Expert-novice Differences Research Articles

Expert-Novice Differences in Memory, Abstraction, and Reasoning in the Domain of Literature

In this study, I explore the information processing abilities associated with expertise in literature. Literary experts were superior to novices in gist level recall, the extraction of interpretations, and the breadth of aspects addressed of literary texts but not of a scientific text. Literary experts also surpassed novices in reasoning about literary texts: They generated arguments with greater hierarchical depth and made more complex comparisons between texts. Experts appeared to form composite representations of literary texts, consisting of a basic novice-like representation augmented by a rich derived representation. The convergence of evidence from a variety of tasks indicates that literary experts' extended experience has led to the development of a knowledge base organized by highly abstract structures that enhance experts' memory, analysis, and reasoning within their domain.

A model for predicting the outcomes of basketball games

AbstractWe investigated the task of predicting the outcomes of sporting events, in particular, basketball games. In two experiments, college students predicted the outcomes of a series of National Basketball Association (NBA) games. Following each prediction, the subject received feedback in the form of the actual outcome of the NBA game. After a period of initial learning about the relative strengths of the teams, the subjects were surprisingly successful in their predictions. We examined expert–novice differences by comparing the published predictions of professional oddsmakers to the predictions of the experimental subjects. The average predictions by the subjects were approximately as accurate as the predictions of experts. Finally, we applied a mathematical model, developed originally as an account of simpler learning experiments, to the subjects' responses. We found that the course of the subjects' learning about the teams was well‐described by this model.

Is Test Theory Ready for a New Generation of Tests?

Originally published in Contemporary Psychology: APA Review of Books, 1994, Vol 39(4), 425–426. Reviews the book, Test Theory for a New Generation of Tests edited by Norman Frederiksen, Robert J. Mislevy, and Isaac I. Bejar (1993). In this edited volume, Frederiksen, Mislevy, and Bejar seek to “consider a variety of directions in which standard test theory might be extended”, because, as they argue, classical test theory does not adequately reflect our current understanding of human cognition, nor does it address applied educational questions. In addition to the focus on test theory, several of the chapters assume prior knowledge of the cognitive psychology literature in problem solving, expertnovice differences, rule learning, schema theory, and complex conceptual understanding. Unfortunately, despite the promise implicit in the title, the chasm between psychology and education has not yet been bridged. The new generation of tests, and the theory to support it, are still beyond the horizon. (PsycINFO Database Record (c) 2006 APA, all rights reserved)

Action video games and informal education: Effects on strategies for dividing visual attention

Two experiments investigated the effects of video game expertise on divided visual attention in college students. Divided attention was measured by using response time to targets of varying probabilities at two locations on a computer screen. In one condition the target appeared 10% of the time in one location (low probability position), 80% of the time in the other location (high probability position), and 10% of the time in both locations. In the other condition the target appeared 45% of the time in each position (equiprobable or neutral positions) and 10% of the time in both positions. The subjects for Experiment 1 represented two extremes of video game skill (labeled experts, novices), whereas the subjects for Experiment 2 were an unselected group with a continuous distribution of video game performance (labeled more skillful, less skillful). Experiment 1 established that video game experts were similar to novices in manifesting an attentional benefit (manifested in faster response time) at the high probability position (relative to a neutral or equiprobable position). However, unlike novices, experts did not show an attentional cost (manifested as slower response time) at the low probability position (again relative to a neutral position). Experts also had significantly faster response times than novices at both the 10% and 80% positions, but not at the 45% position. Experiment 2 established that video game experience was a causal factor in improving strategies of divided attention. Five hours of play on a video game called Robotron produced a significant decrease in response time at the 10% location, the locus of the expert-novice difference in Experiment 1.

Teachers and subtraction problems: expert-novice differences revisited

Teachers and subtraction problems: expert-novice differences revisited

How experts solve a novel problem in experimental design

Research on expert-novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their area of expertise by dividing the problem into a number of subproblems that are solved in a specified order. The lack of domain knowledge is compensated for by using abstract knowledge structures and domain-specific heuristic strategies. However, the quality of their solutions is considerably lower than the quality attained by experts who were familiar with the type of problem to be solved. The results suggest that when experts are confronted with novel problems as compared with familiar problems, their form of reasoning remains intact, but the content of their reasoning suffers due to lack of domain knowledge.

Searching for the minimal essential information for skilled perception and action.

A common concern for both cognitive/computational and ecological/dynamical models of human motor control is the isolation of the minimal essential information needed to support skilled perception and action. In perception isolating essential features of the optic flow field, which are reliably informative regarding the nature of current events, from nonessential features provides a valuable step towards understanding how the computational complexity of perceptual information processing may be reduced to manageable levels and how relatively direct linkages of low dimensionality may be established between information and control variables. Likewise, in the study of action, discrimination of the movement features that remain immutable (invariant?) across changes in task conditions from the variables that are situationally determined provides a principle insight into the structural framework upon which skilled movement is built. Controversy abounds, however, in the study of perception and action as to whether features isolated as informative and immutable are centrally represented (in the form of a template or program) or are rather directly picked up (in the case of perceptual variables) or are simply an emergent consequence of the underlying dynamics (in the case of action variables). In this paper some examples of putative minimal essential information sources in perception and action are provided, strategies for uncovering such sources are discussed, and attention is directed, with the use of some recent data collected on natural skills, to some systematic expert-novice differences in the utilization of essential information and control variables. Expert-novice differences are highlighted because of the insight they may provide regarding the nature of perceptual-motor skill acquisition.

Concepts, Categories, and Psychological Science

Why are psychologists not often vailed on for their expertise in connection with the larger problems of society? The nature of expertise is analyzed in the framework of a theory of concept formation and categorization. Expert-novice differences in approaches to problems, and. more generally, the nature of abstraction and judgment, are interpreted in terms of cognitive processes that operate by computations on an instance-based composite memory. Applied to “growing pains” of psychological science, the theory has implications for graduate training and the direction of research support.

A Cognitive Framework for Integrated Embedded Training and Decision Aiding

This research has developed a theoretically-based cognitive model and design framework for Integrated Decision Aiding/Training Embedded Systems (IDATES). Based on a review of empirical studies of noviceexpert differences and of theoretical and computational models of skill acquisition, we defined a three-stage cognitive hierarchy model as the basis for our IDATES framework. The levels of novice, intermediate, and expert are discrete stages which differ along two primary dimensions: problem representation and problem-solving procedure. Both decision aiding and training must be targeted to the problem representation and cognitive processes of the user/trainee. Thus, there must be three levels of decision aiding targeted to novice, intermediate, and expert decision makers. Furthermore, there are two types of training: incremental training to improve performance within each of the three expertise levels, and representational training to elicit a jump to the next higher level of problem representation. Two implications arise from the IDATES cognitive model. First, integrated cognitive/behavioral task analyses are able to drive both the embedded training requirements and the decision aiding requirements, although the three skill levels must be separately addressed. Second, a single integrated architecture can underlie all the decision aiding and embedded training components of a given IDATES application.

New light on novice—expert differences in physics problem solving

It is now widely accepted that intelligent problem solving requires access to a source of domain‐specific knowledge, and the existence of a control system to constrain the manner in which domain knowledge is searched. An example of a domain which has been intensively investigated is problem solving in physics. A number of researchers have proposed mutually incompatible theories to explain the search control strategies used in this area. This paper looks at backward inference, schema‐guided forward inference, and planstacking (a form of meta‐level backward inference followed by forward inference at the object level). It also considers a proposal to introduce neural network modelling into the short‐term memory component of a production rule‐based problem solver.One widely reported finding (Larkin, McDermott, Simon & Simon, 1980), is that novice problem solvers in the domain of physics use backward inference as a search control technique, while experts use forward inference. The backward‐to‐forward inference shift has indeed become so entrenched that attention has already shifted from whether to how it occurs. In spite of its widespread acceptance, the empirical basis for this claim is somewhat fragile, and a major aim of the present study was to re‐examine it using much larger samples of experts and novices than have been employed in the past, and a methodology which does not depend upon protocol analysis.Our data show that both experts and novices exhibit a forward inference rather than a backward inference order of equation generation. Experts were more likely to be able to plan their solutions at a descriptive meta‐level than novices. While existing theories are able to account for some of these findings, none of them is completely satisfactory in explaining the full range of data.

Domain Knowledge: Evolving Themes and Emerging Concerns

Research in domain knowledge has progressed to the point where it should undergo further examination. As a basis for that reexamination, the nature of domain knowledge and its relationship to other knowledge forms are considered. The status of research on expert-novice differences, on the relationship between domain and strategic knowledge, and on misconceptions is also explored. Clarification of the boundaries between expert and novice performance, a research agenda that incorporates domain and strategic knowledge as partners in knowledge acquisition and use, and specification of the terminology used to examine misconceptions are called for.

Situated Cognition: Stepping out of Representational Flatland

Descriptions of novice-expert differences, reasoning strategies, explanation-based learning, etc. are descriptions of how people create and use models within a representational language, when interacting with their environment in cycles of perceiving and acting. To complement these descriptions, we need to understand how representational languages are created.

Expertise, Visual Search, and Information Pick-up in Squash

The visual search characteristics of expert and novice squash players were compared in two experiments. In the first experiment subjects were required to predict the forthcoming direction and force of an opponent's stroke from a film display. This film display was designed to simulate the normal display available to the defending player in squash and involved the use of variable temporal cut-offs to force the subjects to use advance cues in their prediction. Systematic differences in the information pick-up of the experts and novices were observed on the film task but these differences were achieved with only relatively minor between-group variations in visual search strategy. In the second experiment, set in the natural field setting, no expert-novice differences in either fixation distribution, order, or duration were observed on a comparable prediction task. This provided further support for the conclusion that the limiting factor in the perceptual performance of the novices is not an inappropriate search strategy but rather an inability to make full use of the information available from fixated display features. Some practical implications of these findings for the squash coach and player are considered.

Highs Are to Lows as Experts Are to Novices: Individual Differences in the Representation and Solution of Standardized Figural Analogies

Two experiments compared the strategies used by high and lower scorers on standardized figural analogy tests to represent and solve problems. In Experiment 1, subjects freely sorted completed analogy (A:a::B:b) problems into categories. High scorers categorized problems largely on the basis of well-constrained spatial transformations between problem terms; Low scorers sorted according to perceptual (i.e., shape) similarities among the figures constituting the problems. Experiment 2 compared the two groups' solution strategies in terms of specific patterns of eye movements used in viewing problem and answer terms. High scorers appeared to view problem terms in a more efficient fashion than did lower scorers. The lower scorers focused on noncritical figural relations (mapping figure A onto figure B), and they devoted more resources to processing the answer alternatives than did the high scorers. The pattern of results supports the view that high scorers tend to work in a more forward, or constructive, fashion, whereas lower scorers more often work backwards, using a response-elimination approach. Taken together, these findings converge to suggest specific aptitude-related differences in the representation and solution of standardized figural analogy problems. These differences bear some similarities to expert-novice differences in a number of other problem-solving domains.

Expert-Novice Differences in Teaching: A Cognitive Analysis and Implications for Teacher Education

Livingston and Borko examine the implications of theory and research on pedagogical expertise for teacher education. They describe an investigation of the thoughts and actions of a small number of expert and novice teachers. Differences among the teachers are analyzed from two perspectives: teach ing as a complex cognitive skill and improvisational perfor mance. They then offer recommendations for teacher educa tion practice.

Should Problem Solving Be Used as a Learning Device in Mathematics?

A theory of expertise has developed over the last few years on the basis of research into expert-novice differences. This research has shown an impressive degree of consistency in a variety of fields of knowledge and is beginning now to have direct implications for the teaching of mathematics and related disciplines. Research data, generated in part by current theories of expertise, call into question many long-held assumptions and procedures. We suggest that some views of mathematics and the way it should be taught owe more to tradition than to our current knowledge of cognitive processes. The purpose of this paper is to outline the theory, describe some of the associated experimental findings, and indicate the consequences of those findings for educational practice in mathematics. The work on expert-novice differences can be divided into three sets of interrelated findings: the memory of relevant problem details, modes of categorizing problems, and problem-solving strategies. There is now strong evidence that experts have a better memory than novices for realistic problem states or configurations. De Groot (1966) and Chase and Simon (1973a, 1973b), using realistic chess configurations; Egan and Schwartz (1979), using electronic circuit diagrams; Jeffries, Turner, Polson, and Atwood (1981), using computer programs; and Sweller and Cooper (1985), using algebra, all found a superior memory by experts for relevant items. With respect to modes of categorizing problems, Chi, Glaser, and Rees (1982) found, when studying the differences between experts and novices in solving physics problems, not only that the experts possess a greater body of knowledge, as one might expect, but more significantly that experts classified problems according to the underlying physics principle (e.g., the conservation of energy) that must be invoked to solve the problem. Novices, however, classified on the basis of surface features mentioned in the problem statement (e.g., inclined planes or pulleys) regardless of the principle underlying the solution method. Novices and experts also differ dramatically in their use of problem-solving strategies. A great many routine mathematics and mathematics-related problems

A Forum for Researchers: Should Problem Solving Be Used as a Learning Device in Mathematics?

A theory of expertise has developed over the last few years on the basis of research into expert-novice differences. This research has shown an impressive degree of consistency in a variety of fields of knowledge and is beginning now to have direct implications for the teaching of mathematics and related disciplines. Research data, generated in part by current theories of expertise, call into question many long-held assumptions and procedures. We suggest that some views of mathematics and the way it should be taught owe more to tradition than to our current knowledge of cognitive processes. The purpose of this paper is to outline the theory, describe some of the associated experimental findings, and indicate the consequences of those findings for educational practice in mathematics.

The quality of expertise: implications of expert-novice differences for knowledge acquisition

Recognizing that the knowledge of experts is qualitatively and not just quantitatively different from the knowledge of novices is an important pre-requisite to conducting effective knowledge acquisition. This article reviews the current cognitive research on expertise and proposes seven ways in which the knowledge of experts is different from the knowledge of novices, including such aspects as underlying schema, goal-orientation, practical focus, categorical chunking, cognitive complexity, automaticity of expert problem solving, and finally, the episodic nature of expert memory. The paper concludes with an outline of several implications that follow from this research including showing why knowledge acquisition will remain problematic without knowledge of experts' knowledge, as well as making a number of specific suggestions for those who need to elicit expert knowledge for knowledge-based systems.

Expert—Novice Differences in Person Perception: Evidence of Experts' Sensitivities to the Organization of Behavior

This article demonstrates that features identified in the classic expertise literature are shared by experts in the domain of social cognition. Clinical novices and experts were presented with multiple observations of children's behaviors and the situations in which they occurred. Three types of target personalities were created: a prototypical aggressive target whose behavior patterns corresponded to those displayed by actual aggressive children in natural social situations; an inverse target whose behaviors systematically violated those of the aggressive target; and a random target whose behaviors did not vary systematically across situations. Performances of experts and novices were examined in recall, prediction, and impression formation tasks. Experts and novices did not differ in recall as measured by a free recall task, but the experts' predictions of targets' behaviors were more closely tied to actual behavior than novices' predictions for both the aggressive and inverse targets. As predicted, the...

The contributions of cognitive engineering to the design and use of expert systems

Abstract The cognitive basis of several issues critical to building expert systems is reviewed. Required and desirable attributes of a knowledge elicitation methodology are identified. It is argued that knowledge should be elicited from several levels of expertise. Plausible contributions of research in human problem solving to knowledge representation and design of control structure are examined. Implications of the literature on human learning and on expert-novice differences for machine learning are reviewed. The applicability of systems, which rely on user modelling, to the design of intelligent interfaces is discussed.