Difference between revisions of "Prompted Self-explanation"
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*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]] | *[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]] | ||
*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]] | *[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]] | ||
+ | *[[Craig_questions|Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)]] | ||
== Theoretical rationale == | == Theoretical rationale == |
Revision as of 18:45, 10 December 2007
Contents
Brief statement of principle
Many empirical studies have shown that there is a large amount of variance when it comes to individually produced self-explanations. Some students have a natural tenancy to self-explain, while other students do little more than repeat the content of the example or expository text. The quality of the self-explanations themselves can be highly variable (Renkl, 1997). One instructional intervention that has been shown to be effective is to prompt students to self-explain. Prompting can take many forms, including verbal prompts from human experimenters, prompts automatically generated by computer tutors, or embedded in the learning materials themselves.
Description of principle
Operational definition
Examples
Now that all the given information has been entered, we need to apply One way to start is to ask ourselves, “What quantity is the problem seeking?” We know that there is an electric field. If there is an electric field, We use the Force tool from the vector tool bar to draw the electric force. [ PROMPT ] Now that the direction of the electric force has been indicated, we can work on the electric force to the strength of the electric field, and the charge on the [ PROMPT ] |
Experimental support
Laboratory experiment support
In vivo experiment support
- Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)
- The effects of interaction on robust learning (Hausmann & VanLehn, 2007)
- Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)
Theoretical rationale
(These entries should link to one or more learning processes.)
Conditions of application
Caveats, limitations, open issues, or dissenting views
Variations (descendants)
Generalizations (ascendants)
References
Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477.
Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14.
Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press.
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29.