- 1 Brief statement of principle
- 2 Description of principle
- 3 Experimental support
- 4 Theoretical rationale
- 5 Conditions of application
- 6 Caveats, limitations, open issues, or dissenting views
- 7 Variations (descendants)
- 8 Generalizations (ascendants)
- 9 References
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
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 ]
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)
(These entries should link to one or more learning processes.)
Conditions of application
Caveats, limitations, open issues, or dissenting views
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.