Difference between revisions of "Feature validity"
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Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (MacWhinney & Bates, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Glaser & Feltovitch, 1981). | Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (MacWhinney & Bates, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Glaser & Feltovitch, 1981). | ||
| − | The feature validity of a [[knowledge component]] measures how well the features associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled. | + | The feature validity of a [[knowledge component]] measures how well the features associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled. A student has acquired a knowledge component with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. |
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| + | See the Booth page in Coordinative Learning for one example. | ||
Revision as of 20:59, 29 March 2007
Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (MacWhinney & Bates, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Glaser & Feltovitch, 1981).
The feature validity of a knowledge component measures how well the features associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled. A student has acquired a knowledge component with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant.
See the Booth page in Coordinative Learning for one example.
