Difference between revisions of "Features"
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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]]. |
Revision as of 23:07, 29 March 2007
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.
