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1 Introduction

Expertise is domain related and takes years to achieve.

“The nature of expertise has been studied in two general ways. One way is to study truly exceptional people with the goal of under- standing how they perform in their domain of expertise. [...] A second research approach to expertise is to study experts in comparison to novices. This relative approach assumes that exper- tise is a level of proficiency that novices can achieve” (Chi, 2001)

The literature includes many constructs that describe cognitive structures and processes. E.g. Mylopoulos and Regehr (2007) mention prototypes, scripts, encapsulated concepts, instances, semantic networks, semantic axes and probability matrices for the structures. Expert processes could be as heuristics, reasoning strategies, restricted searches and pattern recognition.

According to Alexander (2003), expert/novice theory and research was “framed largely by artificial intelligence and information-processing theory, those traditional research programs initially took shape in the 1970s and 1980s around the problem-solving performance of ex- perts. The primary goal was to determine the characteristics and actions of experts so that these features could be programmed in “intelligent” machines or trained in nonexperts”

2 Scales of expertise

According to Chi (2001) we could distinguish seven levels of expertise that have been adapted from Hoffmann (1998).

Level Description
Naive One who is totally ignorant of a domain
Novice Literally, someone who is new – a probationary member. There has been some minimal exposure to the domain.
Initiate Literally, a novice who has been through an initiation ceremony and has begun introductory instruction.
Apprentice Literally, one who is learning – a student undergoing a program of instruction

beyond the introductory level. Traditionally, the apprentice is immersed in the domain by living with and assisting someone at a higher level. The length of an apprenticeship depends on the domain, ranging from about one to 12 years in the Craft Guilds.

Journeyman Literally, a person who can perform a day’s labor unsupervised, although working

under orders. An experienced and reliable worker, or one who has achieved a level of competence. Despite high levels of motivation, it is possible to remain at this proficiency level for life.

Expert The distinguished or brilliant journeyman, highly regarded by peers, whose

judgments are uncommonly accurate and reliable, whose performance shows consummate skill and economy of effort, and who can deal effectively with certain types of rare or “tough” cases. Also, an expert is one who has special skills or knowledge derived from extensive experience with subdomains.

Master Traditionally, a master is any journeyman or expert who is also qualified to teach

those at a lower level. Traditionally, a master is one of an elite group of experts whose judgments set the regulations, standards, or ideals. Also, a master can be that expert who is regarded by the other experts as being “the” expert, or the “real” expert, especially with regard to sub-domain knowledge.

According to Chi (2006), “Proficiency level can be grossly assessed by measures such as academic qualifications (such as graduate students vs. undergraduates), seniority or years performing the task, or consensus among peers. It can also be assessed at a more fine-grained level, in terms of domain-specific knowledge or performance tests.”

The same author defined ways in which (relative) experts excel:

Ways Description
Generating the best Experts find the best solution to a problem or the best design to solve a task. And they can do it faster.
Detection and recognition Experts can detect patterns and features of "data" and also structures of problems and situation that novices cannot.
Qualitative analysis Experts can analyse a problem qualitatively through the development of a problem representation that includes as well domain-specific as general constraints.
Monitoring Experts have better self-monitoring skill, e.g. can detect both errors and the state of their comprehension.
Strategies Expert are better in selecting appropriate strategies to solve a problem. In addition they can use case-based reasoning, i.e. a data-driven forward chaining approach as opposed to a hypothesis-driven backward chaining approach-
Opportunistic Experts can make use of whatever resource is available
Cognitive effort Experts can retrieve (domain) knowledge with minimal cognitive effort and use automatized procedures. In addition, they know when control is desirable.

On the darker side, experts can also fall short in seven ways:

  • Expertise is domain-limited, i.e. an expert behaves like a novice in a different domain
  • Experts can be overly confident
  • Expert can gloss over detail, i.e. stick to surface features. Novices can recall case relevant details better.
  • Within a domain, experts rely on contextual clues, e.g. in order to perform a diagnosis a doctor needs patient's background information, as opposed to just the features that would define a case.
  • Experts may lack flexibility, i.e. may not accept that a problem has a deep structure that is different from the one that is "accepted" in the domain
  • Experts cannot help novices (predict, judge and advise)
  • Experts may be biases and show functional fixedness, e.g. analyse a multi-dimensional problem only in terms of their domain.

3 Adaptive expertise

“Hatano (1988) describes adaptive expertise as the meaningful and well-connected knowledge that can be applied to new tasks.”. For Alexander (2003) adaptive expertise “is a balance between innovation and efficiency where learners develop meaningful knowledge so they can adapt their skills in response to new situations” (Werner et al. 2013: 12).

According to Mylopoulos and Regehr (2007), the traditional cognitive paradigm defines a (medical) expert in three ways:

  • The expert is a (routine) diagnostician;
  • His/her developmental process is the (automatic and unreflective) accrual of resources through experience
  • The accrued knowledge is a relatively static resource in the expert’s mental processing that is subsequently used and built upon with further experience.

This expertise can be labelled routine expertise and it is the accrual of resources that enable the rapid and uncomplicated solution to typical problems. In opposition, they define adaptive expertise as “not a state of accomplishment, but rather [something that] is best thought of as an approach to practice, an ongoing process of continual reinvestment of cognitive resources in an effort to transform practice and extend the boundaries of knowledge and technique iteratively.” (Mylopoulos and Regehr, 2007).

The distinction between routine and adaptive expertise has an important impact for education, i.e. instructional designs must come up with way to foster both.

4 The Model of domain learning

Alexander (1997, 2003), since it has proven difficult to translate the findings of past generations of expert/novice research into educational practice (Ericsson & Smith, 1991; Hatano & Oura, 2003), developed a model of domain learning (MDL).

MDL includes several components.

  • It distinguishes between domain knowledge (representing the breath of knowledge within a field) and topic knowledge (representing the depth with respect to specific domain topics).
  • MDL hypothesises quantiative and qualitative shifts in surface-level and deep-processing strategies (during text-based learning)
  • MDL distinguises between the enduring individual interest (investment one has in a domain or a facet of it) and the fleeting situational interest (Hidi, 1990) interest in the "here and now".

MDL is then based on the interplay of these components, i.e. the “the interrelation of knowledge, strategic processing, and interest.” (Alexander, 2003: 11). That interplay plays differently at different stages of learning.

In terms of expertise development, Alexander defines three stages:

Acclimation is the initial stage and places demands placed on the student when they orient to a complex, unfamiliar domain. At this stage they lack a cohesive and integrated body of domain knowledge.

Compentence refers to a stage where the individual can demonstrate a foundational body of knowledge that is cohesive and principled in stucture.

Proficiency/Expertise requires a synergy among components. Experts should demonstrate both broad and deep knowledge and also contribute to new knowledge.

5 Bibliography

  • Alexander, P. A. (1997). Mapping the multidimensional nature of do main learning: The interplay of cognitive, motivational, and strategic forces. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement, (Vol. 10, pp. 213–250). Greenwich, CT:JAI Press.
  • Alexander, P. 2003. The Development of Expertise: The Journey from Acclimation to Proficiency. Educational Researcher, 32, 10-14. doi:10.3102/0013189X032008010
  • Bereiter C, Scardamalia M. Surpassing Ourselves: An Inquiry into the Nature and Implications of Expertise. La Salle, IL: Open Court Publishing Company 1993.
  • Bereiter C, Scardamalia M. Learning to Work Creatively with Knowledge. In: De CorteE, VerschaffiL, EntwistleN, Van MerriënboerJ, eds. Powerful Learning Environments: Unravelling Basic Components and Dimensions (Advances in Learning and Instruction Series). Oxford: Elsevier Science 2003;55–68.
  • Billett, S. (2001). Learning in the Workplace: Strategies for Effective Practice. Allen & Unwin, PO Box 8500, St Leonards, 1590 NSW, Australia.
  • Chi, M. T. (2006). Two approaches to the study of experts’ characteristics. in The Cambridge handbook of expertise and expert performance, 21-30. PDF Preprint
  • Collins, H. M., & Evans, R. (2002). The Third Wave of Science Studies Studies of Expertise and Experience. Social studies of science, 32(2), 235-296.
  • Ericsson, K. A., & Smith, J. (Eds.). (1991). Toward a general theory of expertise: Prospects and limits. in Ericsson, K. A., & Smith, J. (eds). Prospects and limits of the empirical study of expertise, Cambridge University Press.
  • Ericsson, K. A., & Smith, J. (1991). Prospects and limits of the empirical study of expertise: An introduction. Toward a general theory of expertise: Prospects and limits, 344.
  • Ericsson KA, Krampe RT, Teschromer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev 1993;100:363–406.
  • Herling, R. W. (2000). Operational definitions of expertise and competence. Advances in developing human resources, 2(1), 8-21.
  • Hatano G & Oura Y. (2003). Commentary: reconceptualising school learning using insight from expertise research. Educational Researcher, 32:26–29.
  • Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60, 549–571.
  • Hoffman, R. R. (1998). How can expertise be defined?: Implications of research from cognitive psychology. In R. Williams, W. Faulkner, & J. Fleck (Eds.), Exploring expertise (pp. 81 – 100 ). New York: Macmillan.
  • Hoffman, R. R., Trafton, G., & Roebber, P. (2005). Minding the weather: How expert forecasters think, Cambridge, MA: MIT Press
  • Holyoak, K. J. (1991). Symbolic connectionism: toward third-generation theories of expertise. Toward a general theory of expertise: Prospects and limits, 301.
  • Mylopoulos, M., & Regehr, G. (2007). Cognitive metaphors of expertise and knowledge: prospects and limitations for medical education. Medical education, 41(12), 1159-1165.
  • Schvaneveldt, R. W., Durso, F. T., Goldsmith, T. E., Breen, T. J., Cooke, N. M., Tucker, R. G., & De Maio, J. C. (1985). Measuring the structure of expertise. International journal of man-machine studies, 23(6), 699-728.
  • Serra T. De Arment, Evelyn Reed and Angela P. Wetzel (2013), Promoting Adaptive Expertise: A Conceptual Framework for Special Educator Preparation Teacher Education and Special Education, Teacher education adn special education 36, 217-230, DOI:10.1177/0888406413489578
  • Werner,Linda Charlie McDowell, Jill Denner (2013). A First Step in Learning Analytics: Pre-processing Low-Level Alice Logging Data of Middle School Students, JEDM - Journal of Educational Data Mining, Vol 5, No 2, HTML/PDF