Difference between revisions of "Feature validity"

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[[Category:Glossary]]
 
[[Category:Glossary]]
 
[[Category:Coordinative Learning]]
 
[[Category:Coordinative Learning]]
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. Strength is roughly proportionally to the number of times an encoding of a knowledge component was accessed and how recently it was accessed.
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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.
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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.
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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 (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).
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See the [[Booth]] page for examples of knowledge components with different levels of feature validity.
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=== References ===
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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.
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* 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.
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* 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).