Difference between revisions of "Feature validity"
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A student has acquired a knowledge component (KC) with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. Through the learning process of [[refinement]] a learner may modify an existing KC to produce a new one with higher feature validity. | A student has acquired a knowledge component (KC) with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. Through the learning process of [[refinement]] a learner may modify an existing KC to produce a new one with higher feature validity. | ||
| − | 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 | + | Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (McDonald & MacWhinney, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Feltovitch, & Glaser, 1981). |
See the [[Booth]] page for examples of knowledge components with different levels of feature validity. | See the [[Booth]] page for examples of knowledge components with different levels of feature validity. | ||
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* Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. | * Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. | ||
* McDonald, J. L., & MacWhinney, B. (1989). Maximum likelihood models for sentence processing research. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of sentence processing (pp. 397-421). New York: Cambridge University Press. | * McDonald, J. L., & MacWhinney, B. (1989). Maximum likelihood models for sentence processing research. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of sentence processing (pp. 397-421). New York: Cambridge University Press. | ||
| + | * Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). | ||
Latest revision as of 15:43, 31 August 2011
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 (KC) with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. Through the learning process of refinement a learner may modify an existing KC to produce a new one with higher feature validity.
Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (McDonald & MacWhinney, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Feltovitch, & Glaser, 1981).
See the Booth page for examples of knowledge components with different levels of feature validity.
References
- Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.
- McDonald, J. L., & MacWhinney, B. (1989). Maximum likelihood models for sentence processing research. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of sentence processing (pp. 397-421). New York: Cambridge University Press.
- Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351).
