EXPERTS AND NOVICES

if there is a domain-general ability that leads to expertise in a chosen field. Some studies have suggested that expert chess players also performed well in other fields such as chess journalism and languages (DeGroot, 1965; Elo, 1978). However, there seems to be remarkably little correlation between intelligence and other measures such as subsequent occupation, social status, money earned, and so on, despite a high correlation with success in school tests (Ceci, 1996; Sternberg, 1997a). Schneider, Körkel, and Weinert (1989) found that children who were highly knowledgeable about football, but who were low on measured IQ scores, could nevertheless outperform other children with high IQ scores in reading comprehension, inferencing, and memory tasks if those tasks were in the domain of football. Ceci and Likert (1986) compared two groups of racetrack goers, one of which was expert at predicting what the odds would be on a horse at post time. The expert group, unlike the other non-expert group, used a complex set of seven interacting variables when computing the odds. Each bit of information (one variable) they were given about a real horse or a hypothetical one would change the way they considered the other bits of information (the other six variables). Despite the “cognitive complexity” of the task, the correlation between the experts’ success at the racetrack and their IQ scores was–.07—no correlation at all, in fact.

Swanson, O’Connor, and Carter (1991) divided a group of schoolchildren into two sub-groups based on their verbal protocols while engaged in a number of problem-solving tasks. One sub-group was designated as having “gifted intelligence” because of the sophistication of the heuristics and strategies they employed.

Figure 9.1. Dimensions in which one can understand or study expertise.

The sub-groups were then compared with each other on measures of IQ, scholastic achievement, creativity, and attribution (what people ascribe success or failure to). No substantial differences were found between the two groups. Swanson et al. concluded that IQ, among other measures, is not directly related to intelligence defined in terms of expert/novice representations. If a person shows “gifted intelligence” on a task, this does not mean that that person will have a high IQ.

Does becoming an expert in a domain help you in understanding or developing expertise in another? In a review of the literature, Frensch and Buchner (1999) have pointed out that there is little evidence for expertise in one domain “spreading” to another. Ericsson and Charness (1997) have also stated (although with specific reference to memory) that “Experts acquire skill in memory to meet specific demands of encoding and accessibility in specific activities in a given domain. For this reason their skill is unlikely to transfer from one domain to another” (Ericsson & Charness, 1997, p. 16, emphases added).

On the other hand, there can be skills developed in one domain which can be transferred to another where the skills required overlap to some extent. Schunn and Anderson (1999) tested the distinction between domain-expertise and taskexpertise. Experts in the domain of the cognitive psychology of memory (with a mean of 68 publications), social science experts (mean of 58 publications—task experts), and psychology undergraduates were given the task of testing two theories of memory concerning the effects of massed versus distributed practice. As they designed the experiment they were asked to think aloud. All the experts mentioned the theories as they designed the experiment, whereas a minority of students referred to them— nor did the students refer often to the theories when trying to interpret the results. Domain experts designed relatively complicated experiments manipulating several variables, whereas task experts designed simple ones keeping the variables under control. The complexity of the experiments designed by the students was somewhere in between. Schunn and Anderson claim that, at least in this domain, there are shared general transferable skills that can be learned more or less independently of context.

Nevertheless, we need some way of explaining why one person can be outstanding in a field whereas someone else with the same length of experience is not. Factors that have been used to explain exceptional performance are personality and thinking styles. People vary. Some are better at doing some things than others. Gardner (1983) has argued that there are multiple intelligences that can explain exceptional performance by individuals in different domains. In this view, exceptional performance or expertise comes about when an individual’s particular intelligence profile suits the demands of that particular domain.

Furthermore, people differ in their thinking styles. While some prefer to look at the overall picture in a task or domain, others are happier examining the details. While some are very good at carrying out procedures, others prefer to think up these procedures in the first place (Sternberg, 1997b).

Because people vary in their experience, predispositions, and thinking styles, it is possible to devise tests in which person A will perform well and person B will perform poorly and vice versa (Sternberg, 1998). A might do well in a test of gardening and poorly in an IQ test; B may perform well in the IQ test but poorly in the test of gardening. In this scenario measures of intelligence such as IQ tests are really measures of

achievement. Sternberg has therefore argued that intelligence and developing expertise are essentially the

same thing and that the intelligence literature should be a subset of the expertise literature.

Nevertheless, knowledge differences are not the only measures of individual differences in expertise. There are also differences in people’s ability to gain and exploit knowledge in the first place. The wide variety of interacting variables involved in skilled performance gives rise to individual differences in performance. Sternberg and Frensch have pointed out the same thing, although their argument is based on Sternberg’s own model of intelligent performance. They state (1992, p. 193):

The reason that, of two people who play chess a great deal, one may become an expert and the other a duffer is that the first has been able to exploit knowledge in a highly efficacious way, whereas the latter has not. The greater knowledge base of the expert is at least as much the result as the cause of the chess player’s expertise, which derives from the expert’s ability to organize effectively the information he or she has encountered in many, many hours of play.

To sum up this section: although there are generally poor correlations between exceptional performance and measures of ability or personality, there is a range of factors—including innate ones—that can lead to expertise and exceptional performance. These factors also help to account for wide individual differences in performance on the same task despite the same amount of experience.

SKILL DEVELOPMENT

The last chapter dealt with the acquisition of skill in some detail. Following on from that, the recent literature has tended to emphasise the role of extended practice. For example, Ericsson, Krampe, and Tesch- Römer (1993) argued that expert performance was a function of the amount of deliberate practice in a particular skill such as violin playing. The next few sections look at research into the development of expertise and novice-expert differences that tends to ignore individual differences within each sub-group.

Some researchers have listed various stages people go through as they move from novice to expert. For example, Dreyfus (1997) reviews a five-stage model from novice at stage 1, to advanced beginner in stage 2, competent at stage 3, proficient at Stage 4, leading to expertise at stage 5.

Glaser (1996) has referred to three general stages in what he has termed a “change of agency” in the development of expert performance. The stages are termed external support, transitional, and self-

regulatory. The first stage is one where the novice receives support from parents, teachers, coaches, and so

on, who help structure the environment for the novice to enable him or her to learn. The second stage is a transitional stage where the “scaffolding” provided by these people is gradually withdrawn. The learner at this stage develops self-monitoring and self-regulatory skills, and identifies the criteria for high levels of performance. In the final self-regulatory stage the design of the learning environment is under the control of the learner. The learner might seek out the help of teachers or coaches or other sources of information when he or she identifies a gap or shortcoming in performance, but the learning and the regulation of performance is under the control of the learner.

Another stage model is presented by Schumacher and Czerwinski (1992), this time the model refers to the development of memory representations of systems such as a computer-operating system. The three stages are pretheoretical, experiential and expert. The pretheoretical stage involves retrieval of specific instances from memory based on superficial features of the current situation and the instances stored in memory. This works assuming the superficial features are correlated with the underlying structural features of the system. In the experiential stage an understanding of the causal relations that underpin the system emerges. This stage sees the emergence of abstractions from experience of instances. In the expert stage the learner can make abstractions across various system representations. The knowledge gained is therefore transferable.

An understanding of experts’ mental models of systems has been the basis of research into how one might teach novices the appropriate representations to understand complex systems. A conceptual model of a system such as a computer-operating system can form the basis of instructional materials. However, to make sure that such instructional materials are effective the conceptual models on which they are based should be as accurate as possible. Hence experts are required so that the appropriate models can be generated and used. Cardinale (1991), for example, found that training using such conceptual models of

systems made significant changes in the conceptual, strategic, and syntactic knowledge of low-ability novices. Similarly, in the domain of statistics, Hong and O’Neill (1992) found that presenting appropriate mental models to novices in the form of diagrams or other conceptual instruction before procedural or quantitative instruction produced fewer misconceptions than presenting a purely descriptive explanation.

Schumacher and Czerwinski’s stage model reflects the acquisition of problemsolving schemas outlined in the previous chapter. Novice to expert shifts of this type have been modelled by Elio and Scharf (1990). They developed a computer model called EUREKA that begins by using novice strategies such as means- end analysis and builds up to the conceptual knowledge of the expert. EUREKA models the shift from surface feature understanding of problems, and develops schemas for problem types along with the associated solution procedures. As EUREKA solves problems, it abstracts out the commonalities between them, and the problem features that it identifies change over time to become more discriminating. In this way the model begins to identify features that reflect fundamental principles in physics. Other examples of computer models designed to learn from experience include Larkin, Reif, Carbonell, and Gugliotta (1986) and Keane (1990). Lamberts (1990) used a hybrid system that employed a connectionist system that learned to categorise and identify a problem based on the problem features, and a production system that employed the relevant solution strategy to solve it.

The intermediate effect

In some domains at least there is evidence of an intermediate effect as one of the stages that learners go through on the way to becoming experts. Lesgold and colleagues (Lesgold et al., 1988; Lesgold, 1984) have found evidence for such an intermediate effect, where there is a dip in performance on certain kinds of tasks such that novices outperform the intermediates despite the greater experience of the latter. Lesgold (1984) found that third- and fourth-year hospital residents performed less well at diagnosing X-ray films than either first-and second-year residents or experts. The same phenomenon was found by Patel and Groen (1991) (see also Schmidt, Norman, & Boshuizen, 1990). Lesgold argues that this should not be surprising, as the same phenomenon can be found in the intermediate phase of language learning that children go through where they produce over-regularisations. For example, a child might start off by saying “I went…” but by absorbing the rules for the formation of English past tenses, the child goes through a stage of saying “I goed…” before learning to discriminate between regular and irregular past tenses. Patel and Groen (1991) have argued that the intermediates have a lot of knowledge but that it is not yet well organised. This lack of coherent organisation makes it hard for them to encode current information or retrieve relevant information from long-term memory. Experts have a hierarchically organised schematic knowledge that allows them to pick out what is relevant and ignore what is irrelevant (Patel & Ramoni, 1997). Novices don’t know what is relevant, and stick to the surface features of situations and base their representations on them. Intermediates, on the other hand, try to “process too much garbage”. As a result, the novices in Lesgold’s (1984) study rely on the surface features of the problem (which are usually diagnostic of the underlying features), the experts take the context into account, but the intermediates try to do so and fail.

Raufaste, Eyrolle, and Mariné (1999) have argued that the intermediate effect can be explained by assuming that some kinds of knowledge are only weakly accessible. Much of an expert’s knowledge is implicit, and experience adds this implicit knowledge to the structures originally acquired through the academic study of the domain. Furthermore Raufaste et al. distinguish between two types of experts: basic experts and “super experts”. Basic experts are typical of the population (at least, of radiologists in France) whereas “super experts” refers to the very small number of world-class experts. This distinction is similar to that between expert chess players and Grandmasters. There is, Raufaste et al. argue, a qualitative difference

between basic experts and super experts. If Lesgold had used basic experts in his studies then the U-shaped curve produced by the performance of novices, intermediates, and basic experts on atypical X-ray films would have become a straight line. Figure 9.2 compares Lesgold’s model of the intermediate effect and Raufaste and co-workers’ model.

Although basic experts have typically had less practice than super experts, an appeal to weakly associated memory, and hence implicit or intuitive knowledge, is probably not enough to explain a qualitative gap between basic experts and the super experts. There still seems to be some magic involved in producing the gap.

Other changes in the shift from expert to novice concern the nature of the information presented and what the novice or expert finds useful. Kalyuga, Chandler, and Sweller (1998) have shown that different types of presentation of material can reduce or increase the demands on working memory, or what they term “cognitive load” (Sweller, 1988). In the early stages of learning from textbooks, learners are often presented with text-based explanations of concepts along with diagrams or figures. Sweller and his colleagues have shown that the text and illustrations have to be well integrated in order to reduce cognitive load in the early stages of learning a domain. Kalyuga et al. have demonstrated that the same diagram that has been integrated with the text to reduce cognitive load for novices can increase cognitive load for experts, who do better with the diagram alone without the redundant text (see also Marcus, Cooper, & Sweller, 1996; Ward & Sweller, 1990).

According to Glaser (1996), the final stage in the development of expertise involves a degree of self- regulatory skill. Chi, Glaser, and Rees (1982) found that physics experts were better than novices at assessing a problem’s difficulty. They also have knowledge about the relative difficulty of particular schemas.

KNOWLEDGE ORGANISATION

The fact that experts know more than novices will not come as a shock to many people. Nor will the fact that experts have more experience and skill than novices. In fact these features of expertise are defining features. Of more psychological interest is how that knowledge is organised.

Figure 9.2. Raufaste and co-workers’ (1999) reinterpretation of the intermediate effect.

Moderately abstract conceptual representations

Zeitz (1997) argues that experts’ knowledge is represented at an intermediate level of abstraction known as a moderately abstract conceptual representation (MACR). This level of representation is a compromise between abstractions, such as equations in physics or chemistry, and concrete specific problems. A representation at too abstract a level (such as equations and general principles) is not appropriate for use. For example, a set of equations by itself does not include conditions for their use. On the other hand a representation at too concrete a level is not readily transferable. An expert would have to have a representation of thousands of individual problems, which does not seem to be an economical way of representing problems. Some kind of intermediate representation appropriate to a given domain would seem to be needed. “The development of expertise amounts to becoming facile at processing information at the level of abstraction appropriate for use in a given domain” (Zeitz, 1997, p. 45).

Zeitz suggests that there are a number of benefits of having a level of abstraction that is neither too abstract, and hence shorn of content, nor too concrete and detailed:

• Detailed representations become rapidly inaccessible due to retroactive interference. Abstractions are more stable (see Brainerd & Reyna, 1993).

• A MACR can be retrieved by a broader range of retrieval cues and, therefore, is more accessible. • A MACR is easier to manipulate due to its schematic nature.

• Processing non-essential details in a concrete representation may produce no accuracy gains and may interfere with reasoning.

MACRs provide a link between low-level specific problems and the experts’ extensive domain knowledge. One could argue that device models and causal models are examples of MACRs. Such models help link the details of a system with high-level abstractions. In science education, causal models link bottom-up details with high-level abstractions (equations) (White, 1993). Koedinger and Anderson (1990) found that experts in geometry plan at a higher level of abstraction than basic step-by-step procedures. As a result they are able to skip steps.

Expert computer programmers have been found to have schemas or abstract plans for particular programming tasks. These are often script-like plans or stereotypical “chunks” of code. An “averaging plan” would be the knowledge that an average involves a sum divided by a count (Rist, 1989). Programmers plan a program at an abstract level by co-ordinating and sequencing chunks of code to achieve a desired task (Erlich & Soloway, 1984). Such plans are MACRs because they relate abstract knowledge to its use in stereotypical situations.

Adelson (1981) found that novices categorised computer programming code in terms of the syntax, whereas experts used a more hierarchical organisation based on underlying principles of programming. Chan (1997) also found that experts’ knowledge of architecture was organised in a hierarchical and functional way. Hierarchically organised knowledge structures should allow experts to store MACRs as a kind of basic-level representation and to be flexible in the use they make of their knowledge.

Schema development and the effects of automatisation

Schemas are useful because they allow aspects of knowledge to be abstracted out of the context in which