Acquisition Of Expertise Research Paper

1. Expertise: Core Concepts

1.1 Definitions

‘Expertise’ denotes the outstanding performance of an individual in a particular domain (e.g., medicine, physics, chess, music). ‘Experts’ thus are persons who, by objective standards and over time, consistently show superior performance in typical activities of a domain. Expert performance is often illuminated by comparison with individuals with limited performance. Such individuals are called ‘novices.’ In contrast to giftedness, expertise is usually considered as acquired special skill in or knowledge of a particular subject through practical experience.

1.2 Scientific Orientation

De Groot’s (1965) seminal work on chess masters pioneered the scientific orientation of research on expertise. The most striking difference between grandmasters and weaker players was revealed in a memory task, in which subjects were presented chess positions for a few seconds and asked to reconstruct them immediately. The experts’ superior recall was explained with specific perceptual structures they held in memory which were closely related to their domainspecific knowledge.

De Groot’s interpretation of these findings directed the future research on expertise from the perspective of information processing theory and cognitive psychology. The focus on the analysis of cognitive correlates of expert performance (perception, memory, knowledge, problem-solving) has since been maintained (Chi et al. 1988, Ericsson and Smith 1991) and has only recently been supplemented by research on the contexts in which expert performance is situated. Three main research topics can be derived from this orientation: (a) modeling of cognitive structures and mechanisms that characterize expert information processing; (b) description of acquisition of expertise; (c) study of instructional means to support acquisition of expertise.

1.3 Methods

Two kinds of empirical research methods are most prominent in studies on expertise: the contrastive approach and retrospective analyses.

In studies using the quasi-experimental contrastive approach (Voss et al. 1986), experts are compared with novices; sometimes subjects with an intermediate level of expertise (‘semi-experts’) are investigated, too. Maximizing the difference in level of expertise helps to point out the effects of expertise. Contrasting characteristics of the groups can be identified, which help to establish theories of expertise and to draw instructional implications for its acquisition. To a certain degree, the cross-sectional comparison can be interpreted as a developmental model ‘from novice to expert.’

In retrospective analyses, two variants can be differentiated. The classical one is the biographical analysis of the development of distinguished experts. The aim to enlighten individual conditions for outstanding success is impaired by the danger of solely anecdotal reports. Ideally, different sources are investigated, or the method is combined with the contrastive approach. The same is true for the second variant, in which contrastive groups retrospectively report about their expertise career—in particular, about the contexts of pursuit with the domain. The main idea with using retrospective reports of experts is that they are the most valid and authentic data source. Evidence exists that retrospective verbal reports can complement cross-sectional methods sensibly.

2. Expertise: Research Results

2.1 Domain Specificity

Experts in virtually any complex domain have intensively practiced for 10 years or more (according to the 10-year rule of necessary preparation), and thus have extensive knowledge at their disposal. Not even the most ‘talented’ individuals can attain outstanding performance without such a period of preparation; most experts have even spent considerably longer.

A main consequence of the 10-year rule is that expertise is extremely domain-specific. Expertise cannot easily be transferred to another domain. For example, experts’ memory is not in general superior to that of novices, though excellence in memorizing new information is the most striking characteristic of experts.

2.2 Expert Memory

As outlined, the central point of departure of research on expertise was evidence for experts’ excellent memory for domain-specific information. In contrastive comparisons of memory performance, large effect sizes can be observed. These differences cannot be explained by general memory factors: no differences occur in control tasks such as digit-span. Thus, it is obvious that domain-specific practice determines the differences. The concept of ‘pattern recognition’ (Chase and Simon 1973) explains experts’ ability to recognize relevant patterns very quickly. It is based on the integration of perception and knowledge through practice. Components of successful pattern recognition are the mechanisms of ‘chunking’ and ‘skilled memory.’

The mechanism proposed in the chunking concept is the integration of small knowledge units into larger chunks, which are labeled with indices that are subsequently used during recall instead of separate knowledge units. Simulations suggest that experts have some 50,000 chunks available. The mechanism proposed in the skilled memory concept relies on the availability of an explicit hierarchical organization of memory that allows new information to be quickly stored in and recalled from long-term memory.

2.3 Expert Knowledge

Experts’ superior memory performance is closely related to their knowledge. Experts excel by both a great amount of knowledge and advantageous knowledge organization in order to make functional and efficient use of their knowledge.

The domain-specificity of expertise implies that the analysis of domain knowledge plays a major role in research on expertise. However, it is a fallacy to equate ‘expert knowledge’ and ‘declarative domain knowledge.’ Though experts have much domain knowledge available, they excel in flexibly using this knowledge, modifying it, or restraining from applying it within certain contextual circumstances. Recent educational studies showed that providing students with much declarative knowledge often leads to inert knowledge rather than to expert knowledge.

The relevance of knowledge for expert performance is so obvious that in studies on expertise only rarely is the concept of knowledge under investigation explicitly defined. However, many different types and qualities of knowledge can be differentiated, each of them with distinguished functionality (De Jong and Ferguson-Hessler 1996). Most important for the acquisition of expertise is the distinction between declarative knowledge (know-what) and proceduralized knowledge (know-how). Proceduralization of knowledge is one of the basic mechanisms for explaining the acquisition of expertise.

3. Acquisition Of Expertise: General Assumptions

3.1 Proceduralization

Many expert actions are highly automated. With growing expertise, knowledge increasingly becomes proceduralized. In his model, Anderson (1982) stated that skill acquisition mainly consists of changing declarative knowledge through practice into proceduralized knowledge. This is achieved through knowledge compilation and rule tuning. Skill acquisition as a basic component of expertise can be described in three phases: declarative, compilation, and tuning. Students first acquire much declarative knowledge, which is later proceduralized and associated with action sequences. Then the skill is automatized and tuned through repeated practice. Anderson assumes innate cognitive structures for the acquisition of knowledge in the declarative phase, which are not elaborated in detail. However, it is plausible that the roles of dispositions and experience vary in different phases. This is influential for educational efforts to foster the acquisition of expertise.

3.2 Changing Roles Of Dispositions And Experience

The changing roles of dispositions and experience are analyzed in the theory of ability determinants of skilled performance (Ackerman, 1986). Similar to Anderson’s model, three phases are distinguished in this theory (cognitive, associative, and autonomous). In the cognitive phase, students are burdened with high demands: understanding task instructions, comprehending relevant goals, expressing strategies, and memorizing knowledge. For mastering these demands, general dispositional abilities play an important role, unless consistent task characteristics allow knowledge proceduralization. If tasks remain inconsistent, general dispositions remain important. During the associative phase, strategies get proceduralized, and compilation of knowledge leads to an increased speed of information processing. Skills are increasingly automatized during the autonomous phase, in which conscious attention for actions is no longer necessary. In ill-defined domains with inconsistent tasks, the autonomous phase is reached only rarely.

The roles of dispositions and experience thus depend on the nature of the domain. Given inconsistent demands, general abilities are important for performance; if demands are consistent, the importance of practice and experience increases. The analysis of individual differences in dispositions are important in order to understand the way in which individuals notice, accumulate, process, and apply information and in order to design learning environments adequately.

3.3 Development Of Generic And Specific Expertise

Beyond the emergence of experience-based proceduralized knowledge, further elaborations of acquisition of expertise are possible. Hatano and Inagaki (1986) distinguished the acquisition of routine expertise and adaptive expertise. Routine expertise can be described through automatization of actions, adaptive expertise through increasing flexibility of actions. Adaptive expertise can emerge only when routine expertise is available. Similarly, Patel and Groen (1991) distinguish generic expertise and specific expertise. Generic expertise denotes the acquisition of declarative knowledge, whereas specific expertise is characterized by action competence based on a sufficient amount of generic expertise. The development of specific expertise passes through several stages. Beginners have declarative knowledge available; intermediates have already compiled their knowledge into simple procedures; generic experts dispose of domainspecific schemata and scripts; and specific experts have enriched these with case experience.

4. Acquisition Of Expertise: Models

4.1 Stage Models

Some models of acquisition of expertise roughly delineate stages of this process. The merit of stage models is that they foster an understanding of components that are important at different moments of the acquisition process. Stage models of acquisition of expertise have existed for almost a century, an early one being Cleveland’s (1907) five-stage model in the domain of chess. Through the domain-specificity of this model, relevant cognitive processes during the acquisition of expertise are described rather precisely. In sum, stage models give hints about typical developments but remain on a molar level. More detailed models are necessary if the acquisition of expertise is to be supported by instruction.

4.2 Deliberate Practice

More detailed approaches theorize about learning processes. Understanding these processes is important, because extensive practice within a domain is a necessary, but not sufficient, condition for acquisition of expertise. Using retrospective methods, it was found that today’s experts differed from other individuals early in their career: they practiced more efficiently, had more committed teachers, and showed higher achievement demands. Ericsson et al. (1993) found that experts were more involved in effortful training activities over a long period of time that had the sole purpose of improving performance. Such activities are called ‘deliberate practice.’ Spontaneously, individuals only rarely engage in deliberate practice, although they recognize that it would improve their performance; they prefer regular activities that are motivated by inherent enjoyment (play) or external rewards (work). Therefore, it is important that teachers persist in deliberate practice, and they can foster the motivation for deliberate practice by offering explicit teaching goals, feedback, and opportunities for gradual improvement through repetition and correction of errors.

The most important reason for guidance by expert teachers is that in all complex domains over time a body of organized experience, in the form of knowledge and produced artifacts, has been accumulated. Through teachers this body is shared with students, because teachers can foresee future skill demands. They know with what method and to what degree of mastery simpler tasks have to be learned to serve as a solid foundation for more complex skills. In other words: students are supported in becoming fully enculturated in a community of expert practice without having to remake all the experiences earlier generations have already made.

4.3 Encapsulation Of Knowledge In Cases

Changes of knowledge during practice are at the core of the encapsulation theory (Schmidt and Boshuizen 1993). This model, developed in the domain of medicine, postulates that acquisition of expertise leads to an integration of declarative and experiential knowledge in ‘encapsulated knowledge.’ Through professional activity and experience with real cases, declarative knowledge about diseases is developed into knowledge structures called ‘illness scripts.’ These are generalized over cases, but nevertheless are closely related to application contexts, because they are based on episodic experiences with real cases. The use of illness scripts leads to quick action without the need to activate declarative knowledge effortfully. The declarative knowledge remains available if necessary but can be neglected in most cases, because the knowledge encapsulated in clinical experience is sufficient.

Empirical evidence for the encapsulation model is based on findings that medical experts improve their performance if presented with additional case data about patients—such information is useless for novices, who are dependent upon abstract declarative knowledge. Additionally, experts’ descriptions of clinical phenomena of diseases contain much more information about cases than do those of novices. Connecting declarative knowledge with case experiences is a crucial component of acquisition of expertise in medicine.

4.4 Experience Of Relevant Episodes

The experience of episodes and their conscious reflection plays an important role for the acquisition of expertise. In the model of dynamic memory, Kolodner (1983) explains how episodes are represented in memory, and how episodic experiential knowledge can be applied. Knowledge is regarded as ‘episodic definitions’ that include the subjective relevance and perception of episodes, as well as knowledge about applicability and application errors. Since experts’ episodic definitions are superior to those of novices, the acquisition of expertise can be interpreted as continuing refinement of episodic definitions based on experience from repeated application of knowledge.

Episodic definitions are represented in Episodic Memory Organization Packets (E-MOPs). An E-MOP can be described as a generalized episode; it contains both information that is common across episodes, and deviations of particular episodes from this general information. Thus E-MOPs include applicable episodic knowledge in which the subjects’ own experiences are integrated.

Two classes of events trigger learning from experience: generalization across episodes, and analysis of errors. After the occurrence of an error in a particular episode, the deviations of this episode from the generalized one are explicitly stored. Such knowledge about errors can later be used to avoid further errors. In educational theory, the explicit use of errors during learning has been discussed controversially. However, because errors inevitably occur in complex domains, dealing with them is very important, in order to enable subjects to cope with new errors.

5. Acquisition Of Expertise: Instructional Consequences

5.1 Case-Based Learning

Both the model of encapsulation and the theory of dynamic memory imply consequences for instructional support of the acquisition of expertise based on the implementation of case-based reasoning during learning.

As learning by experience with cases alters knowledge structures, instructional consequences are evident: the acquisition of expertise can be supported instructionally by fostering reflective application of knowledge through the presentation of complex learning environments in which real application situations occur. Case-based learning is a preferable mode of learning to reach these goals.

Case-based learning stresses the similarity between learning situation and application situation and implies a number of advantages: (a) by dealing with complex initial case problems, students get a notion of the relevance of the ‘to-be-learned’; (b) cases are highly authentic and they are embedded in relevant situative contexts—thus they offer a possibility to make experiences in complex near-to-reality episodes of learning; and (c) multiple perspectives on the same subject matter help to avoid oversimplifications and to enhance the transferability of the to-be-learned.

5.2 Cognitive Flexibility Through Multiple Perspectives

The advantage of multiple perspectives is the focus of the cognitive flexibility theory (Spiro et al. 1989). Employing multiple perspectives aims at making knowledge more transferable. Students should deal with concepts at different times, in different contexts, with different purposes, and in different roles. Thus, oversimplification and too narrow ties to specific contexts are avoided.

Cognitive flexibility theory stresses the importance of multiple contexts in which the knowledge to be acquired is embedded. The theory is particularly relevant for research on expertise, because it mainly deals with advanced knowledge acquisition in ill structured domains. Such domains can be described by: (a) complexity of concepts and cases; and (b) irregularity and large variability of cases. Instruction following the theory of cognitive flexibility aims to induce multiple and, as a consequence, flexible representations of the knowledge, which can be applied in many different contexts. An instructional means to induce this is the technique of landscape crisscrossing, in which the ‘conceptual map’ is explored in many different ways. As a consequence, many facets of the concepts are learned, so that they can be applied in a variety of contexts. Expert flexibility can thus be enhanced.

6. Future Research Issues

The outline of general assumptions and theoretical and instructional models about acquisition of expertise shows that much is already known in that area. Some restrictions were mentioned; to overcome them is a challenge for the future. It is easily predictable that the following issues will play a major role in forthcoming research on acquisition of expertise.

6.1 Longitudinal Studies

Research on acquisition of expertise lacks concomitant longitudinal studies. Factors other than cognitive structures and processes could thus be revealed. For example, reasons have to be identified why some individuals are motivated to work and deliberately practice for many years, whereas many others are not.

6.2 Analysis Of Ill-Defined Domains

For many years, research on expertise has focused on subjects’ performance in solving well-defined problems that frequently can be worked out by routines. Only preliminary steps are taken concerning the flexibility of expert practitioners working in communities of practice in ill-defined domains. In such domains, a constructivistic epistemology might be more appropriate, in which, for example, the veridicality of knowledge is doubted.

6.3 Generalizability Across Domains

As a consequence of the domain-specificity of expertise, an important question has been widely neglected: the question about generalizability of empirical results across domains. Seeking commonalities between experts of different—yet not too different—domains will be a demanding task within the innovative field of comparative research on expertise.

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