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Adaptive expertise is a broad construct that encompasses a range of cognitive, motivational, and personality-related components, as well as habits of mind and dispositions. Generally, problem-solvers demonstrate adaptive expertise when they are able to efficiently solve previously encountered tasks and generate new procedures for new tasks.〔Hatano, G. and K. Inagaki (1986). "Two courses of expertise." Child development and education in Japan: 262–272.〕 This definition can be contrasted with more traditional ideas of expertise popularized by Chi〔Chi, M., Feltovich, P. and Glaser, R., (1981). Categorization and representation of physics problems by experts and novices (), ''Cognitive Science'', ''5'', 121-152.〕 and others, which do not typically consider adaptation to completely novel situations. Its empirical validity has been examined in a number of training and learning contexts. The term was first coined by Giyoo Hatano and Kayoko Inagaki,〔 to tease out the variability within groups of experts. Hatano and Inagaki,〔Hatano, G. and K. Inagaki (1986). "Two courses of expertise." Child development and education in Japan: 262–272.〕 described two types of expertise: routine expertise, or classic expertise, and adaptive expertise. They defined routine expertise as involving mastering procedures in such a way as to become highly efficient and accurate, whereas developing adaptive expertise requires an individual to develop conceptual understanding that allows the "expert" to invent new solutions to problems and even new procedures for solving problems. To illustrate, imagine two sushi chefs: one who makes every piece perfectly but routinely makes the same few types over and over (routine, or classic, expertise), and one produces new menus frequently (adaptive expertise). To some, this is an unfair comparison,as ones' environment supports behavior. For example, the routine of the classic expert sushi chef may be tied to his restaurant environment, and this chef may be able to break out of the routines easily given a different situation. However, the adaptive expert chef clearly demonstrates flexible knowledge and performance of sushi-making. Learning Scientists are interested in adaptive expertise, in part because they would like to understand the types of learning trajectories that may allow practitioners break free from routines when necessary. There is not, however, a true dichotomy between adaptive and classic expertise. Expertise can be thought of as a continuum of adaptive ability. On one end, practitioners can be classified as "routinely skilled" versus "innovatively competent"; as "artisans" versus "virtuosos";〔Miller, R.B. (1978) "The Information System Designer" In Singleton, W.T. (Ed.) The Analysis of Practical Skills. Baltimore, MD: University Park Press.〕 or as those approaching a task in a routine versus more flexible way.〔Schwartz, D. L., Bransford, J. D. & Sears, D. (2005). Efficiency and innovation in transfer. Transfer of Learning from a modern multidisciplinary perspective. () J. Mestre. Greenwich, CT, Information Age Publishing: 1-51.〕 The notion of adaptive expertise suggests that new problems can be viewed as a platform for exploration in a new problem space〔 and not just an opportunity to practice completing a task more efficiently.〔Bransford, J. D., Brown, A. L. & Cocking R. R. (2000) How People Learn: Brain, Mind, Experience, and School (expanded edition). Washington: National Academy Press〕 For example, adaptability enabled the Apollo 13 crew to successfully build an air filter from ill-fitting parts whilst in space, while the TV chef, Jamie Oliver, is able to flamboyantly and creatively produce good food using only simple ingredients. A distinguishing feature of adaptive expertise is the ability to apply knowledge effectively to novel problems or atypical cases in a domain. Holyoak characterized adaptive experts as being capable of drawing on their knowledge to invent new procedures for solving unique or fresh problems, rather than simply applying already mastered procedures.〔Holyoak, K. J. (1991) Symbolic Connectionism: Toward third-generation theories of expertise In K. A. Ericsson & J. Smith (Eds.) Toward a General Theory of Expertise: Prospects and Limits 301-335. Cambridge, UK: Cambridge University Press.〕 Adaptability allows experts to recognize when highly practiced rules and principles do not apply in certain situations in which other solvers might typically attempt to use a previously learned procedure.〔Gott, S., Hall, P., Pokorny, A., Dibble, E., & Glaser, R. (1992) A naturalistic study of transfer: Adaptive expertise in technical domains In D. Detterman & R. Sternberg (Eds.) Transfer on Trial: Intelligence, Cognition, and Instruction 258–288. Norwood, NJ: Ablex.〕 Moreover, studies have shown that this flexibility can result in better performance than that of classically defined experts, resulting in, amongst other things, better technical trouble shooting;〔 workplace error avoidance; and more accurate medical diagnosis.〔Feltovich, P. J., Spiro, R. J., & Coulson, R.L. (1997) Issues of expert flexibility in contexts characterized by complexity and change In P. J. Feltovich, K. M. Ford, & R. R. Hoffman, (Eds.) Expertise In Context 126–146. Menlo Park, California: AAAI Press/MIT Press.〕 John D. Bransford considers this flexible, innovative application of knowledge, in large part, underlies adaptive experts' greater tendency to enrich and refine their understanding on the basis of continuing experience to learn from problem-solving episodes.〔 ==A model of adaptive expertise== One model of adaptive expertise〔 looks at two dimensions along which a learner may develop: efficiency and innovation. Classic experts are defined as being efficient when solving problems that are routine. When presented with a problem that is not routine, or when transferring into a different situation, the adaptive expert may innovate. Schwartz, Bransford and Sears〔 have graphically illustrated these two dimensions of expertise. On the horizontal axis, they plot efficiency of problem solving, and on the vertical axis they plot ability to innovate. In this graph, they identify four important regions: Novice (low efficiency, low innovation), Routine Expert (high efficiency, low innovation), Frustrated or Annoying Novice (low efficiency and high innovation), and Adaptive Expert (high efficiency and high innovation). As originally presented, this graph is intended as a starting point for understanding how educators should guide students' learning and trajectory to adaptive expertise. Schwartz and colleagues suggest that the trajectory, and therefore instruction, should aim for a balance of innovation and efficiency. This work is highly related to their theories of Transfer of Learning and research on instruction that supports transfer and trajectories to adaptive expertise. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Adaptive expertise」の詳細全文を読む スポンサード リンク
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