Features - Revision history

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

2011-09-02T20:29:51Z

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

New page

[[Category:Glossary]]
[[Category:PSLC General]]
Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article).

If the KC is part of the knowledge that we want students to learn, and it makes sense to distinguish contexts where the KC should and should not be applied, then we can also distinguish ''relevant'' features of the KC from ''irrelevant'' features. Relevant features tend to be present in contexts where the KC should be applied and absent in contexts where the KC should not be applied. Irrelevant features tend to be absent in contexts where the KC should be applied and/or present in contexts where the KC should not be applied. A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].

Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

A number of projects provide some good examples of KC feature analysis including [[Booth | Julie Booth's in Algebra]] and [[FrenchCulture|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to [[refinement|refine]] their knowledge, that is, learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).

=== 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.

Koedinger at 20:22, 3 January 2008

2008-01-03T20:22:30Z

← Older revision		Revision as of 20:22, 3 January 2008
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	Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.		Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

	A number of projects provide some good examples of KC feature analysis including [[Booth \| Julie Booth's in Algebra]] and [[FrenchCulture\|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).		A number of projects provide some good examples of KC feature analysis including [[Booth \| Julie Booth's in Algebra]] and [[FrenchCulture\|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to [[refinement\|refine]] their knowledge, that is, learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).

			=== 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.

Vanlehn at 13:43, 7 November 2007

2007-11-07T13:43:17Z

← Older revision		Revision as of 13:43, 7 November 2007
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
	Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article). Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.		Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article).

	A number of projects provide some good examples of KC feature analysis including [[Booth \| Julie Booth's in Algebra]] and [[FrenchCulture\|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign)~~. A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]]~~.		If the KC is part of the knowledge that we want students to learn, and it makes sense to distinguish contexts where the KC should and should not be applied, then we can also distinguish ''relevant'' features of the KC from ''irrelevant'' features. Relevant features tend to be present in contexts where the KC should be applied and absent in contexts where the KC should not be applied. Irrelevant features tend to be absent in contexts where the KC should be applied and/or present in contexts where the KC should not be applied. A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].

			Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

			A number of projects provide some good examples of KC feature analysis including [[Booth \| Julie Booth's in Algebra]] and [[FrenchCulture\|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).

Koedinger at 23:07, 29 March 2007

2007-03-29T23:07:13Z

← Older revision		Revision as of 23:07, 29 March 2007
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	Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article). Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.		Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article). Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

	A number of projects provide some good examples of KC feature analysis including Julie Booth's in Algebra and Amy Ogan's in French. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign). A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].		A number of projects provide some good examples of KC feature analysis including [[Booth \| Julie Booth's in Algebra]] and [[FrenchCulture\|Amy Ogan's in French]]. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign). A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].

Koedinger at 21:43, 29 March 2007

2007-03-29T21:43:21Z

← Older revision		Revision as of 21:43, 29 March 2007
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
	Features are the individual properties of a knowledge component (KC) that determine the retrieval conditions of that KC. ~~Sometimesfeatures~~ are relatively directly perceivable (seen or heard). In the language literature, such features are called cues ~~(ref?)~~. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.		Features are the individual properties of a [[knowledge component]] (KC) that determine the retrieval conditions of that KC, that is, when a student uses or thinks of a particular action or idea (e.g., divide both sides of an equation, pick 'a' vs. 'the' as an article). Sometimes features are relatively directly perceivable (seen or heard). In the language literature, such features are called cues. Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser (1981) distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

	A number of projects provide some good examples of KC feature analysis including Julie Booth's in Algebra and Amy Ogan's in French. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term ~~that~~ includes a number and ~~it's~~ positive or negative ~~sign~~) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).		A number of projects provide some good examples of KC feature analysis including Julie Booth's in Algebra and Amy Ogan's in French. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term includes a number and its sign, positive or negative) and distinguish them from irrelevant shallow features (e.g., a number without it's sign). A knowledge component that has just relevant features and no irrelevant features has high [[feature validity]].

Koedinger at 17:02, 28 December 2006

2006-12-28T17:02:50Z

← Older revision		Revision as of 17:02, 28 December 2006
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
	Features are the individual properties of a knowledge component that determine the retrieval conditions of that ~~knowledge component~~. Sometimesfeatures are relatively directly perceivable (seen or heard). In the language literature, such features are called cues (ref?). Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.		Features are the individual properties of a knowledge component (KC) that determine the retrieval conditions of that KC. Sometimesfeatures are relatively directly perceivable (seen or heard). In the language literature, such features are called cues (ref?). Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

			A number of projects provide some good examples of KC feature analysis including Julie Booth's in Algebra and Amy Ogan's in French. In both, much of the instructional design is focused on helping students to learn the relevant deep features (e.g., a term that includes a number and it's positive or negative sign) and distinguish them from irrelevant shallow features (e.g., a number without it's sign).

Koedinger at 16:57, 28 December 2006

2006-12-28T16:57:55Z

← Older revision		Revision as of 16:57, 28 December 2006
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
	Features are the individual ~~measurable heuristic~~ properties of the ~~phenomena being observed~~.		Features are the individual properties of a knowledge component that determine the retrieval conditions of that knowledge component. Sometimesfeatures are relatively directly perceivable (seen or heard). In the language literature, such features are called cues (ref?). Sometimes the relevant features of a knowledge component require more complex inference to be detected by the student. For example, Chi, Feltovich, and Glaser distinguish between shallow features of physics problems, like pulley system or inclined plane, that are irrelevant to correct problem solving (i.e., KC application) and deep features, like conservation of energy, that are relevant to accessing correct knowledge components.

Adele at 19:02, 27 December 2006

2006-12-27T19:02:33Z

← Older revision		Revision as of 19:02, 27 December 2006
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:PSLC General]]		[[Category:PSLC General]]
			Features are the individual measurable heuristic properties of the phenomena being observed.

PhilPavlik at 15:40, 2 October 2006

2006-10-02T15:40:09Z

← Older revision		Revision as of 15:40, 2 October 2006
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	[[Category:Glossary]]		[[Category:Glossary]]
	[[Category:~~Fluency and Refinement~~]]		[[Category:PSLC General]]

PhilPavlik at 15:25, 2 October 2006

2006-10-02T15:25:13Z

New page

[[Category:Glossary]]
[[Category:Fluency and Refinement]]