Knowledge Organization and the Acquisition of Procedural Expertise

The influence of the organization of a declarative knowledge base on the development and application of proceduralized knowledge was investigated in a complex troubleshooting domain. Domain explanations were generated in either a depth-first or breadth-first manner for different groups of subjects who were also given experience learning to troubleshoot in the domain. Although the two explanatory structures led to similar training performance, the two groups differed significantly in their overall level of performance in subsequent troubleshooting problems. Examination of objective measures of troubleshooting performance and think-aloud protocols indicated that breadth-first declarative knowledge representation fosters the use of mental models during problem-solving in training. It also facilitates proceduralization of that knowledge into fast and accurate methods for localizing faults. Language: en

[1]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[2]  J. Greeno,et al.  Structural differences between outcomes produced by different instructional methods. , 1972 .

[3]  Edward E. Smith,et al.  Understanding Written Instructions: The Role of an Explanatory Schema , 1984 .

[4]  Joan I. Heller,et al.  Knowledge structure and problem solving in physics , 1982 .

[5]  John R. Anderson Acquisition of cognitive skill. , 1982 .

[6]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[7]  F. Reif,et al.  Effects of Knowledge Organization on Task Performance , 1984 .

[8]  P. Johnson-Laird Mental models , 1989 .

[9]  Robert Glaser,et al.  Thoughts on Expertise , 1985 .

[10]  R. Glaser Education and Thinking: The Role of Knowledge. , 1984 .

[11]  J. Rasmussen,et al.  Mental procedures in real-life tasks: a case study of electronic trouble shooting. , 1974, Ergonomics.

[12]  H. Simon,et al.  Perception in chess , 1973 .

[13]  M. Chi,et al.  Problem-Solving Ability. , 1985 .

[14]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[15]  Daniel S. Weld,et al.  Explaining Complex Engineered Devices. , 1983 .

[16]  John R. Anderson,et al.  Learning to Program in LISP , 1984, Cogn. Sci..

[17]  John R. Anderson,et al.  Learning to Program in LISP , 1984, Cogn. Sci..

[18]  Robin Jeffries,et al.  The Processes Involved in Designing Software. , 1980 .

[19]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[20]  G. Bower,et al.  Hierarchical retrieval schemes in recall of categorized word lists , 1969 .

[21]  Benjamin Kuipers,et al.  Causal Reasoning in Medicine: Analysis of a Protocol , 1984 .

[22]  Michael J. Prietula,et al.  Expertise and error in diagnostic reasoning , 1981 .

[23]  B. K. Britton,et al.  Effects of Text Structure on Use of Cognitive Capacity during Reading. , 1982 .

[24]  B. Adelson Problem solving and the development of abstract categories in programming languages , 1981, Memory & cognition.