Editing Expert (section)

===Related research===
A number of computational models have been developed in [[cognitive science]] to explain the development from novice to expert. In particular, [[Herbert A. Simon]] and Kevin Gilmartin proposed a model of learning in chess called MAPP (Memory-Aided Pattern Recognizer).{{sfn|Simon|Gilmartin|1973}} Based on simulations, they estimated that about 50,000 [[chunking (psychology)#Chunking as the learning of long-term memory structures|chunks]] (units of memory) are necessary to become an expert, and hence the many years needed to reach this level. More recently, the [[CHREST|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.{{Sfn|Gobet|Simon|2000}}{{Sfn|Gobet|de Voogt|Retschitzki|2004}}

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 recognize 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. Work on "Skilled Memory and Expertise" by [[Anders Ericsson]] and [[James J. Staszewski]] confronts the paradox of expertise and claims that people not only acquire content knowledge as they practice cognitive skills, they also develop mechanisms that enable them to use a large and familiar knowledge base efficiently.{{sfn|Ericsson|Stasewski|1989}}

Work on [[expert systems]] (computer software designed to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted) typically is grounded on 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 judgment and decision-making. However, there is increasing evidence that expertise does not work in this fashion. Rather, experts recognize situations based on experience of many prior situations. They are in consequence able to make rapid decisions in complex and dynamic situations.

In a critique of the expert systems literature, Dreyfus & Dreyfus suggest:{{Sfn|Dreyfus|Dreyfus|2005|p=788}}
<blockquote> 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.</blockquote>

====Skilled memory theory====
{{ref improve section|date=October 2023}}
The role of long-term memory in the skilled memory effect was first articulated by Chase and Simon in their classic studies of chess expertise. They asserted that organized patterns of information stored in long-term memory (chunks) mediated experts' rapid encoding and superior retention. Their study revealed that all subjects retrieved about the same number of chunks, but the size of the chunks varied with subjects' prior experience. Experts' chunks contained more individual pieces than those of novices. This research did not investigate how experts find, distinguish, and retrieve the right chunks from the vast number they hold without a lengthy search of long-term memory.

Skilled memory enables experts to rapidly encode, store, and retrieve information within the domain of their expertise and thereby circumvent the capacity limitations that typically constrain novice performance. For example, it explains experts' ability to recall large amounts of material displayed for only brief study intervals, provided that the material comes from their domain of expertise. When unfamiliar material (not from their domain of expertise) is presented to experts, their recall is no better than that of novices.

The first principle of skilled memory, the ''meaningful encoding principle,'' states that experts exploit prior knowledge to durably encode information needed to perform a familiar task successfully. Experts form more elaborate and accessible memory representations than novices. The elaborate semantic memory network creates meaningful memory codes that create multiple potential cues and avenues for retrieval.

The second principle, the ''retrieval structure principle'' states that experts develop memory mechanisms called retrieval structures to facilitate the retrieval of information stored in long-term memory. These mechanisms operate in a fashion consistent with the meaningful encoding principle to provide cues that can later be regenerated to retrieve the stored information efficiently without a lengthy search.

The third principle, the ''speed up principle'' states that long-term memory encoding and retrieval operations speed up with practice, so that their speed and accuracy approach the speed and accuracy of short-term memory storage and retrieval.

Examples of skilled memory research described in the Ericsson and Stasewski study include:{{sfn|Ericsson|Stasewski|1989}}
* a '''waiter''' who can accurately remember up to 20 complete dinner orders in an actual restaurant setting by using mnemonic strategy, patterns, and spatial relations (position of the person ordering). At the time of recall all items of a category (e.g., all salad dressings, then all meat temperatures, then all steak types, then all starch type) would be recalled in clockwise for all customers.
* a '''running enthusiast''' who grouped together short random sequences of digits and encoded the groups in terms of their meaning as running times, dates, and ages. He was thus able to recall over 84% of all digit groups presented in a session totaling 200–300 digits. His expertise was limited to digits; when a switch from digits to letters of the alphabet was made he exhibited no transfer—his memory span dropped back to about six consonants.
* '''math enthusiasts''' who can in less than 25 seconds mentally solve 2 × 5 digit multiplication problems (e.g., 23 × 48,856) that have been presented orally by the researcher.

====In problem solving====
Much of the research regarding expertise involves the studies of how experts and novices differ in solving problems.{{sfn|Chi|Glasser|Rees|1982}} Mathematics{{Sfn|Sweller|Mawer|Ward|1983}} and physics{{Sfn|Chi|Feltovich|Glaser|1981}} are common domains for these studies.

One of the most cited works in this area examines how experts (PhD students in physics) and novices (undergraduate students that completed one semester of mechanics) categorize and represent physics problems. They found that novices sort problems into categories based upon surface features (e.g., keywords in the problem statement or visual configurations of the objects depicted). Experts, however, categorize problems based upon their deep structures (i.e., the main physics principle used to solve the problem).<ref name=":3">Chi et al. 1981</ref>

Their findings also suggest that while the schemas of both novices and experts are activated by the same features of a problem statement, the experts' schemas contain more procedural knowledge which aid in determining which principle to apply, and novices' schemas contain mostly declarative knowledge which do not aid in determining methods for solution.<ref name=":3" />

====Germain's scale====
Relative to a specific field, an expert has:
* Specific education, training, and knowledge
* Required qualifications
* Ability to assess importance in work-related situations
* Capability to improve themselves
* Intuition
* Self-assurance and confidence in their knowledge

Marie-Line Germain developed a psychometric measure of perception of employee expertise called the Generalized Expertise Measure.{{Sfn|Germain|2006a}} She defined a behavioral dimension in experts, in addition to the dimensions suggested by Swanson and Holton.{{sfn|Swanson|Holton|2009}} Her 16-item scale contains objective expertise items and subjective expertise items. Objective items 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.{{sfn|Germain|2006a}}

* This person has knowledge specific to a field of work.
* This person shows they have the education necessary to be an expert in the field.
* This person has the qualifications required to be an expert in the field.
* This person has been trained in their area of expertise.
* This person is ambitious about their work in the company.
* This person can assess whether a work-related situation is important or not.
* This person is capable of improving themselves.
* This person is charismatic.
* This person can deduce things from work-related situations easily.
* This person is intuitive in the job.
* This person is able to judge what things are important in their job.
* This person has the drive to become what they are capable of becoming in their field.
* This person is self-assured.
* This person has self-confidence.
* This person is outgoing.