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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[1]. This definition can be contrasted with more traditional ideas of expertise popularized by Chi[2] 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[1], to tease out the variability within groups of experts. To illustrate, imagine two sushi chefs: one who makes every piece perfectly but routinely makes the same few types over and over (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";[3] or as those approaching a task in a routine versus more flexible way.[4] The notion of adaptive expertise suggests that new problems can be viewed as a platform for exploration in a new problem space[3] and not just an opportunity to practice completing a task more efficiently.[5] 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.[6] 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.[7] 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;[7] workplace error avoidance; and more accurate medical diagnosis.[8] 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.[5]

A Model of Adaptive Expertise

One model of adaptive expertise[4] 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[4] 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.

Trajectories to Adaptive Expertise

Schwartz and his colleagues suggest two possible trajectories to adaptive expertise 1) innovate and then become efficient or 2) become efficient and then practice innovating.[4] In several studies of instructional interventions[9][10], they have demonstrated that trajectory 1, innovation to efficiency, is the better developmental path. Based on this finding, these researchers have suggested that before learning procedures for solving problems, students should first be given the opportunity to innovate and attempt to discover solutions to novel problems without instruction. Following this practice with innovation, students can then benefit from routine practice, with less risk of becoming a routine expert or simply a frustrated novice.

Calculating Adaptive Expertise

Adaptive expertise is tied to the ability to transfer, that is, to apply knowledge to solving problems in a new context by recognizing the underlying similar concepts or principles that govern the given situation. A problem may be composed of factual knowledge, conceptual knowledge, and require transfer. One group[11] of researchers looking specifically at the development of adaptive expertise in bioengineering operationalize adaptive expertise as the following: , based on experimental results, but they do not yet know if these weights are generalizable.

See also


  1. 1.0 1.1 Hatano, G. and K. Inagaki (1986). "Two courses of expertise." Child development and education in Japan: 262–272.
  2. Chi, M., Feltovich, P. and Glaser, R., (1981). Categorization and representation of physics problems by experts and novices [1], Cognitive Science, 5, 121-152.
  3. 3.0 3.1 Miller, R.B. (1978) "The Information System Designer" In Singleton, W.T. (Ed.) The Analysis of Practical Skills. Baltimore, MD: University Park Press.
  4. 4.0 4.1 4.2 4.3 Schwartz, D. L., Bransford, J. D. & Sears, D. (2005). Efficiency and innovation in transfer. Transfer of Learning from a modern multidisciplinary perspective. [2] J. Mestre. Greenwich, CT, Information Age Publishing: 1-51.
  5. 5.0 5.1 Bransford, J. D., Brown, A. L. & Cocking R. R. (2000) How People Learn: Brain, Mind, Experience, and School (expanded edition). Washington: National Academy Press
  6. 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.
  7. 7.0 7.1 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.
  8. 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.
  9. Physically Distributed Learning: Adapting and Reinterpreting Physical Environments in the Development of Fraction Concepts. Taylor Martin & Daniel L. Schwartz. Cognitive Science.[3]
  10. Constructivism in an age of non-constructivist assessments. Daniel L. Schwartz, Robb Lindgren & Sarah Lewis. In T. Duffy and S. Tobias (Eds.), Constructivist instruction: Success or failure. [4]
  11. Pandy, M. G., A. J. Petrosino, et al. (2004). "Assessing Adaptive Expertise in Undergraduate Biomechanics." Journal of Engineering Education 93(3): 211-222.

Further reading

  • Woods, D.D., Johannesen, L., Cook, R.I., and Sarter, N.B. (1994) Behind Human Error: Cognitive Systems, Computers, and Hindsight. Dayton, OH: Crew Systems Ergonomic Information and Analysis Center
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