Prompted self-explanation hypothesis - Revision history

Bobhaus: Redirecting to Prompted self-explanation principle

2009-01-23T14:50:30Z

Redirecting to Prompted self-explanation principle

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			#REDIRECT [[Prompted self-explanation principle]]

	== Brief statement of principle ==		== Brief statement of principle ==
	When students are given a [[worked examples\|worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.		When students are given a [[worked examples\|worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.

Aleven at 19:07, 10 January 2008

2008-01-10T19:07:49Z

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	== Description of principle ==		== Description 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]] (Chi et al., 1989). 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 (Lovett, 1992; 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).		Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation\|self-explanations]] (Chi et al., 1989). 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 (Lovett, 1992; 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; Aleven & Koedinger, 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.		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.

Bobhaus: /* Operational definition */

2008-01-04T15:22:45Z

Operational definition

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	=== Operational definition ===		=== Operational definition ===
	* <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.		* <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>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).		** 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.		*<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.

Bobhaus: /* In vivo experiment support */

2008-01-04T15:21:32Z

In vivo experiment support

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	*[[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)]]		*[[Craig_questions\|Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)]]
			*[[Bridging_Principles_and_Examples_through_Analogy_and_Explanation\|Bridging Principles and Examples through Analogy and Explanation (Nokes & VanLehn, 2007)]]

	== Theoretical rationale ==		== Theoretical rationale ==

Bobhaus: /* References */

2008-01-04T15:19:05Z

References

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	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]		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]

			Lovett, M. C. (1992). Learning by problem solving versus by examples: The benefits of generating and receiving information. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (pp. 956-961). Hillsdale, NJ: Erlbaum.

	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]		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]

Bobhaus: /* Description of principle */

2008-01-04T15:18:54Z

Description of principle

← Older revision		Revision as of 15:18, 4 January 2008
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	== Description of principle ==		== Description 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).		Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation\|self-explanations]] (Chi et al., 1989). 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 (Lovett, 1992; 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.		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.

Bobhaus: /* Brief statement of principle */

2008-01-04T15:13:45Z

Brief statement of principle

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	== Brief statement of principle ==		== Brief statement of principle ==
	When students are given a [[worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.		When students are given a [[worked examples\|worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.

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

Bobhaus: /* Theoretical rationale */

2008-01-04T15:11:51Z

Theoretical rationale

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	\|Start		\|Start
	# Process the line shallowly, e.g., paraphrasing it<br>		# Process the line shallowly, e.g., paraphrasing it<br>
	## There is nothing more to learn  Exit, with learning<br>		## There is nothing more to learn => Exit, with learning<br>
	## The line is incomplete; its explanation is missing  Exit, with little learning<br>		## The line is incomplete; its explanation is missing => Exit, with little learning<br>
	# Try to process the line deeply, e.g., self-explain it<br>		# Try to process the line deeply, e.g., self-explain it<br>
	## There is nothing missing from the line  Exit, with learning<br>		## There is nothing missing from the line => Exit, with learning<br>
	## The line is incomplete; its explanation is missing<br>		## The line is incomplete; its explanation is missing<br>
	### The attempted self-explanation succeeds  Exit, with learning<br>		### The attempted self-explanation succeeds => Exit, with learning<br>
	### The attempted self-explanation fails  Exit, with perhaps less learning<br>		### The attempted self-explanation fails => Exit, with perhaps less learning<br>
	\|-		\|-
	\|}		\|}

Koedinger: /* Variations (descendants) */

2008-01-04T01:37:57Z

Variations (descendants)

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	== Variations (descendants) ==		== Variations (descendants) ==
			[[Corrective self-explanation]]

	== Generalizations (ascendants) ==		== Generalizations (ascendants) ==

Koedinger: /* Generalizations (ascendants) */

2008-01-04T01:37:11Z

Generalizations (ascendants)

← Older revision		Revision as of 01:37, 4 January 2008
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	== Generalizations (ascendants) ==		== Generalizations (ascendants) ==
			[[Example-rule coordination principle]]

	== References ==		== References ==