Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Training for reflective expertise: A four-component instructional design model for complex cognitive skills

Training for reflective expertise: A four-component instructional design model for complex... Abstract This article presents a four-component instructional design model for the training of complex cognitive skills. In the analysis phase, the skill is decomposed into a set of recurrent skills that remain consistent over problem situations and a set of nonrecurrent skills that require variable performance over situations. In the design phase, two components relate to the design of practice; they pertain to the conditions under which practice leads either to rule automation during the performance of recurrent skills or to schema acquisition during the performance of nonrecurrent skills. The other two components relate to the design of information presentation; they pertain to the presentation of information that supports the performance of either recurrent or nonrecurrent skills. The basic prediction of the model is that its application leads to “reflective expertise” and increased performance on transfer tasks. Applications of the model that support this prediction are briefly discussed for the training of fault management in process industry, computer programming, and statistical analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Educational Technology Research and Development Springer Journals

Training for reflective expertise: A four-component instructional design model for complex cognitive skills

Loading next page...
 
/lp/springer-journals/training-for-reflective-expertise-a-four-component-instructional-VkJtWc03ok

References (100)

Publisher
Springer Journals
Copyright
1992 Association for Educational Communications and Technology
ISSN
1042-1629
eISSN
1556-6501
DOI
10.1007/BF02297047
Publisher site
See Article on Publisher Site

Abstract

Abstract This article presents a four-component instructional design model for the training of complex cognitive skills. In the analysis phase, the skill is decomposed into a set of recurrent skills that remain consistent over problem situations and a set of nonrecurrent skills that require variable performance over situations. In the design phase, two components relate to the design of practice; they pertain to the conditions under which practice leads either to rule automation during the performance of recurrent skills or to schema acquisition during the performance of nonrecurrent skills. The other two components relate to the design of information presentation; they pertain to the presentation of information that supports the performance of either recurrent or nonrecurrent skills. The basic prediction of the model is that its application leads to “reflective expertise” and increased performance on transfer tasks. Applications of the model that support this prediction are briefly discussed for the training of fault management in process industry, computer programming, and statistical analysis.

Journal

Educational Technology Research and DevelopmentSpringer Journals

Published: Jun 1, 1992

There are no references for this article.