• Personal Resonance© is a research forum engaged in transforming findings from proven research studies on learning, training, performance and expertise into practical training solutions and practices to 'accelerate time-to-expertise' of organizations and professionals. Aggressive time-to-market drives organizations to develop complex cognitive skills of their employees at faster pace to beat their competitors. Goal of forum is to find and share the answer to that ‘speed’. The forum is trying to develop a core knowledge-base in four areas by systematically assimilating, analyzing and synthesizing the proven research studies in wide range of disciplines like cognitive sciences, neuroscience, psychology, education, learning and brain science, etc.: 1) Accelerated Workplace Expertise: Proven research-based strategies and methodologies to accelerate expertise of organization as a whole through training and learning. 2) Accelerated Professional Expertise: Science-based resonance techniques to accelerate expertise, peak performance and effectiveness of individuals. 3) Strategic Training Management: Experience-based competitive philosophies and processes to manage large-scale complex learning and knowledge operations to produce proficient workforce. 4) Competitive Instructional Design: Advanced instructional and learning design techniques to deliver higher order complex skills like problem solving, critical thinking, decision making, technical troubleshooting.

Making of An Expert: 9 Universal Abilities that Represent Expertise

Making of An expert

Taking about expertise and accelerating time-to-expertise, question comes is what are the characteristics of an expert performer. I thought of providing researcher’s view on experts and what makes the expertise.

Expertise typically has been viewed in terms of expert performance which means expertise in some abilities which are possessed by some and not all (Dror et al., 1993). These abilities may contain range of skills, knowledge and performance characteristics and it may vary from one domain to another.  Ericsson (1994) defines expert level performance as “Usually, if someone is performing at least two standard deviations above the mean level in the population, that individual can be said to be performing at an expert level.” Ericsson & Lehman (1996) further elaborated expertise or expert performance as consistently superior performance in tasks pertaining to the field of expertise.

Klein (1998) describes that expert performance comes by virtue of expert’s ability to integrate information from a large array of accumulated experiences to assess the situation; select a course of action through recognition; and then assess the course of action through mental simulation. This is termed as intuitive capability which only experts are deemed to have.

Dror (2011) summarized capabilities of experts which help them achieve such high performance levels as: “experts need to have well-organized knowledge, use sophisticated and specific mental representations and cognitive processing, apply automatic sequences quickly and efficiently, be able to deal with large amounts of information, make sense of signals and patterns even when they are obscured by noise, deal with low quality and quantity of data, or with ambiguous information and many other challenging task demands and situations that otherwise paralyse the performance of novices” (p. 179).

Hoffman (1998) defines an expert as one whose judgments are uncommonly accurate and reliable, whose performance shows range of skills with minimum efforts and who can deal effectively with certain types of tough cases. It is believed that experts within their domains are skilled, competent and think in qualitatively different ways than do novices (Anderson, 2000; Chi et al., 1988). Glaser and Chi (1988) contested that there is strong interplay between knowledge structures, processing capability and problem solving to develop desired expertise.

The pioneer research by de Groot (1946/1978) and Chase and Simon (1973) on differences in performance of novice and experts has generated a great deal of research like Chi, Glaser, & Farr (1988) and Ericsson & Smith (1991).

9 Universal Abilities Indicative of Expertise

Several studies reported some characteristics in which experts were different from novices which I am trying to summarize below:

1.  Superb mental knowledge representations:

Experts are driven by knowledge contained in specific mental representations and schemas which they have acquired by learning and experience (Russell, 1910). One of the most noticeable characteristic of experts that set them apart from non-expert is to use efficient mental representation to reduce cognition load and use of computationally efficient methods.

They re-package the information in such a way that it is used more efficiently while performing certain tasks. As expert gains more experience, he becomes good at chunking. Czerwinski et al. (1992) suggest that experts use ability called ‘perceptual unitization’. The unitization creates new entities and neural processing that causes discrete components to join together in mental representation (Schyns and Rodet, 1997). This new organization is considered to play an important role in expertise (Goldstone, 2000; Shiffrin and Lightfoot, 1997). The ability of experts to reason, to plan out, and to evaluate consequences of possible actions has been seen to be associated with their superb mental representation of the relevant information about the situation. Such mental representations and information processing many times give rise to automatization (Schneider and Shiffrin, 1977; Shiffrin and Schneider, 1977). This is the stage where can perform them effortlessly.

2. Ability to handle complexity very well

Although the experts are not gifted with any better short-term memory than non-experts, they have the ability to use your short-term memory effectively. Research shows that expert have the advantage to using larger chunk of information which may goes up to seven chunks (Miller, 1956). This is characteristic difference as compared to non-experts. The chunking of large information or several steps into unified routine or schemata has also resulted in experts’ ability to handle complexity and solve complex problems during which they can respond quickly and also able to do more.

3. Ability to efficiently store and recall information

The historical studies on expertise started mostly around the game of chess. deGroot (1966) compared the characteristics of experts vs. novice chess players. Chase and Simon (1973) noted that knowledge structure plays an important role in performance of an expert by which experts could recall large number of patterns by briefly glancing on the chess board as compared to non-expert.

4. Ability to process information efficiently with minimum cognitive load

Halyoak (1991) stated that an expert is particularly skilled in general heuristics search.  In several studies experts have been seen to use top-down process of information processing which relies on pre-existing information, context in which the data is presented, past experience and knowledge, expectations, etc. Top-down information allows efficient and effective processing of the bottom-up data (Dror, 2011).  Researchers (Chi, Feltovich, & Glaser, 1981; Kraiger, Ford, & Salas, 1993) suggest that individuals who are proficient within a particular domain have an extensive and well-organized knowledge base that is constructed through experience.

5. Ability to selectively filter relevant information

The most important feature of expertise is experts’ ability to pay attention selectively and focus on the important or relevant information while filtering the irrelevant (Wood, 1999). Experts possess a better overall picture and being able to discriminate between relevant and irrelevant information.  Experts have abilities and knowledge that has been acquired by repeated exposure to the tasks they need to perform. With time, they tune into and pick out the important and relevant information, learning how to detect and use it well while ignoring and filtering out everything else (Kundel and Nodine, 1983; Wood, 1999). With expertise, individual becomes more selective at higher rate.  While a novice’s tendency is to make sense of the information, an expert may jump to the critical information (de Valk and Eijkman, 1984). As a result, experts can perform quickly and efficiently even in environments that contain little data or noise (Gold et al., 1999; Lu and Dosher, 2004).

6. Deep problem solving skills

Larkin, Dermott, Simon & Simon (1980) in their studies noticed that experts were classifying based on deep structure whereas students classify physics problems based on their surface features. They also suggested that experts form an immediate representation of the problem that systematically cues their knowledge, whereas novices do not have this kind of orderly and efficient access to their knowledge.

7. Ability to recognize patterns

Experts have the ability to notice meaningful patterns and features of a given knowledge which novice are not able to recognize at first glance (NRC, 2000). Several studies established in aviation (Endsley, 2006), sports (Williams and Ward, 2003), physics problem solving (Chi, 2006) and medical diagnosis (Norman, Eva, Brooks, & Hamstra, 2006) established that experts possess the ability of early detection and matching of patterns.  They further have the ability to identify new problem types and can actively work toward finding the solution for it (Meig, 2009). Studies by Bereiter and Scardamali (1986) and Chi and Glaser (1988) found that experts outshine in their ability to recognize knowledge patterns much faster than novice in problem solving.

8. Better strategies and meta-skills

One of the key ability that differentiate experts from novice is that of metaskills which guides experts to monitor, adjust and analyze one’s thinking, learning and knowledge during problem solving. Further experts are found to be more learning goals oriented than non-experts and knows how to set the learning goals based on the available resources (Bereiter & Scardamalia, 1993).

9. Intuitive decision making and intuition

As per Dreyfus &Dreyfus (1986), experts don’t apply rules, or uses any maxims or guidelines. He rather has intuitive grasp of situations based on his deep tacit understanding. One key aspect of this level is that individual relies on intuition and analytical approach is used only in new situations or unrecognized problems not earlier experienced. Experience based deep understanding provides him very fluid performance. At this stage skills becomes automatic that even expert is not aware of it. Based on priori experience, they can even come up with solution for new never experienced before situations (DiBello, Lehman, Missldine, 2011).

What it Means to Training Designers and Strategists?

As a training strategist and training professional you may need to know the characteristics of experts, translate those characteristics into the jobs of the target employee groups and then scale it back to define ‘desired proficiency’ expected in a given job. This will help you to develop higher-order training objectives and performance goals for the group.

Stay tuned for practical training strategies to design programs to accelerate expertise.


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About Raman K. Attri

Raman K. Attri is a complex learning strategist, a transformational training consultant and a researcher with over 20 years of experience in engineering, management and technical training. His primary area of focus is to provide strategic directions to organizations in implementing next-generation competitive training strategies. His research interests include complex learning, accelerated expertise and advanced instructional design. He is also the founder of Personal Resonance©, a research forum with a charter to transform proven research studies on accelerated expertise into organizational training practices. His training and learning solutions are strongly founded in system engineering techniques applied to large-scale training programs. Equipped with scientific training methods, he innovated two research-backed complex learning frameworks namely SEAT© (Systems Engineering Approach to Training) and ProBT© (Proficiency Based Training) methodology primarily meant for organizations to accelerate development of complex cognitive skills of their employees systematically at faster rate. He is highly passionate about learning. He holds Professional Doctorate in Corporate Training, MBA in Operations Management and Executive MBA in Customer Relationship Management. Currently he is pursuing another Doctorate degree from Southern Cross University. His personal interests involve writing and painting.
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