in their domain of expertise) than do
novices. The third difference pertains to insight. Experts are
more likely to arrive at novel and appropriate solutions to
problems (again, within their domains) than are novices.
Together, these three differences between experts and
novices comprise our current best guess about the features,
or constellations of features, upon which a prototype of the
expert teacher should be founded.
Knowledge
Perhaps the most fundamental difference between experts
and novices is that experts bring more knowledge to bear
in solving problems within their domain and do so more
effectively than do novices. Although this difference seems
almost too obvious to be worth remarking on, cognitive-
psychological research has provided us with insights re-
garding the nature of expert knowledge and its relation to
the superior performance that experts exhibit.
The study of expertise was revolutionized when Chase
and Simon (1973) reopened a line of study first explored by
deGroot (1965). This research examined differences be-
tween expert and novice chess players in memory for par-
ticular configurations of chess pieces on chess boards.
Chase and Simon showed these configurations to both ex-
perts and novices for brief periods of time, then assessed
memory for the configurations. As expected, experts
showed superior memory—but only when the chess pieces
were arranged in a sensible configuration (i.e., a configura-
tion that might logically evolve during the course of a
game). When chess pieces were placed on the board in ran-
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3. dom configurations, experts and novices both showed
poor memory for these configurations.
The Chase and Simon findings were important for sev-
eral reasons. First, they showed that the advantage of chess
experts over novices was particular to chess configurations
and thus did not reflect superiority in generally applicable
cognitive processes. Second, the Chase and Simon findings
suggested that the advantage that experts enjoyed was one
of knowledge. The chess experts had stored tens of thou-
sands of sensible configurations in memory, and this stored
knowledge allowed them to encode Chase and Simon's
stimulus patterns more easily, giving them an advantage
over novices in the memory task. Other studies have repli-
cated this basic effect in domains other than chess (e.g.,
Chase, 1983; Reitman, 1976).
Lee Shulman has identified types of knowledge necessary
for expert teaching (Shulman, 1987). First, and most obvi-
ously, expert teachers must have content knowledge—:
knowledge of the subject matter to be taught. In addition to
content knowledge, expert teachers need pedagogical
knowledge—knowledge of how to teach. Pedagogical
knowledge of the general variety includes knowledge of
how to motivate students, how to manage groups of stu-
dents in a classroom setting, how to design and administer
tests, and so forth. Finally, expert teachers need pedagogi-
cal-content knowledge—knowlege of how to teach that is
specific to what is being taught. Pedagogical-content knowl-
edge includes knowledge of how to explicate particular
concepts (e.g., how to explain negative numbers), how to
demonstrate and rationalize procedures and methods (Lein-
hardt, 1987), and how to correct students' naive theories and
misconceptions about subject matter (Gardner, 1991).
Although experts clearly have more knowledge than do
novices, this difference has been less useful in understand-
ing expertise than have differences in the way that knowl-
edge is organized in memory. Studies of expertise in
physics problem solving have been particularly influential
on this point (Chi, Feltovich, & Glaser, 1981; Chi, Glaser, &
Rees, 1982; Larkin, McDermott, Simon, & Simon, 1980). For
example, Chi, Feltovich, and Glaser (1981) found that ex-
pert and novice problem solvers sorted the same physics
problems differently. In general, experts were sensitive to
the deep structures of the problems they sorted—they
grouped problems together according to the physics princi-
ples that were relevant to problem solution (e.g., conserva-
tion of momentum). By contrast, novices were more
sensitive to surface structure—they sorted problems ac-
cording to entities contained in the problem statement (e.g.,
inclined planes). These results, and those from a number of
related studies, suggest that experts and novices differ not
only in the amount of knowledge they have but also in the
manner in which that knowledge is organized in memory.
Several studies of expert teaching have concluded that
expert and novice teachers differ in the organization of
their domain-relevant knowledge (Borko & Livingston,
1989; Leinhardt & Greeno, 1986; Livingston & Borko, 1990;
Sabers, Cushing, & Berliner, 1991). Together, these studies
suggest that expert teachers possess knowledge that is
more thoroughly integrated—in the form of scripts, prepo-
sitional structures, and schemata—than is the knowledge
of novice teachers.
One important form of schematically organized teaching
knowledge—the lesson plan or agenda—has received de-
tailed consideration (Borko & Livingston, 1989; Collins &
Stevens, 1982; Collins, Warnock, & Passafiume, 1975; Lein-
hardt, 1987; Leinhardt & Greeno, 1986). The lesson plan in-
tegrates knowledge of content to be taught with
knowledge of teaching methods. According to Leinhardt
and Greeno, a lesson plan includes global (content-non-
specific) planning components, local (content-specific)
planning components, and decision elements that make
the lesson plan responsive to expected and unexpected
events. Global components might include routines for
checking homework, presenting new material, and super-
vising guided practice. Local components might include
routines for presenting particular concepts or for assessing
student understanding of particular concepts. Decision el-
ements, as their name suggests, represent contingencies in
the planning structure such as anticipated questions and
the local components needed to address those questions.
A well-developed planning structure, of the type out-
lined previously, enables the expert teacher to teach effec-
tively and efficiently. Content-nonspecific teaching
knowledge, such as general class-management routines,
maximizes the amount of time that students spend learn-
ing (rather than passing out paper or switching activities).
Content-specific teaching knowledge, such as explanations
keyed to specific student questions, enables the expert
teacher to connect student feedback to lesson objectives,
thus keeping the lesson on track.
•'By contrast, novice teachers are found to have less com-
plex, less connected planning structures. Because they lack
knowledge of efficient general routines, novice teachers
tend to spend more time with their classes off-task (Lein-
hardt & Greeno, 1986). Because their content-specific
teaching knowledge is not as developed as that of experts,
novice teachers tend to have difficulty generating exam-
ples and explanations if those examples and explanations
have not been prepared in advance (Borko & Livingston,
1989). Because their planning structures are less likely to
anticipate particular student misconceptions, novice teach-
ers tend to have difficulty in relating student questions to
lesson objectives (Borko & Livingston, 1989).
In addition to well-organized knowledge of content and
pedagogy, expert teachers need knowledge of the social
and political context in which teaching occurs. We have ar-
gued that practical knowledge of this sort has been ne-
glected in studies of expertise, and in the study of
intelligent behavior generally (Sternberg, Wagner,
Williams, & Horvath, in press). As Csikszentmihalyi (1988)
has pointed out, expertise is embedded in a field as well as
in a domain. For example, an expert in the domain of ex-
perimental physics must know the findings, theories, and
methods of physics. An expert in the field of experimental
physics must know the kinds of research that tend to be
successful, how to get articles published and research
funded, how to get and keep a job, and so forth.
An expert in the domain of teaching must know subject-
matter content and pedagogy. An expert in the field of
teaching must know how to apply teaching knowledge in
a particular social and organizational context. For example,
expert teachers need to know how to package curricular
innovations to sell those innovations to fellow teachers,
parents, and administrators. Expert teachers may need to
know how to insulate their classroom from machinations
at the administrative or societal level (e.g., pressure to im-
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4. prove test scores or debates over textbook content). More
frequently, in an era of shrinking school budgets, expert
teachers need to be proficient at "working the system" to
obtain needed services for their students. We believe that
such practical ability or "savvy" is a nontrivial component
of teaching effectiveness and needs to be a part of a proto-
type representation of teaching expertise.
In attempting to understand practical aspects of exper-
tise, we have employed the tacit-knowledge construct first
proposed by Polanyi (1967). Polanyi used the term tacit
knowledge to refer to the hidden bases for intelligent action.
We define tacit knowledge as knowledge that is instrumen-
tal to the attainment of valued goals but whose transmission
the environment generally does not support. Put simply,
tacit knowledge is the knowledge one needs to succeed that
is not explicitly taught, and that often is not even verbal-
ized. We believe that such knowledge is important in select-
ing, adapting to, and shaping one's environment, and that a
consideration of tacit knowledge is crucial to understanding
expertise as it develops and operates in the real world.
Research has shown that tacit knowledge generally (a)
increases with experience on the job, (b) is unrelated to IQ,
(c) predicts job performance better than does IQ, (d) pro-
vides a significant increment in prediction above that pro-
vided by traditional tests of intelligence, personality, and
cognitive style, and (e) overlaps across fields, though only
partially (for a review, see Sternberg, Wagner, & Okagaki,
1993; Sternberg & Wagner, 1994). In a typical test of tacit
knowledge, subjects are given a scenario relating to a situ-
ation they might encounter on the job and are asked to rate
the quality of different courses of action as responses to the
situation. Every study in this program of research—inves-
tigating the tacit knowledge of business executives, profes-
sors of psychology, sales people, and college students—has
shown that tacit knowledge is important to expertise on
the job. Thus, although tacit knowledge for teaching has
not been fully studied to date, there is reason to suspect
that it is an important contributor to expert performance.
One of the ways in which tacit knowledge helps people
succeed is by helping them be labeled as experts. Note that
getting labeled as an expert is not simply a matter of "ca-
reerism" for its own sake—it is nearly a prerequisite to the
development of expertise in many fields of endeavor. For
example, it is extremely difficult to become an expert re-
search scientist without an academic or institute appoint-
ment, research funds, opportunities to confer with others at
meetings, and so forth. Thus, knowledge of the ins and outs
of publication, teaching, gaining tenure, obtaining grants,
and the like are not important only for careerists but also for
those who seek the opportunity to develop as experts.
Likewise, although there are many fine teachers in, for
example, home-schooling environments, becoming an ex-
pert teacher normally requires a school that will provide
access to students, along with the resources required to
teach them. Once hired, a teacher's ability to teach new
courses or participate in special projects (i.e., to further de-
velop teaching expertise) may depend on having been la-
beled, informally or otherwise, as an expert or "master"
teacher. Thus, one part of what it means to be an expert
teacher is knowing how to get labeled and supported as
one. A teacher who is expert, but in a way that does not
match the public conception of teaching expertise, may
lose the opportunity to develop further.
It goes without saying that the ability to get labeled as an
expert is not a sufficient condition for expertise. An indi-
vidual may be revered as an expert and yet be essentially
vacuous in the message or performance he or she delivers.
The ability to get labeled as an expert is but one ingredient
or aspect of expertise—one that has generally been ig-
nored in the psychological study of expertise.
To summarize, the prototype expert teacher is knowl-
edgeable. He or she has extensive, accessible knowledge
that is organized for use in teaching. In addition to knowl-
edge of subject matter and of teaching per se, the prototype
expert has knowledge of the political and social context in
which teaching occurs. This knowledge allows the proto-
type expert to adapt to practical constraints in the field of
teaching—including the need to become recognized and
supported as an expert teacher.
Efficiency
A second basic difference between experts and novices is
that experts are able to solve problems more efficiently,
within their domain of expertise, than are novices. That is,
experts can do more in less time (or with less apparent ef-
fort) than can novices. As the discussion to follow makes
clear, the efficiency of expert problem-solvers is related to
their ability to automatize well-learned skills as well as to
their ability to effectively plan, monitor, and revise their
^approach to problems.
' The pioneering work of Newell, Shaw, and Simon (1958)
and Miller, Galanter, and Pribram (1960) shifted much of
the emphasis in experimental psychology from stimulus-
response theories of behavior to cognitively based theories.
Instead of treating the mind as a "black box" that mediated
between stimuli and responses to stimuli, psychologists
began to treat processes of thought as of interest and know-
able in their own right. Cognitively oriented psychologists
studied the sequences of mental operations used to solve
various kinds of problems. The notion of expertise implicit
in this work was that experts differed from novices in
terms of the speed and accuracy with which generally ap-
plicable cognitive processes were executed. In the domain
of chess, for example, expertise was seen as the ability to
look farther and faster into possible futures of the game,
identifying those courses of action that reduce distance to
the goal state.
By the early 1980s, however, the strong version of the
general-process view—that there are many critical
processes that generalize across tasks, independently of
content and context—had fallen out of favor. Cognitive ar-
chitectures based on simple yet powerful search routines
(e.g., Newell & Simon, 1972) were supplanted by ex-
pectancy-based systems that used assumptions about the
world to constrain information processing (e.g., Fahlman,
1979; Schank & Abelson, 1977). Similarly, the general-
process view of expertise was supplanted by a representa-
tion-based view that emphasized the role of prior
knowledge in expert performance. Although few today
subscribe to the general-process view of expertise, it re-
mains relevant because it captures a salient fact about
human experts. This fact is that experts can do what
novices do in a shorter period of time (or can do more than
novices do in an equivalent period of time).
Experts seem not only to perform better than novices,
but they also seem to do so with less effort. Cognitive re-
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5. sources are limited in human beings but experts seem at
times to stretch these limits—they seem to do more at a
given level of expenditure of resources. The accepted ex-
planation for this difference is that cognitive processes may
be divided into those that are resource-consuming or con-
trolled and those that are relatively resource-independent
or automatic (Shiffrin & Schneider, 1977). Further, certain
types of cognitive skills may become automatic with ex-
tensive practice. What is initially effortful and resource-
consuming becomes, with practice, automatic and
relatively resource-independent (Anderson, 1982). Thus,
by virtue of their extensive experience, experts are able to
perform tasks effortlessly that novices can perform only
with effort. For example, the expert driver does not need to
think about fundamental driving skills like steering, shift-
ing, and braking. Indeed, most of us are experts in this re-
spect and can plan the day's work while we drive to the
office. For the novice driver, however, the basic driving
skills can be applied only with conscious effort—to let
one's thoughts turn to events later in the day would be to
court disaster.
In considering the importance of automaticity for expert
teaching, we must emphasize that the capacity to automate
well-learned routines cannot be separated neatly from the
schematic organization of teaching knowledge in any rea-
sonable account of the mental processes involved in expert
performance. Consider the example of expert and novice
teachers monitoring ongoing classroom events (as simu-
lated in a study by Sabers et al., 1991). Expert teachers in
this study did a better job of monitoring fast-paced, simul-
taneously presented classroom events than did novices.
Experts also produced think-aloud protocols that were
richer and more interpretive in nature than were those pro-
duced by novices. How might we explain this superior
performance by expert teachers?
If we wish to emphasize the role of automaticity in this
performance, we may argue that the experience of the ex-
pert teachers enabled them to handle (a) more information
per unit time than did the novices, or (b) the same amount
of information but at a lower level of cognitive effort. This
increase in "bandwidth," through the automatization of
classroom monitoring, would explain the experts' superior
ability to see meaningful patterns in the stream of ongoing
events. If, on the other hand, we wish to emphasize the role
of knowledge organization, we may argue that the experi-
ence of the expert teachers provided them with a store of
meaningful patterns, corresponding to classroom situa-
tions, and that the number and accessibility of these pat-
terns made their recognition (through monitoring of
ongoing activities) less resource-consuming for expert
teachers than it was for novices.
Experts differ from novices in their use of higher order,
executive processes. These executive processes, called
"metacomponents" in Sternberg's (1985) triarchic theory of
human intelligence, control the way in which mental re-
sources are marshaled. Metacomponents are used to plan,
monitor, and evaluate ongoing efforts at problem solv-
ing—they are the "white collar workers" in the human
cognitive system (Sternberg, 1980).
Research on expertise has shown that experts and
novices differ in metacognitive or executive control of cog-
nition. Experts typically spend a greater proportion of their
solution time trying to understand the problem to be
solved. Novices, in contrast, typically invest less time in
trying to understand the problem and more in actually try-
ing out different solutions (Lesgold, 1984; Sternberg, 1981).
Experts are more likely to monitor their ongoing solution
attempts, checking for accuracy (Larkin, 1983), and updat-
ing or elaborating problem representations as new con-
straints emerge (Voss & Post, 1988). Similar findings have
been reported in a study of expert teaching (Swanson,
O'Connor, & Cooney, 1990). Expert teachers were found to
be more planful than were novices in their approach to
classroom discipline problems. Experts tended to empha-
size the definition of discipline problems and the evalua-
tion of alternative hypotheses, whereas novices tended to
be more "solution oriented" and less concerned with de-
veloping an adequate model of the discipline problem.
A growing literature on reflective practice in teaching
has also emphasized the importance of metacognitive or
executive processes in expert teaching (Copeland, Birming-
ham, de la Cruz, & Lewin, 1993; Grimmet, MacKinon, Erik-
son, & Riecken, 1990). This literature, which has its origins
in the work of management scientist Donald Schon (1983),
seeks to understand and promote the disposition toward
reflection, typically defined as continuous learning through
experience. Thus, reflective teachers are considered to be
those who use new problems as opportunities to expand
their knowledge and competence. A number of studies
have reported beneficial effects of fostering a "reflective
stance" in teachers (Bean & Zulich, 1989; Bolin, 1988).
Again, we must emphasize the nonindependence of the
putative features of the expert prototype. The capacity to
automatize well-learned routines, discussed earlier, is
clearly related to the experts' capacity to be reflective and,
more generally, to exert effective executive control over
problem solving. That is, the cognitive resources that are
"saved" through automatization do not simply make
problem solving easier for the expert. Rather, these re-
sources are freed for higher level cognition that is beyond
the capacity of the nonexpert. Bereiter and Scardamalia
(1993) place this "reinvestment" function at the core of
their model of expertise. According to their model, true
experts are distinguished from experienced nonexperts by
their reinvestment of cognitive resources in the progres-
sive construction of more nearly adequate problem mod-
els. Thus, whereas novices and experienced nonexperts
seek to reduce problems to fit available methods, true ex-
perts seek progressively to complicate the picture, contin-
ually working on the leading edge of their own
knowledge and skill.
To summarize, the prototype expert teacher is efficient in
solving problems within the domain of teaching. By virtue
of his or her extensive experience, the prototype expert is
able to perform many of the constituent activities of teach-
ing rapidly and with little or no cognitive effort. This rou-
tinized skill enables the prototype expert to devote
attention to high-level reasoning and problem solving in
the domain of teaching. In particular, the prototype expert
is planful and self-aware in approaching problems—he or
she does not jump into solution attempts prematurely.
Insight
Both experts and novices apply knowledge and analysis to
solve problems. Yet somehow experts are more likely to ar-
rive at creative solutions to those problems—solutions that
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6. are both novel and appropriate. Experts don't simply solve
the problem at hand; they often redefine the problem and
thereby reach ingenious and insightful solutions that
somehow do not occur to others. We call these solutions in-
sightful partly to denote the quality of seeing into a prob-
lem deeply.
The processes of insight used in creative problem solv-
ing correspond to what Sternberg and colleagues have re-
ferred to as "selective encoding," "selective combination,"
and "selective comparison" (Davidson & Sternberg, 1984;
Sternberg, 1985). Selective encoding involves distinguish-
ing information that is relevant to problem solution from
information that is irrelevant to problem solution. An in-
sight based on selective encoding is recognizing that a
piece of information others assumed was important is in
fact unimportant, or that a piece of information others as-
sumed was unimportant is in fact important. This filtering
of relevant from irrelevant information is critical to expert
performance in many domains. For example, an expert
teacher can distinguish between those lines of class discus-
sion that are likely to further instructional goals and those
that are merely diverting for students. Selective encoding
provides the basis for insightful solutions.
Selective combination involves combining information
in ways that are useful for problem solution—recognizing
that two pieces of information that seem irrelevant when
considered separately are, when taken together, relevant to
solving the problem at hand. For example, an expert
teacher will recognize that expensive new clothes, when
combined with a drop in academic performance, may sig-
nal that a student is working too many hours at an after-
school job. Selective combination provides the basis for
insightful solutions.
Finally, selective comparison involves applying all the
information acquired in another context to a problem at
hand. It is here that acquired knowledge becomes espe-
cially important, both with respect to quantity and organi-
zation. An insight based on selective comparison is
noticing, mapping, and applying an analogy to solve a
problem. For example, an expert teacher may notice a
structural similarity between a current classroom problem,
say, managing a group of very bright but disruptive stu-
dents, and one she has seen solved before, say, in a busi-
ness-magazine article on the shareholder rights movement.
Similarly, expert teachers frequently exploit analogies be-
tween phenomena that are familiar to their students (e.g.,
a crowd of people moving through a set of turnstiles) and
those that are new to their students (e.g., electrical resis-
tance in DC circuits). By exploring the analogy between the
two problems, the expert arrives at a creative solution that
would have never occurred to the novice.
The capacity to solve problems insightfully is likely to be
correlated, in the population of experts, with other features
of the prototypical expert. In particular, the organization of
domain knowledge may be seen as a major contributor to
the ability insightfully to reformulate problem representa-
tions. Obviously, a teacher's ability to solve a classroom
problem by analogy to a known case will be critically de-
pendent on both the quantity of stored cases and the way
in which those cases are organized for retrieval.
To summarize, the prototype expert teacher is insightful
in solving problems within the domain of teaching. He or
she is able to identify information that is promising with
respect to a problem solution and is able to combine that
information effectively. The prototypical expert is able to
reformulate his or her representation of the problem at
hand through a process of noticing, mapping, and apply-
ing analogies. Through processes such as these, the expert
teacher is able to arrive at solutions to problems in teach-
ing that are both novel and appropriate (Sternberg &
Lubart, 1995).
Implications of the Prototype View
We have suggested that teaching expertise be viewed as a
natural category, structured by the similarity of expert
teachers to one another and represented by a central exem-
plar or prototype with reference to which decisions about
the expert status of a teacher are made. We have outlined a
rudimentary model of categorization and have identified
some putative features of such a prototype, based on psy-
chological research. These putative features are listed, and
briefly exemplified, in Table 1. We now remark upon the
implications of the prototype view for understanding
teaching expertise.
We have said that similarity-based categories are inher-
ently fuzzy. We have also said that when such categories
are organized around a prototype, two equally valid mem-
bers of the category may resemble each other much less
than they individually resemble the prototype. These prop-
*
• erties imply, of course, that by viewing teaching expertise
as a prototype, we can distinguish experts from experi-
enced nonexperts in a way that acknowledges (a) diversity
in the population of expert teachers, and (b) the absence of
a set of individually necessary and jointly sufficient fea-
tures of an expert teacher. Thus, a teacher who displays a
wealth of highly organized content knowledge and a
teacher who is adept at generating insightful solutions to
classroom problems may both be categorized as experts,
even though their resemblance to one another is weak.
It is important, however, that the prototype view does
not make every experienced teacher a presumptive expert.
That is, a teacher whose resemblance to the expert proto-
type is quite low will be judged a very poor example of an
expert teacher (i.e., a nonexpert). Thus, a prototype view
broadens the picture of expert teaching without succumb-
ing to a creeping relativism that treats all alternatives as
commensurable. Note also that the prototype view is not
inconsistent with the existence of features that are neces-
sary (but insufficient) for membership in the category. For
example, because the feature "can be used to make a pleas-
ing sound" is weighted heavily in the computation of sim-
ilarity with respect to the category "musical instrument," a
holographic image of a violin is judged a likely nonmem-
ber of the category. Similarly, a teacher who lacks content
knowledge is judged a likely nonexpert, even if his or her
tacit knowledge about the social and political milieu is ex-
traordinarily high.
A second implication of a prototype view concerns the
tendency for features to be correlated and the possibility
that a smaller number of factors or components can be used
to describe the composition of a category. To return to our
earlier example, the woodwinds form a subordinate cate-
gory of musical instruments based on a constellation of
shared features. With respect to expert teaching, the proto-
type view captures the sense, inherent in a definitional ap-
proach to expertise, that the many aspects of expertise can
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7. Table 1
Contents of the Expert Teaching Prototype
Feature Example
Knowledge (Quantity and Orj
Content knowledge
Pedagogical knowledge
Content-specific
Content-nonspecific
Practical knowledge
Explicit
Tacit
Efficiency
Automatization
Executive control
Planning
Monitoring
Evaluating
Reinvestment of cognitive
resources
Insight
Selective encoding
Selective combination
Selective comparison
anization)
Expert knows principles of
coordinate geometry
Expert knows lesson plans or
agendas for teaching princi-
ples of coordinate geometry
Expert knows routines for
distributing and collecting
homework with minimal dis-
ruption
Expert knows school-district
criteria for special-education
services
Expert knows to whom to
speak to obtain special-edu-
cation services for a student
who does not fit standard
identification criteria
Expert supervises collection
and distribution of home-
work while thinking ahead
in lesson plan
Expert anticipates difficulties
in the execution of a lesson
plan
Expert detects students' fail-
ures of comprehension or in-
terest during the execution
of a lesson plan
Expert revises a lesson plan
for future use, based on diffi-
culties encountered
Expert uses the distribution
and collection of homework
as an opportunity to observe
and evaluate the conduct of
a particular student
Expert notes that students are
having trouble plotting
points outside the upper-
right quadrant of the Carte-
sian grid
Expert notes that trouble
with plotting points outside
the upper-right quadrant and
trouble in calculating inter-
point distances together re-
flect a failure to master the
concept of negative numbers
Expert employs an analogy
between negative numbers
and moneys owed in debt to
clear up students' misunder-
standing
be reduced to a smaller set of underlying dispositions or
abilities. For example, Bereiter and Scardamalia (1993) argue
that an expert can be defined as one who works on the lead-
ing edge of his of her knowledge and skill. Thus, an expert
seeks progressively to complicate the model of the problem
to be solved, whereas an experienced nonexpert seeks to re-
duce the problem to fit available methods. The current pop-
ularity of "reflective practice" as a touchstone for teacher
excellence suggests that, in the minds of many, the disposi-
tion toward reflection is central to expert teaching.
Clearly, the question of whether a generalized disposi-
tion toward reflection drives the acquisition of domain
knowledge, and the automatization of well-learned skills,
or whether these cognitive attainments themselves are
causative (i.e., create the foundation for progressive prob-
lem solving and reflection) is a complex one. We merely
wish to point out that a prototype view, unlike a defini-
tional view, seems to accommodate either possibility. If the
putative features of the expert prototype are themselves
manifestations of an underlying disposition or ability, then
the intercorrelations among features in the prototype oper-
ationally define the underlying disposition. If, alterna-
tively, the common disposition itself reflects the
cooperation of the putative features, then the prototype
view gives an account of what it means to be an expert.
Finally, the prototype view provides insight into social-
perception processes related to teaching expertise. If peo-
ple's implicit theories of teaching expertise are embodied
in a summary representation based on expert teachers the
people have seen or heard about, then there should be sys-
tematic differences in the ways in which individuals, com-
munities, and even subdisciplines judge teaching
expertise. In particular, we would expect those with more
restricted experience of excellence in teaching to have nar-
rower conceptions of what it means to be an expert teacher.
This possibility, testable using methods developed in the
study of object categories (Rosch 1973,1978), would seem
to have implications both for the way in which teachers are
evaluated and for the recruitment and development of
teachers from disadvantaged backgrounds.
In general, the view presented here can accommodate a
multitude of prototypes, each based on a different sam-
pling from the population of expert teachers, and each re-
flecting the particular set of experiences of an individual or
community of individuals. Thus, the prototypical expert
teacher of elementary school students may differ systemat-
ically from the prototypical expert teacher of middle or
high school students. Similarly, in the later grades and in
postsecondary education, prototypes may differ as a func-
tion of subject taught. Thus, the social studies teacher, the
art teacher, and the mathematics teacher may be judged
with reference to somewhat different prototypes of expert
teaching. Whether these differing prototypes represent dif-
ferent weightings of essentially the same features, or a dif-
ferent sampling of possible features, is a question for
empirical study.
We have argued for a reconceptualization of expert
teaching based on the ways in which people learn and rea-
son about natural categories. We have outlined a similar-
ity-based account, employing the prototype construct, and
have suggested that it affords a middle ground between a
definitional and an ad hoc conceptualization of teaching
expertise. We have spoken of a prototype view rather than
AUGUST-SEPTEMBER 1995 15
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8. of a prototype model in acknowledgment of the pretheoret-
ical nature of our argument. But why does it matter how
we conceptualize teaching expertise? Aren't we simply
adding to the clutter of psychological constructs on the ed-
ucational-research landscape?
Obviously, we believe that it matters how teaching ex-
pertise is conceptualized—particularly within the commu-
nity of educational researchers. It matters because the
expert teacher is a focal element in the movement toward
excellence in education and because conceptualizations are
not simply descriptive but generative. It is our hope that a
prototype view of teaching expertise will generate innova-
tions in both research and practice. In the realm of practice,
a prototype view may suggest new approaches to the re-
cruitment, training, selection, and assessment of teachers,
as well as to the evaluation of systems directed toward the
previously mentioned. Each of these aspects of educational
practice is predicated, either explicitly or implicitly, on a
model of the effective or expert teacher.
In the realm of research, our (admittedly speculative) ac-
count of the content of the expert-teacher prototype needs
to be validated and, almost certainly, modified. Specifi-
cally, we need to examine which features are important in
people's judgments of expert status, how these features are
weighted and combined, and what the factor structure
among features tells us about the content and structure of
the expert-teacher category. A program of research along
these lines will allow us to explore the social-perception
and self-perception processes of teachers, administrators,
and other "stakeholders" in the educational enterprise.
Conclusion
We maintain that expertise is best thought of as a proto-
typical concept, bound together by the family resemblance
that experts bear to one another. In this article we have
tried to derive, from psychological research on expertise
and abilities, some of the dimensions on which the family
resemblance among expert teachers may be founded. First,
and perhaps most importantly, the prototype expert is
knowledgeable. He or she has extensive, accessible knowl-
edge that is organized for use in teaching. In addition, the
prototypical expert has knowledge of the organizational
context in which teaching occurs and is able to adapt to
practical constraints within the field of teaching. Because
his or her experience in teaching is extensive, the proto-
typical expert is able to perform many of the constituent
activities of teaching rapidly and with little or no cognitive
effort. This routinized skill enables the prototypical expert
to "reinvest" cognitive resources in problem reformulation
and problem solving within the domain of teaching. The
prototypical expert is planful and self-aware in approach-
ing problems—he or she neither jumps into solution at-
tempts prematurely nor follows a solution path blindly.
Finally, the prototypical expert is able selectively to en-
code, combine, and compare information to arrive at in-
sightful solutions to problems in teaching.
Human expertise, like human intelligence, can take a va-
riety of forms, and this fact is reflected in the common lan-
guage of the workplace. This language, which dis-
tinguishes "brainy" individuals from "shrewd" ones and
"knowledgeable" individuals from "clever" ones, implic-
itly acknowledges that there are different types of exper-
tise. In this article, we have argued that these different
types of expertise can be thought of as the correlated at-
tributes of a prototypical concept, and that an individual's
degree of expertise can be expressed in terms of distance
from or resemblance to this prototype. It is our hope that
these ideas will stimulate discussion and investigation of
what it means to be an expert teacher.
Note
Research for this article was supported by contract MDA903-92-K
from the U.S. Army Research Institute and by a grant under the Javits
Act Program as administered by the Office of Educational Research
and Improvement, U.S. Department of Education. Grantees undertak-
ing such projects are encouraged to express freely their professional
judgment. This article, therefore, does not represent the position or
policies of the Government, and no official endorsement should be in-
ferred.
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Received October 25,1994
Revision received May 9,1995
Accepted May 9,1995
I III mm;
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