Taking about expertise and accelerating time to expertise, three basic questions are raised: 1) What makes experts so special? 2) What characteristics set experts apart from rest of us? 3) What traits or abilities define expertise?
In last several posts, I focussed on skill development towards a higher level of expertise and how to accelerate progress towards expertise. While some of those posts like 5 Training Guidelines for Skill Acquisition Towards Unconscious Competence, Mastery Demystified: How Do the Skills of a Novice Develop into Mastery?, 7 Models That Explain How Novice Develops into an Expert
focussed on explaining the process of expertise development, others raised questions on how to accelerate and hasten that progression towards expertise or mastery in posts like Accelerated Expertise with Mentoring and Tough Cases, 6 Training Strategies to Accelerate Expertise from Sternberg’s Model, What Does 9 Famous Training Models Say About Accelerating Expertise?In this post, I will explain the what makes an expert 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 a 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 the 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 a 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 a strong interplay between knowledge structures, processing capability and problem-solving to develop desired expertise.
The pioneering 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).
Several studies reported some characteristics in which experts were different from novices which I am trying to summarize below:
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 characteristics 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 an 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.
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 has the advantage of using a larger chunk of information which may go up to seven chunks (Miller, 1956). This is the 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.
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 the performance of an expert by which experts could recall a large number of patterns by briefly glancing on the chess board as compared to the non-expert.
Halyoak (1991) stated that an expert is particularly skilled in general heuristics search. In several studies, experts have been seen to use a top-down process of information processing which relies on pre-existing information, the context in which the data is presented, past experience and knowledge, expectations, etc. The 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.
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, an individual becomes more selective at a 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).
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.
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 the novice in problem-solving.
One of the key ability that differentiates experts from the novice is that of meta-skills which guide 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).
As per Dreyfus &Dreyfus (1986), experts don’t apply rules or uses any maxims or guidelines. He rather has an 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).
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.