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KARMA: knowledge acquisition, retention and maintenance analysis

KARMA: knowledge acquisition, retention and maintenance analysis KARMA is a quantitative model which can be used to predict a user's acquisition, retention, maintenance, and transfer of system knowledge. In KARMA, three stages over the course of an operator's model (see Schumacher & Czerwinski, in press) are characterized: pretheoretic - the understanding of system structure centers on the retrieval of superficially similar instances in memory; experiential - some understanding of causal system relationships emerges, as does some reliance upon structural system information. Also, abstraction begins to occur across different, but similar systems; and expert - at this stage, the user makes abstractions across various system representations stored in memory; full reliance upon structural system knowledge emerges. Currently, a programmed simulation is used to compare our theoretical predictions and assumptions with experimental data. The simulation is running in Scheme (a dialect of LISP). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGCHI Bulletin Association for Computing Machinery

KARMA: knowledge acquisition, retention and maintenance analysis

ACM SIGCHI Bulletin , Volume 23 (1) – Jan 1, 1991

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References (3)

Publisher
Association for Computing Machinery
Copyright
Copyright © 1991 by ACM Inc.
ISSN
0736-6906
DOI
10.1145/122672.122691
Publisher site
See Article on Publisher Site

Abstract

KARMA is a quantitative model which can be used to predict a user's acquisition, retention, maintenance, and transfer of system knowledge. In KARMA, three stages over the course of an operator's model (see Schumacher & Czerwinski, in press) are characterized: pretheoretic - the understanding of system structure centers on the retrieval of superficially similar instances in memory; experiential - some understanding of causal system relationships emerges, as does some reliance upon structural system information. Also, abstraction begins to occur across different, but similar systems; and expert - at this stage, the user makes abstractions across various system representations stored in memory; full reliance upon structural system knowledge emerges. Currently, a programmed simulation is used to compare our theoretical predictions and assumptions with experimental data. The simulation is running in Scheme (a dialect of LISP).

Journal

ACM SIGCHI BulletinAssociation for Computing Machinery

Published: Jan 1, 1991

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