Difference between revisions of "Prompted Self-explanation"

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== Brief statement of principle ==
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<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.
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* 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.
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* 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).
  
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>
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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.
  
== Description of principle ==
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See also [[prompted self-explanation hypothesis]].
  
=== Operational definition ===
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=== Examples ===
  
=== Examples ===
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Here are the instructions to self-explain, taken from Chi et al. (1994):
Here are the prompts from Chi et al. (1994):
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"We would like you to read each sentence out loud and then explain what it means to you. That is, what<br>
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new information does each line provide for you, how does it relate to what you've already read, does it give<br>
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you a new insight into your understanding of how the circulatory system works, or does it raise a question<br>
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in your mind. Tell us whatever is going through your mind–even if it seems unimportant."<br>
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These prompts were reworded to be used in Hausmann & VanLehn (2007):
  
 
* What new information does each step provide for you?
 
* What new information does each step provide for you?
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* Does it give you a new insight into your understanding of how to solve the problems?
 
* Does it give you a new insight into your understanding of how to solve the problems?
 
* Does it raise a question in your mind?
 
* Does it raise a question in your mind?
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 +
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:
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<Blockquote>A charged particle is in a region where there is an electric field E of magnitude<br>
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14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle<br>
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is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote>
  
 
<br>
 
<br>
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|-
 
|-
 
| style="border-bottom: 3px solid grey;" |  
 
| style="border-bottom: 3px solid grey;" |  
Now that all the given information has been entered, we need to apply<br> our knowledge of physics to solve the problem.<br>
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&nbsp; &nbsp; Now that all the given information has been entered, we need to apply<br> our knowledge of physics to solve the problem.<br>
  
One way to start is to ask ourselves, “What quantity is the problem seeking?” <br> In this case, the answer is the magnitude of the force on the particle due to the electric field.<br>
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&nbsp; &nbsp; One way to start is to ask ourselves, “What quantity is the problem seeking?” <br> In this case, the answer is the magnitude of the force on the particle due to <br> the electric field.<br>
  
We know that there is an electric field. If there is an electric field, <br> and there is a charged particle located in that region, then we can infer <br> that there is an electric force on the particle. The direction of the <br> electric force is in the opposite direction as the electric field because <br> the charge on the particle is negative.
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&nbsp; &nbsp; We know that there is an electric field. If there is an electric field, <br> and there is a charged particle located in that region, then we can infer <br> that there is an electric force on the particle. The direction of the <br> electric force is in the opposite direction as the electric field because <br> the charge on the particle is negative.
  
We use the Force tool from the vector tool bar to draw the electric force. <br> This brings up a dialog box. The force is on the particle and it is due to some <br> unspecified source. We do know, however, that the type of force is electric, so <br> we choose “electric” from the pull-down menu. For the orientation, we need to <br> add 180 degrees to 22 degrees to get a force that is in a direction that is <br> opposite of the direction of the electric field. Therefore we put 202 degrees. <br> Finally, we use “Fe” to designate this as an electric force.
+
&nbsp; &nbsp; We use the Force tool from the vector tool bar to draw the electric force. <br> This brings up a dialog box. The force is on the particle and it is due to some <br> unspecified source. We do know, however, that the type of force is electric, so <br> we choose “electric” from the pull-down menu. For the orientation, we need to <br> add 180 degrees to 22 degrees to get a force that is in a direction that is <br> opposite of the direction of the electric field. Therefore we put 202 degrees. <br> Finally, we use “Fe” to designate this as an electric force.
  
[ PROMPT ]
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<center>[ PROMPT ]</center>
  
Now that the direction of the electric force has been indicated, we can work on <br>finding the magnitude. We must choose a principle that relates the magnitude of
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&nbsp; &nbsp; Now that the direction of the electric force has been indicated, we can work on <br>finding the magnitude. We must choose a principle that relates the magnitude <br> of the electric force to the strength of the electric field, and the charge on the <br> particle. The definition of an electric field is only equation that relates these <br> three variables. We write this equation, in the equation window.
  
the electric force to the strength of the electric field, and the charge on the <br> particle. The definition of an electric field is only equation that relates these <br> three variables. We write this equation, in the equation window.
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<center>[ PROMPT ]</center>
 
 
[ PROMPT ]
 
  
 
|}
 
|}
 
Note. PROMPT = "Please begin your self-explanation."
 
Note. PROMPT = "Please begin your self-explanation."
  
== Experimental support ==
 
 
=== Laboratory experiment support ===
 
 
=== In vivo experiment support ===
 
 
*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]
 
*[[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 ==
 
 
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)
 
 
== Conditions of application ==
 
 
== Caveats, limitations, open issues, or dissenting views ==
 
 
== Variations (descendants) ==
 
 
== Generalizations (ascendants) ==
 
  
 
== References ==
 
== References ==
 
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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]
Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explain with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179. [http://dx.doi.org/10.1016/S0364-0213%2802%2900061-7]
 
  
 
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]
 
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; Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [http://www.pitt.edu/~bobhaus/hausmann2002.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]
 
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]
  
McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222-233. [http://www.ingentaconnect.com/content/psocpubs/brm/2004/00000036/00000002/art00007]
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[[Category:Glossary]]
 
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[[Category:Independent Variables]]
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]
 

Latest revision as of 15:19, 6 July 2011

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

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:

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.


An example of prompting for self-explanining

    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.

[ PROMPT ]

    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.

[ PROMPT ]

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. [1]

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [2]

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. [3]