Feature validity - Revision history

Mbett: Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

2011-08-31T15:43:21Z

Reverted edits by Pierrehernandez (Talk); changed back to last version by Koedinger

← Older revision		Revision as of 15:43, 31 August 2011
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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.		* 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).		* 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).
	~~[http://custom-essay.ws/index.php essay papers]~~

Pierrehernandez at 13:12, 31 August 2011

2011-08-31T13:12:43Z

← Older revision		Revision as of 13:12, 31 August 2011
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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.		* 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).		* 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).
			[http://custom-essay.ws/index.php essay papers]

Koedinger at 12:28, 22 November 2010

2010-11-22T12:28:47Z

← Older revision		Revision as of 12:28, 22 November 2010
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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).

Macw at 18:10, 4 December 2009

2009-12-04T18:10:22Z

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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 ~~& Bates~~, 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).		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.

Macw: /* References */

2009-12-04T18:09:39Z

References

← Older revision		Revision as of 18:09, 4 December 2009
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	=== References ===		=== 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.		* 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.
	* MacWhinney & Bates (~~1989~~).		* 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.

Koedinger at 21:00, 3 January 2008

2008-01-03T21:00:56Z

← Older revision		Revision as of 21:00, 3 January 2008
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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 & 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, Feltovitch, & Glaser, 1981).

	See the Booth page ~~in Coordinative Learning~~ for ~~one example~~.		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.
			* MacWhinney & Bates (1989).

Koedinger at 22:59, 1 June 2007

2007-06-01T22:59:44Z

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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:Coordinative Learning]]		[[Category:Coordinative Learning]]

			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 (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. 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.		See the Booth page in Coordinative Learning for one example.

Koedinger at 20:59, 29 March 2007

2007-03-29T20:59:33Z

← Older revision		Revision as of 20:59, 29 March 2007
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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. ~~Strength is roughly proportionally to~~ the ~~number of times an encoding~~ of a knowledge component ~~was accessed~~ and ~~how recently it was accessed~~.		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.

Adele at 18:37, 28 December 2006

2006-12-28T18:37:55Z

New page

[[Category:Glossary]]
[[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.