Introduction and Purpose
The authors rationale for clarifying an expert teaching prototype, or a model of teaching expertise, is to better inform teaching performance standards-to distinguish those teachers who are expert at teaching students from those who are merely experienced at teaching students. The authors seek a middle ground between the current definitional or ad hoc models of teaching expertise prevalent in educational research. The premise of their argument is that no well-defined standard exists which all experts meet and no nonexperts meet. The authors propose instead a prototype view of expert teaching that could provide a way of thinking about teaching expertise that incorporates standards (such that not every experienced practitioner is an expert) but allows for variability in the profiles of individual experts.
The Categorization Model
Sternberg & Horvath (1995) define a category as a set of objects perceived to be similar, objects that "seem to go together". Similarity is considered to be an increasing function of shared features and a decreasing function of nonshared features. Unlike a category based on an all-or-nothing definition, similarity-based categories tend to be "fuzzy" on the issue of whether a particular object is a valid category member. The authors suggest that similarity-based categories exhibit a graded structure wherein some category members are "better" or more typical exemplars of the category than are others.
One way of capturing the fuzziness of a similarity-based category is to postulate a central or "prototypical" category member that serves as a summary representation of the category. The prototype may be thought of as the central tendency of feature values across all valid members of the category. According to this prototype model, judgements about category membership are made by computing the similarity between the object in questions and the prototype of the category. The higher the similarity, the higher the subjective probability that the object belongs to the category.
In addition to their probabilistic nature, prototype-centered categories have three properties; (1) different members of a category may resemble the category prototype on different features, (2) there is differential weighting of features in the computation of overall similarity to the prototype, and (3) the features that make up a category prototype may be correlated-they may occur together in category members at a level greater than chance. The second property suggests that because features are weighted differently, an object may be judged to be a category member by virtue of relatively few shared features (if those features are heavily weighted), and leaves open the possibility of a feature that is necessary but not sufficient for category membership.
Sternberg & Horvath (1995) present several disclaimers along with their thumbnail sketch of a prototype model. The categorization model of a prototype does not specify the composition of the prototype representation, the computation of similarity, the nature of a decision rule for category assignment, and the precise nature of a feature. The authors also admit that although a prototype models can account for a number of effects observed in studies of human learning and reasoning about similarity-based categories, they may give a less adequate account of learning and reasoning about classes of highly dissimilar objects and ad hoc categories.
Content of the Expert Prototype
The authors attempt to define the specific features that make up the prototype expert teacher. Sternberg and Horvath (1995) describe a defensible, rather than a definitive, prototype based on psychological research of expert performance across a variety of domains. They suggest there are three basic ways in which experts differ from novices in their domain of expertise: (1) experts bring knowledge to bear more effectively on problems than do novices, (2) experts solve problems more efficiently and do more in less time, than do novices, and (3) experts are more likely to arrive at novel and appropriate solutions to problems than are novices. These three differences, knowledge, efficiency, and insight, comprise Sternberg and Horvath’s (1995) current best guess about the features upon which a prototype of the expert teacher should be founded.
Concept Map of the Expert Teaching Prototype
The following concept map is a visual representation of the expert teaching prototype described by Sternberg & Horvath (1995). Figure 1. Prototype of Expert Teacher
Although this feature of teacher expertise seems obvious, the authors include different types of knowledge necessary for the expert teacher. The first type of knowledge an expert teacher requires is content knowledge; knowledge of the subject matter to be taught. In addition, teachers need pedagogical knowledge; knowledge of how to teach, which includes strategies for motivating students, managing groups in a classroom setting, and how to design and administer assignments and tests. Finally, expert teachers need pedagogical-content knowledge; knowledge of how to teach that is specific to what is being taught. The authors describe studies of expert teaching that have concluded that expert and novice teachers differ in the organization of their domain-relevant knowledge. Expert teachers seem to possess knowledge that is more thoroughly integrated, in the form of scripts, propositional structures, and schemata, than is the knowledge of novice teachers. The lesson plan, or agenda, is an important form of schematically organized teaching knowledge, integrating knowledge of content to be taught with knowledge of teaching methods. The well developed planning structure of the expert teacher includes global (content-non-specific) components, local (content-specific) components, and decision elements that make the lesson plan responsive to expected and unexpected events. Global components might include routines for collecting assignments and presenting new material. Local components might include routines for presenting particular concepts. Decision elements represent contingencies in the planning structure such as anticipated questions and the local components needed to address those questions. By contrast, novice teachers are found to have less complex, less connected planning structures.
In addition to well-organized knowledge of content and pedagogy, Sternberg and Horvath (1995) make a compelling argument for knowledge of the social and political context in which teaching occurs. The authors suggest that practical knowledge of this sort is largely neglected in studies of expertise in general. They point out that expertise is embedded in a field as well as a domain. An expert in the domain of teaching must know subject-matter content and pedagogy, and 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 may need to know how to insulate their classroom from machinations at the administrative or social level (e.g., pressure to improve standardized test scores). In the era of shrinking school budgets, expert teachers need to be proficient at "working the system" to obtain needed services or resources for their students. The authors borrow Polanyis (1967) description of tacit knowledge to describe this practical ability, or "savvy", that they believe is a nontrivial component of teaching effectiveness and should be part of a prototypical representation of teaching expertise.
Research in other domains (e.g., business executives, sales people and college students) has shown that tacit knowledge generally increases with experience on the job, is unrelated to IQ, predicts job performance better than does IQ, personality and cognitive style, and overlaps across fields. Studies have shown that tacit knowledge is important to expertise on the job. One of the ways in which tacit knowledge helps people succeed is by helping them be labeled as experts. The authors propose that 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. Sternberg & Horvath (1995) do not suggest that the ability to get labeled as an expert is a sufficient condition for expertise. One can be revered as a expert and yet be essentially vacuous in the message or performance he or she delivers. Yet, the ability to get labeled as an expert is an aspect of expertise which enables an expert to continue to develop in an environment that is conducive and supportive of that expertise. Sternberg & Horvath (1995) summarize that tacit knowledge is an important feature of teaching expertise that warrants further psychological study.
Expert teachers are able to solve problems more efficiently within their domain of expertise than novices. They can do more in less time, and with less apparent effort than can novices. Sternberg & Horvath (1995) discuss the efficiency of expert teachers in relation to their ability to automatize well-learned skills, as well as their ability to effectively plan, monitor, and revise their approach to problems.
Research on automatic versus controlled processing point to the apparent ability of experts to stretch the limits of human cognitive processing; they seem to do more at a given level of expenditure of resources. Certain types of cognitive skills may become automatic with extensive, focused practice. Thus, by virtue of their extensive experience, expert teachers are able to perform tasks effortlessly that novices can perform only with effort.
Sternberg & Horvath (1995) emphasize that the capacity to automate well-learned routines cannot be separated neatly from the schematic organization of teaching knowledge in any reasonable account of the mental processes involved in expert performance. Expert teachers in a study by Sabers et al. (1991) did a better job of monitoring fast-paced, simultaneously presented classroom events than did novices. In explaining this expert performance, one may argue that the experience of the expert teachers enabled them to handle (a) more information per unit of time than did the novices, or (b) the same amount of information but at a lower level of cognitive effort. The first conclusion suggests and increase in "bandwidth", through the automatization of classroom monitoring, and the second emphasizes the role of knowledge organization, or a store of meaningful patterns, corresponding to classroom situations, and that the number and accessibility of these patterns made their recognition less resource-consuming for experts than for novice teachers.
Research on expertise has shown that experts and novices differ in metacognitive or executive control of cognition. Experts typically spend more time trying to understand the problem to be solved, while novices invest less time trying to understand the problem and more in actually trying out different solutions. For example, in their approach to classroom discipline problems, expert teachers are found to be more planful than are novices. Experts tended to emphasize the definition of discipline problems and the evaluation of alternative hypotheses, whereas novices tend to be more "solution oriented" and less concerned with developing an adequate model of the discipline problem. Literature on the reflective practice of teaching also emphasizes the importance of metacognitive or executive processes in expert teaching. The disposition toward reflection, typically defined as continuous learning through experience, indicates that expert teachers use new problems as opportunities to expand their knowledge and competence.
To summarize the efficiency of expert teachers, the authors emphasize the nonindependance of the putative features of the expert prototype. The experts capacity to automatize well-learned routines is related to the experts’ capacity to be reflective and to exert effective executive control over problem solving. The cognitive resources that are "saved" through automatization do not simply make problem solving easier for the expert. Rather, these resources are freed for higher level cognition that is beyond the capacity of the nonexpert. This "reinvestment" function distinguishes experts from experienced nonexperts by their reinvestment of cognitive resources in the progressive construction of more nearly adequate problem solving models. Thus, whereas novices seek to reduce problems to fit available models, experts seek progressively to complicate the picture, continually working on the leading edge of their own knowledge and skill.
Although both experts and novices apply knowledge and analysis to solve problems, experts are more likely to arrive at creative solutions to those problems, solutions that are both novel and appropriate. Expert teachers do not 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.
The processes of insight used in creative problem solving are referred to as "selective encoding", "selective combination", and "selective comparison". Selective encoding involves distinguishing information that is relevant from information that is irrelevant to problem solution. An expert recognizes that a piece of information others assumed was important is in fact unimportant, or that a piece of information that others assumed 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 discussion that are likely to further instructional goals and those that are merely diverting for students.
Selective combination involves combining information in ways that are useful for problem solution. An expert recognizes 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, combined with a drop in academic performance, may signal that the student is working too many hours at a part time job.
Finally, selective comparison involves applying all the information acquired in another context to a problem at hand. It is here that acquired knowledge becomes important, both with respect to quantity and organization. An insight based on selective comparison is noticing, mapping, and applying an analogy to solve a problem. For example, an expert teacher may exploit an analogy that is familiar to students (e.g., finding numbers in a telephone directory) and those that are new to their students (e.g., conducting a structured database search).
Selective encoding, selective combination, and selective comparison all provide the basis for insightful solutions. The capacity to solve problems insightfully is likely to be correlated with the organization of domain knowledge. An expert teachers ability to solve a classroom problem by analogy to a known case will be critically dependent on both the quantity of stored cases and the way in which those cases are organized for retrieval.
Implications of the Prototype View
The authors main proposal is that teaching expertise be viewed as a natural category, structured by the similarity of expert teachers to one another and represented by a central exemplar or prototype with reference to which decisions about the expert status of a teacher are made. The authors imply that by viewing teaching expertise as a prototype, we can distinguish experts from experienced nonexperts in a way that acknowledges diversity, and the absence of a set of individually necessary and jointly sufficient features of an expert teacher. Sternberg & Horvath (1995) point to the "fuzziness" of similarity-based categories, and suggest that when such categories are organized around a prototype, two equally valid members of the category may resemble each other much less than they individually resemble the prototype. Thus, a teacher with 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. However, the authors caution that a prototypical view should broaden the picture of expert teaching without succumbing to a creeping relativism that treats all alternatives as commensurable. The prototypical view is consistent with the existence of features that are necessary (but insufficient) for membership in the category. For example, a teacher who lacks content knowledge is likely to be judged a nonexpert, even if his or her tacit knowledge about the social and political milieu is extraordinarily high.
A second implication of the authors prototypical 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. The current popularity of "reflective practice" as a touchstone for teacher excellence suggests that, in the minds of many, the disposition toward reflection is central to expert teaching. The question of whether a generalized disposition toward reflection drives the acquisition of domain knowledge, and the automatization of well-learned skills, or whether these cognitive attainments themselves are causative is a complex one. The authors suggest that a prototype view, unlike a definitional 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 operationally define the underlying disposition. If 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. In general, the prototypical view can accommodate a multitude of prototypes, based on peoples implicit theories of teacher expertise based on different samples from the population of expert teachers, and each reflecting the particular set of experiences of an individual or community of individuals. Thus, the expert elementary teacher may differ systematically from the expert high school english teacher. Whether these differing prototypes represent different weighting of essentially the same features, or a different sampling of possible features, is a question for empirical study.
Sternberg & Horvath (1995) acknowledge the pretheoretical nature of their argument by emphasizing a prototypical view, rather than a prototypical model of teacher expertise. It is the authors intention to have a generative effect on the conceptualization of teaching expertise in both research and practice. In practice, it is the authors hope that a prototypical view may suggest new approaches to the recruitment, training, selection, and assessment of teachers, as well as the evaluation of systems directed towards these activities. In the realm of research, the authors call for both validation and modification of the expert teacher prototype. Specifically, research should be directed at examining those features that are important in peoples judgement of expert status, how these features are weighted and combined, and what factor structure tells us about the content and structure of the expert teacher category.
Limitations of this Article
Although they criticize current research for presenting definitional or ad hoc models of the expert teacher, the authors do not provide alternate models of the expert teacher with which to compare their prototype. A more convincing argument may have included an alternate model of the expert, or ideal teacher, and addressed features that were lacking, or those which the current model better addresses. However, Sternberg & Horvath (1995) do not present their prototype model as complete, nor should it be viewed as such. The authors present their prototype as a "best guess", based on extensive psychological research. Sternberg and Horvaths (1995) model appears to be missing at least two nodes: the expert teachers personal belief structure, and their ability to be self-regulated.