Definitions

expert

expert system

Computer-based system designed to respond like a human expert in a given field. Expert systems are built on knowledge gathered from human experts, analogous to a database but containing rules that may be applied to solving a specific problem. An interface allows the user to specify symptoms and to clarify a problem by responding to questions posed by the system. Software tools exist to help designers build a special-purpose expert system with minimal effort. An outgrowth of work in artificial intelligence, expert systems show promise for an ever-widening range of applications. There are now widely used expert systems in the fields of medicine, personnel screening, and education.

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An "expert" is someone widely recognized as a reliable source of technique or skill whose faculty for judging or deciding rightly, justly, or wisely is accorded authority and status by their peers or the public. An expert, more generally, is a person with extensive knowledge or ability in a particular area of study. Experts are called in for advice on their respective subject, but they do not always agree on the particulars of a field of study. An expert can be, by virtue of training, education, profession, publication or experience, believed to have special knowledge of a subject beyond that of the average person, sufficient that others may officially (and legally) rely upon the individual's opinion. Historically, an expert was referred to as a sage. The individual was usually a profound philosopher distinguished for wisdom and sound judgment.

Introduction

Experts have a prolonged or intense experience through practice and education in a particular field. In specific fields, the definition of expert is well established by consensus and therefore it is not necessary for an individual to have a professional or academic qualification for them to be accepted as an expert. In this respect, a shepherd with 50 years of experience tending flocks would be widely recognized as having complete expertise in the use and training of sheep dogs and the care of sheep. Another example from computer science is that an expert system may be taught by a human and thereafter considered an expert, often outperforming human beings at particular tasks. In law, an expert witness must be recognized by argument and authority.

Expertise

Expertise consists of those characteristics, skills and knowledge of a person (that is, expert) or of a system, which distinguish experts from novices and less experienced people. In many domains there are objective measures of performance capable of distinguishing experts from novices: expert chess players will almost always win games against recreational chess players; expert medical specialists are more likely to diagnose a disease correctly; etc.

There are broadly two academic approaches to the understanding and study of expertise. The first understands expertise as an emergent property of communities of practice. In this view expertise is socially constructed; tools for thinking and scripts for action are jointly constructed within social groups enabling that group jointly to define and acquire expertise in some domain.

In the second view expertise is a characteristic of individuals and is a consequence of the human capacity for extensive adaptation to physical and social environments. Many accounts of the development of expertise emphasise that it comes about though long periods of deliberate practice. In many domains of expertise estimates of 10 years experience or 10,000 hours deliberate practice are common. Typically recent research on expertise emphasises the nurture side of the nature versus nurture argument. It should be noted that some factors not fitting the nature versus nurture dichotomy are important as well. These typically are biological but not genetic factors, and include starting age, handedness, and season of birth.

A number of computational models have been developed in cognitive science to explain the development from novice to expert. In particular, Herbert Simon and Kevin Gilmartin proposed a model of learning in chess called MAPP (Memory-Aided Pattern Recognizer). Based on simulations, they estimated that about 50,000 chunks (units of memory) are necessary to become an expert, and hence the many years needed to reach this level. More recently, the CHREST model (Chunk Hierarchy and REtrieval STructures) has simulated in detail a number of phenomena in chess expertise (eye movements, performance in a variety of memory tasks, development from novice to expert) and in other domains.

Work on expert systems typically works from the premise that expertise is based on acquired repertoires of rules and frameworks for decision making which can be elicited as the basis for computer supported judgement and decision-making. However, there is increasing evidence that expertise does not work in this fashion. Rather, experts recognise situations based on experience of many prior situations. They are in consequence able to make rapid decisions in complex and dynamic situtions relying on recognition-primed decision-making.

In a critique of the expert systems literature, Dreyfus and Dreyfus suggest:

If one asks an expert for the rules he or she is using, one will, in effect, force the expert to regress to the level of a beginner and state the rules learned in school. Thus, instead of using rules he or she no longer remembers, as the knowledge engineers suppose, the expert is forced to remember rules he or she no longer uses. … No amount of rules and facts can capture the knowledge an expert has when he or she has stored experience of the actual outcomes of tens of thousands of situations.”

An important feature of expert performance seems to be the way in which experts are able to rapidly retrieve complex configurations of information from long-term memory. They recognise situations because they have meaning. It is perhaps this central concern with meaning and how it attaches to situations which provides an important link between the individual and social approaches to the development of expertise.

In line with the socially constructed view of expertise, expertise can also be understood a form of power; that is, experts have the ability to influence others as a result of their defined social status.

Value

Plato’s ‘Noble Lie’, albeit arguably a notion of ideological propaganda, is often where the debate begins concerning ‘expertise’. Plato did not believe most people were clever enough to look after their own and society’s best interest, so the few ‘clever’ people of the world needed to lead the rest of the flock. Therefore, the idea was born that only the elite should know the truth in its complete form and the rulers, Plato said, must tell the people of the city ‘The Noble Lie’ to keep them passive and content, without the risk of upheaval and unrest. Thus, the creation of an elite form of specialist and authoritative knowledge came about.

In contemporary society, doctors and scientists, for example, are considered to be experts in that they hold a body of dominant knowledge that is, on the whole, inaccessible to the layman (Fuller: 2005: 141). However, this inaccessibility and perhaps even mystery that surrounds expertise does not cause the layman to disregard the opinion of the experts on account of the unknown. Instead, the complete opposite occurs whereby members of the public believe in and highly value the opinion of medical professionals or of scientific discoveries (Fuller: 2005: 144), despite not understanding it.

Germain's scale

Marie-Line Germain (Germain, 2006) developed a measure of perception of employee expertise called the Generalized Expertise Measure (GEM). She also found that there is a behavioral dimension found in "experts", in addition to the dimensions suggested by Swanson and Holton (2001). The 16-item scale contains objective expertise items and subjective expertise items. Objective items (the first 5 items of the measure below) were named Evidence-Based items. Subjective items (the remaining 11 items from the measure below) were named Self-Enhancement items because of their behavioral component.

  1. This person has knowledge that is specific to his or her field of work.
  2. This person shows that they have the education necessary to be an expert in his/her field.
  3. This person has knowledge about his/her field.
  4. This person has the qualifications required to be an expert in his/her field.
  5. This person has been trained in his or her area of expertise.
  6. This person is ambitious about their work in the company.
  7. This person can assess whether a work-related situation is important or not.
  8. This person is capable of improving himself or herself.
  9. This person is charismatic.
  10. This person can deduce things from work-related situations easily.
  11. This person is intuitive in the job.
  12. This person is able to judge what things are important in his/her job.
  13. This person has the drive to become what he or she is capable of becoming in his/her field.
  14. This person is self-assured.
  15. This person has self-confidence.
  16. This person is an expert who is outgoing.

note:This material is copyrighted and must not be used without citing the author (Germain, 2006). With a sample of N=307, the scale reliability (internal consistency, Cronbach Alpha coefficient) of the 16-item scale was high (.91 for the five Evidence-Based items and .92 for the eleven Self-Enhancement items).References:

  • Germain, M. L. (2006). Development and preliminary validation of a psychometric measure of expertise: The Generalized Expertise Measure (GEM). Unpublished Doctoral Dissertation. Barry University, Florida.
  • Germain, M. L. (2006, April). Perception of Instructors’ Expertise by College Students: An Exploratory Qualitative Research Study. American Educational Research Association annual conference, San Francisco, CA. April 7-11.
  • Germain, M. L. (2006, February). What experts are not: Factors identified by managers as disqualifiers for selecting subordinates for expert team membership. Academy of Human Resource Development Conference. Columbus, OH. February 22-26.
  • Germain, M. L. (2005, February). Apperception and self-identification of managerial and subordinate expertise. Academy of Human Resource Development. Estes Park, CO. February 24-27.
  • Swanson, R. A., & Holton III, E. F. (2001). Foundations of Human Resource Development. San Francisco: Berrett-Koehler Publishers, Inc.

Contrasts and comparisons

An expert differs from the specialist in that a specialist has to be able to solve a problem and an expert has to know its solution. The opposite of an expert is generally known as a layperson, while someone who occupies a middle grade of understanding is generally known as a technician and often employed to assist experts. A person may well be an expert in one field and a layperson in many other fields. The concepts of experts and expertise are debated within the field of epistemology under the general heading of expert knowledge. In contrast, the opposite of a specialist would be a generalist, somebody with expertise in many fields.

The term is widely used informally, with people being described as 'experts' in order to bolster the relative value of their opinion, when no objective criteria for their expertise is available. The term crank is likewise used to disparage opinions. Academic elitism arises when experts become convinced that only their opinion is useful, sometimes on matters beyond their personal expertise.

By a similar token, a fear of experts can arise from fear of an intellectual elite's power. In earlier periods of history, simply being able to read made one part of an intellectual elite. The introduction of the printing press in Europe during the fifteenth century and the diffusion of printed matter contributed to higher literacy rates and wider access to the once-rarefied knowledge of academia. The subsequent spread of education and learning changed society, and initiated an era of widespread education whose elite would now instead be those who produced the written content itself for consumption, in education and all other spheres.

In contrast to an expert, a novice (known colloquially as a newbie or 'greenhorn') is any person that is new to any science or field of study or activity or social cause and who is undergoing training in order to meet normal requirements of being regarded a mature and equal participant.

Developmental characteristics

Some characteristics of the development of an expert have been found to include

  • At a minimum usually 10 years of consistent practice, sometimes more for certain fields
  • A characterization of this practice as "deliberate practice", which forces the practitioner to come up with new ways to encourage and enable themselves to reach new levels of performance
  • An early phase of learning which is characterized by enjoyment, excitement, and participation without outcome-related goals
  • The ability to rearrange or construct a higher dimension of creativity. Due to such familiarity or advanced knowledge experts can develop more abstract perspectives of their concepts and/or performances.

Use in literature

Mark Twain defined an expert as "an ordinary fellow from another town". Will Rogers described an expert as "A man fifty miles from home with a briefcase."

See also

General: Scholar, Know-how, Skill, Competence, Excellence, Technical government, Insider, Tutor expertise in adult educationCriticism: Anti-intellectualism, Denialism

Notes

References

  • Black Tech Expert
  • Dreyfus, H. and Dreyfus, S. (2005) Expertise in real world contexts, Organization Studies, 26(5), 779-792.
  • Ericsson, K. A. (2000). Expert Performance and Deliberate Practice
  • Ericsson, K. Anders, Neil Charness, Paul Feltovich & Robert R. Hoffman (Eds.), 2006: Cambridge handbook on expertise and expert performance. Cambridge, UK: Cambridge University Press.
  • Germain, M. L. (2006). Development and preliminary validation of a psychometric measure of expertise: The Generalized Expertise Measure (GEM). Unpublished Doctoral Dissertation. Barry University, Florida.
  • Germain, M. L. (2006, April). Perception of Instructors’ Expertise by College Students: An Exploratory Qualitative Research Study. American Educational Research Association annual conference, San Francisco, CA. April 7-11.
  • Germain, M. L. (2006, February). What experts are not: Factors identified by managers as disqualifiers for selecting subordinates for expert team membership. Academy of Human Resource Development Conference. Columbus, OH. February 22-26.
  • Germain, M. L. (2005, February). Apperception and self-identification of managerial and subordinate expertise. Academy of Human Resource Development. Estes Park, CO. February 24-27.
  • Gibbons, M. (1994). The new production of knowledge: the dynamics of science and research in contemporary societies. London: SAGE Publications.
  • Gobet. F. & Campitelli, G. (2007). The role of domain-specific practice, handedness and starting age in chess. Developmental Psychology, 43, 159-172. Available online at Retrieved 22 July 2007.
  • Gobet. F. & Chassy, P. (in press). Season of birth and chess expertise. Journal of Biosocial Science. Available online at Retrieved 22 July 2007.
  • Gobet, F., de Voogt, A. J., & Retschitzki, J. (2004). Moves in mind: The psychology of board games. Hove, UK: Psychology Press.
  • Gobet, F., & Simon, H. A. (2000). Five seconds or sixty? Presentation time in expert memory. Cognitive Science, 24, 651-682.
  • Goldman, A. I. (1999). Knowledge in a Social World. Oxford: Oxford University Press.
  • Mieg, Harald A. (2001). The social psychology of expertise. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Shanteau, J., D.J. Weiss, R.P. Thomas, and J.C. Pounds, Performance-based assessment of expertise: How to decide if someone is an expert or not. European Journal of Operational Research, Volume 136, Number 2, 16 January 2002, pp. 253-263(11).
  • Simon, H. A., & Gilmartin, K. J. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5, 29-46.
  • Sowell, T. (1980). Knowledge and decisions. New York: Basic Books, Inc.
  • Swanson, R. A., & Holton III, E. F. (2001). Foundations of Human Resource Development. San Francisco: Berrett-Koehler Publishers, Inc.
  • Tynjala, P. Towards expert knowledge? A comparison between a constructivist and a traditional learning environment in the university. Educational Research, 1999. ece.uncc.edu.

  • Fuller, S. (2005). The Intellectual. Icon Books
  • Collins, R. (1979). The Credential Society
  • Dewey, J. (1927). The Public and its Problems
  • Nettleton, S., Burrows, R. and O’Malley, L. (2005) ‘The mundane realities of the everyday use of the internet for health, and their consequences for media convergence.’ Sociology of Health and Illness. 27: 7: 972-992
  • Wasa, M. Hacker

Further reading

Books and publications

  • Ikujiro Nonaka, Georg von Krogh, and Sven Voelpel, Organizational Knowledge Creation Theory: Evolutionary Paths and Future Advances. Organization Studies, Vol. 27, No. 8, 1179-1208 (2006). SAGE Publications, 2006. DOI 10.1177/0170840606066312
  • Lennart Sjöberg (2001), Limits of Knowledge and the Limited Importance of Trust. Risk Analysis 21 (1), 189–198. doi 10.1111/0272-4332.211101
  • Barbara K. Hofer and Paul R. Pintrich, The Development of Epistemological Theories: Beliefs about Knowledge and Knowing and Their Relation to Learning. Review of Educational Research, Vol. 67, No. 1 (Spring, 1997), pp. 88-140 doi 10.2307/1170620
  • B Wynne, May the sheep safely graze? A reflexive view of the expert-lay knowledge divide. Risk, Environment and Modernity: Towards a New Ecology, 1996.
  • TH Davenport, et al., Working knowledge . 1998, knowledge.hut.fi.
  • Mats Alvesson, Knowledge work: Ambiguity, image and identity. Human Relations, Vol. 54, No. 7, 863-886 (2001). The Tavistock Institute, 2001.
  • Peter J. Laugharne, Parliament and Specialist Advice, Manutius Press, 1994.
  • Jay Liebowitz, Knowledge Management Handbook. CRC Press, 1999. 328 pages. ISBN 0849302382
  • C. Nadine Wathen and Jacquelyn Burkell, Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, VL. 53, NO. 2. PG 134-144. John Wiley & Sons, Inc., 2002. DOI 10.1002/asi.10016
  • Nico Stehr, Knowledge Societies. Sage Publications, 1994. 304 pages. ISBN 0803978928Patents
  • , Basic expert system tool, Steven Hardy et al., Filed November 25, 1987, Issued February 7, 1989.

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