Prompted Self-explanation - Revision history

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2011-07-06T15:19:53Z

remove spam

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	=== Examples ===		=== Examples ===

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	~~[http://www.royalworldcasino.com/ online casino reviews]~~

	Here are the instructions to self-explain, taken from Chi et al. (1994):		Here are the instructions to self-explain, taken from Chi et al. (1994):

Nick at 09:45, 15 September 2010

2010-09-15T09:45:05Z

New page

Self-explaining is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
* A meta-cognitive statement is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
* A self-explanation inference is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).

Prompting is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.

See also [[prompted self-explanation hypothesis]].

=== Examples ===

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[http://www.royalworldcasino.com/ online casino reviews]

Here are the instructions to self-explain, taken from Chi et al. (1994):

"We would like you to read each sentence out loud and then explain what it means to you. That is, what 
new information does each line provide for you, how does it relate to what you've already read, does it give 
you a new insight into your understanding of how the circulatory system works, or does it raise a question 
in your mind. Tell us whatever is going through your mind–even if it seems unimportant." 

These prompts were reworded to be used in Hausmann & VanLehn (2007):

* What new information does each step provide for you?
* How does it relate to what you've already seen?
* Does it give you a new insight into your understanding of how to solve the problems?
* Does it raise a question in your mind?

These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:

<Blockquote>A charged particle is in a region where there is an electric field E of magnitude 
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle 
is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote>

 

{| cellspacing="0" cellpadding="5" border="1"
|+ '''An example of prompting for self-explanining'''
|-
| style="border-bottom: 3px solid grey;" |
    Now that all the given information has been entered, we need to apply our knowledge of physics to solve the problem. 

    One way to start is to ask ourselves, “What quantity is the problem seeking?” In this case, the answer is the magnitude of the force on the particle due to the electric field. 

    We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative.

    We use the Force tool from the vector tool bar to draw the electric force. This brings up a dialog box. The force is on the particle and it is due to some unspecified source. We do know, however, that the type of force is electric, so we choose “electric” from the pull-down menu. For the orientation, we need to add 180 degrees to 22 degrees to get a force that is in a direction that is opposite of the direction of the electric field. Therefore we put 202 degrees. Finally, we use “Fe” to designate this as an electric force.

<center>[ PROMPT ]</center>

    Now that the direction of the electric force has been indicated, we can work on finding the magnitude. We must choose a principle that relates the magnitude of the electric force to the strength of the electric field, and the charge on the particle. The definition of an electric field is only equation that relates these three variables. We write this equation, in the equation window.

<center>[ PROMPT ]</center>

|}
Note. PROMPT = "Please begin your self-explanation."

== References ==
Chi, M. T. H. (2000). Self-explaining: The dual processes of generating and repairing mental models. In R. Glaser (Ed.), ''Advances in Instructional Psychology'' (pp. 161-238). Mahwah, NJ: Erlbaum. [http://www.public.asu.edu/~mtchi/papers/advances.pdf]

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]

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. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]

[[Category:Glossary]]
[[Category:Independent Variables]]

Goldin: /* References */

2009-03-26T01:54:42Z

References

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	== References ==		== References ==
	Chi, M. T. H. (2000). Self-explaining: The dual processes of generating and repairing mental models. In R. Glaser (Ed.), ''Advances in Instructional Psychology'' (pp. 161-238). Mahwah, NJ: Erlbaum.		Chi, M. T. H. (2000). Self-explaining: The dual processes of generating and repairing mental models. In R. Glaser (Ed.), ''Advances in Instructional Psychology'' (pp. 161-238). Mahwah, NJ: Erlbaum. [http://www.public.asu.edu/~mtchi/papers/advances.pdf]

	Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]		Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]

Koedinger at 16:15, 25 January 2008

2008-01-25T16:15:13Z

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	<b>Prompting</b> is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.		<b>Prompting</b> is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.

			See also [[prompted self-explanation hypothesis]].

	=== Examples ===		=== Examples ===

Vanlehn at 03:25, 12 December 2007

2007-12-12T03:25:28Z

@@ Line 1: / Line 1: @@
-== Brief statement of principle ==
+<b>Self-explaining</b> is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
 * A <b>meta-cognitive statement</b> is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
 * A <b>self-explanation inference</b> is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).
-*<b>Prompting</b> is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.
 === Examples ===
@@ Line 57: / Line 49: @@
 Note. PROMPT = "Please begin your self-explanation."
 == References ==
+Chi, M. T. H. (2000). Self-explaining: The dual processes of generating and repairing mental models. In R. Glaser (Ed.), ''Advances in Instructional Psychology'' (pp. 161-238). Mahwah, NJ: Erlbaum.
 Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., &amp; LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]
 Hausmann, R. G. M., &amp; VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger &amp; J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]
 [[Category:Glossary]]
-[[Category:Instructional Principle]]
+[[Category:Independent Variables]]

Vanlehn: /* Brief statement of principle */

2007-12-11T18:58:19Z

Brief statement of principle

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	== Brief statement of principle ==		== Brief statement of principle ==

	Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation\|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 (Chi et al., 1994). [[Prompting]] can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Koedinger & Aleven, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).~~<br>~~		Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation\|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 (Chi et al., 1994). [[Prompting]] can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Koedinger & Aleven, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).

			In the context of studying an example or reading a text, prompting for [[Self-explanation\|self-explanations]] leads to greater learning gains than naturally occuring student practices.

	== Description of principle ==		== Description of principle ==

Vanlehn at 18:10, 11 December 2007

2007-12-11T18:10:22Z

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	VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.		VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.


			[[Category:Glossary]]
			[[Category:Instructional Principle]]

Bobhaus: /* Laboratory experiment support */

2007-12-11T15:32:11Z

Laboratory experiment support

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	=== Laboratory experiment support ===		=== Laboratory experiment support ===
	Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).		Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).


	=== In vivo experiment support ===		=== In vivo experiment support ===

Bobhaus: /* Caveats, limitations, open issues, or dissenting views */

2007-12-11T15:31:51Z

Caveats, limitations, open issues, or dissenting views

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	== Caveats, limitations, open issues, or dissenting views ==		== Caveats, limitations, open issues, or dissenting views ==
	Examples typically precede problem solving. For example, in Sweller and Cooper (1985), they asked students to study X examples in preparation to solve X problems. Similarly, Chi et al. (1989) asked students to read through X chapters of a physics text, which contained several examples. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied X examples, then solved X problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until X problems and X examples were completed.		Examples typically precede problem solving. For example, in Sweller and Cooper (1985; Experiment 2), they asked students to study 2 examples in preparation to solve 8 problems. Similarly, Chi et al. (1989) asked students to read through 4 chapters of a physics text, which contained several examples. After studying each chapter, the students were asked to solve problems related to the content that they just studied. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied 6 examples, then solved 6 problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until all problems and examples were completed.

	The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.		The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.

Bobhaus: /* References */

2007-12-11T15:23:53Z

References

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	Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [http://dx.doi.org/10.1016/S0364-0213(99)80017-2]		Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [http://dx.doi.org/10.1016/S0364-0213(99)80017-2]

			Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89. [http://scholar.google.com/scholar?hl=en&lr=&client=firefox-a&cluster=16552570726007249431]

	Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. [http://citeseer.ist.psu.edu/rd/40331946%2C149956%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/2910/http:zSzzSzwww.aic.nrl.navy.milzSz%7EtraftonzSzpaperszSzcogsci93-exp1.pdf/the-contributions-of-studying.pdf]		Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. [http://citeseer.ist.psu.edu/rd/40331946%2C149956%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/2910/http:zSzzSzwww.aic.nrl.navy.milzSz%7EtraftonzSzpaperszSzcogsci93-exp1.pdf/the-contributions-of-studying.pdf]

	VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.		VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.