The Models of Skill Acquisition and Expertise Development

A Quick Reference of Summaries

Raman K. Attri

• Publisher: Speed To Proficiency Research: S2Pro©
• ISBN:
  9789811189883
• Topic: Education
• Subtopic: Education Curricula

The book offers condensed summaries of twenty-three major models of skill acquisition and expertise development presented by leading researchers during the last half a century of classic and new research. This book presents new researchers in learning, training, cognitive sciences or education disciplines with a big picture starting point for their literature review journey. The book presents an easy to understand taxonomy of twenty-three models which can give new researchers a good bird's eye view of existing models and theories, based on which they can decide which direction to dig further. The reviews in this book are complemented with over 200 authentic sources which a researcher read for detailed and deeper dive and set the direction for further exploration. This book would also act as an essential reference for training & learning professionals and instructional designers to design research-based training curriculum to develop skills of their staff.

CHAPTER 1
LEARNING, SKILL ACQUISITION AND EXPERTISE DEVELOPMENT

The processes of learning, skill acquisition, and expertise development are intricately interwoven when the discussion is about an employee’s job performance. However, these terms do not always get their due distinction. On a broader scale, learning is assumed to be the most fundamental process in an individual’s overall development. At the same time, it is also believed that learning may not always translate into performance at a job. While skill acquisition processes underscore learning as the most fundamental mechanism in an individual’s overall development, expertise development is viewed as a journey in which learning and skill acquisition play an undeniable role in delivering the required performance.

1.1 LEARNING AND PERFORMANCE

In general, task performance and job performance are considered to be direct functions of the abilities or skills acquired by an individual. Hunter (1986), in one of his studies, reported from over 3000 cases that abilities impact job knowledge and work samples (performance), which, in turn, affect the supervisory ratings of performance. He noted that abilities did not have a direct effect on how employee supervisors rated them. Rather, the abilities impacted how job knowledge and work samples were accomplished. This theory supports that knowledge and skills are mediating variables in an individual’s performance. McDaniel, Schmidt & Hunter (1988; 1986) suggested that knowledge, skills, and abilities are the primary determinants of job performance. Kanfer & Kantrowitz (2002, p. 32), based on an analysis of several studies, observed that ‘individual differences in general cognitive ability may account for more variance in performance when performance is defined in terms of skill acquisition or job proficiency.’

Subsequently, several classical studies established that the knowledge, skills, and abilities acquired by an individual as a result of job experience or training are the primary determinants of his/her performance in any job (McDaniel, Schmidt & Hunter 1988; Qui’nones, Ford & Teachout 1995). Campbell (1990) explained that job performance could be distinguished from one person to another with the help of three direct determinants—declarative knowledge (knowledge of facts, principles, and procedures), procedural knowledge and skills (knowing what to do and actually doing the task), and motivation (choice to exert effort, how much and how long). The level of training, education, experience, range of skills, and amount of practice determine the level of declarative and procedural knowledge or skills possessed by an individual. This study supports the link between knowledge, skills, abilities, and experience toward individual performance.

Motowidlo, Borman & Schmit (1997) presented a model of task performance and contextual performance in which they theorized that the intervening variables to performance are knowledge, skills, and work habits, which are basically acquired through experience. They postulated that task knowledge (procedures, judgment, heuristics, rules, and decisions), task skills (using technical information, solving problems, and making judgments etc.), and task work habits (pattern of behaviors, tendencies, choices individuals make in a situation, motivational aspects, persistence, and planning, etc.) affect the task performance. Similarly, contextual knowledge (knowledge about the effective actions in situations that call for volunteering, helping, supporting, persisting, and defending etc.), contextual skills (ability to carry out actions deemed effective in a situation), and contextual work habits (ways of handling conflicts, tendencies, and interpersonal styles etc.) determine the contextual performance.

Thus, each of these kinds of performance ( i.e., task performance, job performance, and contextual performance) is a direct function of the knowledge, skills, and abilities of an individual/employee, though it may not be entirely attributed to them. Several studies have proposed that learning has a central role in the performance concept and is an underlying mechanism in the acquisition of knowledge and skills (Campbell 1990; Hesketh & Neal 1999; London & Mone 1999). Learning is viewed as a long-term behavioral change, which positively impacts performance. Some studies show that individual performance improves with learning in terms of the time spent on the job (Avolio, Waldman & McDaniel 1990; McDaniel, Schmidt & Hunter 1988; Qui’nones, Ford & Teachout 1995). Learning a task or skill leads to ultimate performance, be it behavioral, task, outcome, or job performance. Thus, learning is considered a significant dimension of performance. Though the role of learning in improving performance is well understood, the nature of the relationship between the two has been debated extensively. For example, performance and learning share a direct relationship during training and an inverse relationship after training. During any training intervention, learning is contended to be more important than in-training performance, while after the training, at-the-job performance is contended to be more important than learning. In a study, Bjork (2009, p. 313) expressed that performance during a training event may not be the right indicator of job performance:

Performance during training is often an unreliable guide to whether the desired learning has actually happened. Considerable learning can happen across periods when performance is not improving and, conversely, little or no learning can happen across periods when performance is improving markedly.

From this perspective, learning becomes more important than performance during training. Soderstrom & Bjork (2015, p. 193) termed learning as ‘the relatively permanent changes in behavior or knowledge that support long-term retention and transfer’ and performance during training as ‘the temporary fluctuations in behavior or knowledge that are observed and measured during training or instruction or immediately thereafter.’ Furthermore, the performance during a training intervention may not be the desired performance required at the job. However, what matters is the job performance of an individual after training. Bjork (2009, p. 319) highlights the challenge as: ‘The problem for a training organisation is to maximise performance when it matters, that is, after training [sic] and, [sic] specially, when individuals are deployed.’ The corollary to this assertion is that accelerating performance within training may have only short-term effects, which may be defined as ‘expediting acquisition performance today does not necessarily translate into the type of learning that will be evident tomorrow’ (Soderstrom & Bjork 2015, p. 193). However, the performance on the job (i.e., outcomes and behaviors) matters more than learning, as suggested by Sonnentag & Frese (2002, p. 6):

One might argue that what ultimately counts for an organisation is the individuals’ performance and not their learning—although learning might help to perform well. This line of reasoning stresses that learning is a highly relevant predictor of performance but is not performance itself.

A review of the literature on performance metrics indicates that learning is one of the key determinants of performance. A noteworthy observation is that knowledge and skill acquisition appear to be inseparable parts of job performance. In their study on pilots tackling direct problems in their job, Dreyfus & Dreyfus (2005) noted that an individual/employee (in this case, a pilot) passed through five stages during the course of acquiring experience: novice, advanced beginner, competent, proficient, and expert. The representation was based on the assumption that performance or skill proficiency was a continuum in which novice was at one end and expert at the other. The overall goal of knowledge and skill acquisition is to develop a higher level of competence, proficiency, or expertise of an individual, which then translates into performance through their behaviors/actions or results.

1.2 EXPERTISE

Expertise has been studied from several different perspectives.

According to one of the perspectives, expertise can be defined from three dimensions—development of expertise, knowledge structures possessed by experts, and reasoning processes used by experts (Hoffman 1998). Novak (2011) expressed that expertise can be defined through four lenses: attributes, cognition, stages, and community. The “attributes” lens defines expertise based on several years of study in different fields, which reveals some of the ways in which experts operate. The “cognition” lens defines expertise based on cognitive studies of experts in fields such as chess, music, and sports, etc., which reveal how experts think and organize knowledge. The “stages” lens views expertise as a sort of progression of knowledge and skills. The “community” lens views expertise as a quality that emerges from the interactions individuals have with fellow individuals and the environment.

One stream of literature is almost entirely devoted to novice–expert differences. The pioneering research by De Groot (1965; 1966) and Chase & Simon (1973) on the differences in the performance of novices and experts in the game of chess has generated plenty of research studies (for instance, studies by Chi, Glaser & Farr 1988). Several studies reported some characteristics in which experts were different from a novice and have attempted to explain the nature of expertise. For instance, some researchers believed that experts within their respective domains are skilled, competent, and think in qualitatively different ways than novices (Anderson 2000; Chi, Glaser & Farr 1988). Klein (1998) describes that expert performance comes by virtue of an expert’s ability to integrate information from a large array of accumulated experiences to assess a situation; select a course of action through recognition; and then assess the course of action through mental simulation. This is termed as an intuitive capability, which only experts are deemed to have. Thus, exclusivity is one of the features of expertise that sets it apart from other constructs in skill acquisition. Expertise typically has been viewed as the abilities possessed by only a few people and usually are not common enough to be possessed by all (Dror 2011). The abilities may comprise a range of skills, knowledge, and performance characteristics and may vary among domains. Dror (2011, p. 179) summarized the capabilities of experts that help them achieve 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.

A continuum view of the novice-to-expert transition has usually been used to explain the characteristics and actions of experts. Based on the continuum, Dreyfus & Dreyfus (2005) contend that an expert operates and behaves differently from a novice, advanced beginner, competent, or proficient performer. An expert exhibits experience-based deep understanding. According to Dreyfus and Dreyfus, ‘An immense library of distinguishable situations is built up on the basis of experience’ (Dreyfus & Dreyfus 1986, p. 32). Thus, experts treat knowledge in context, and possess the ability to recognize the relevance. Experts do not apply rules or use maxims or guidelines. Dreyfus and Dreyfus suggest that, individuals, at an expert level, rely on intuition and use an analytical approach only in new situations or while encountering unrecognized problems not experienced earlier. They showed that, at the expert stage, actions are rather effortless and out of intuition and tacit knowledge. Their problem-solving is based on an intuitive grasp of situations, the ability to recognize the features in a given situation, and a conceptual understanding of the underlying principles that govern those situations or occurrences. Expert performers possess the ability to see what needs to be achieved and how to achieve it. An expert ‘focuses in on the accurate region of the problem without wasteful consideration of a larger range of unfruitful possibilities’ (Benner 1984, p. 34). Similarly, experts possess the capacity to make subtle discriminations that proficient performers can not, which enables them to adapt their problem-solving approach based on the situation. Experts, based on their prior experience, can even come up with a solution for situations they have never experienced before (DiBello & Missildine 2011). They can see alternative approaches in a given situation. At this stage, their skills become so automatic that even they are not always conscious of the fact. Their performance is fluid. Therefore, it is believed that experts could move effortlessly between intuitive and analytical approaches and possess the ability to see the overall picture.

In almost all the expertise and proficiency development models, proficiency progression has been considered to be unidimensional, especially in staged models. Such approaches view proficiency as a trait possessed by an individual. However, researchers appear to recognize that experts develop ‘specialist tacit knowledge’ by becoming socially embedded in a group of experts (Collins 2011, p. 255). Collins (2011) and Collins et al. (2006) proposed the construct of interactional expertise. They proposed two additional dimensions to expertise: (1) the ...

Table of contents

• CONTENTS
• PREFACE
• THE BOOK
• THE AUTHOR
• ABBREVIATIONS
• CHAPTER 1: LEARNING, SKILL ACQUISITION AND EXPERTISE DEVELOPMENT
• CHAPTER 2: CLASSIFYING THE MODELS OF SKILL, PROFICIENCY AND EXPERTISE DEVELOPMENT
• CHAPTER 3 STAGE-BASED MODELS
• CHAPTER 4: PRACTICE-, TIME-, OR TASK-BASED MODELS
• CHAPTER 5: FACTOR-BASED MODELS
• CHAPTER 6: EXPERT MODELING-BASED MODELS
• CHAPTER 7: COGNITION-BASED MODELS
• CHAPTER 8: PHASES OF SKILL ACQUISITION: INTEGRATING VARIOUS VIEWS
• RELEVANT PUBLICATIONS BY THE AUTHOR
• REFERENCES