Hausmann Diss - Revision history

Bobhaus: /* Independent variables */

2008-10-10T17:02:34Z

Independent variables

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 === Independent variables ===
 * Collaboration training: elaboration vs. critical vs. control vs. individual
 === Hypothesis ===

Bvds: /* Problem Solving */

2008-04-22T02:19:21Z

Problem Solving

← Older revision		Revision as of 02:19, 22 April 2008
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	* ''Optimization score'': the summation of the stress-to-strength ratios for each member, which was then divided by the total number of members:		* ''Optimization score'': the summation of the stress-to-strength ratios for each member, which was then divided by the total number of members:

	<math>\frac{\sum_{n=1}^i}{\frac{stress_{i}}{strength_{i}}}}{n}</math>		<math>\frac{\sum_{n=1}^i}{\frac{stress_{i}}{strength_{i}}}{n}</math>

	where n = the number of members per bridge, stress is the amount of force loaded on the i-th member of the bridge, and strength is the maximum loading the i-th member can withstand before failure. The optimization score represents the average load per member; thus, higher values indicate better optimized designs.		where n = the number of members per bridge, stress is the amount of force loaded on the i-th member of the bridge, and strength is the maximum loading the i-th member can withstand before failure. The optimization score represents the average load per member; thus, higher values indicate better optimized designs.

Bvds: /* Problem Solving */

2008-04-22T02:18:24Z

Problem Solving

← Older revision		Revision as of 02:18, 22 April 2008
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	* ''Optimization score'': the summation of the stress-to-strength ratios for each member, which was then divided by the total number of members:		* ''Optimization score'': the summation of the stress-to-strength ratios for each member, which was then divided by the total number of members:

	<math>\frac{\sum_{n=1}^\i}{\frac{stress_{i}}{strength_{i}}}}{n}</math>		<math>\frac{\sum_{n=1}^i}{\frac{stress_{i}}{strength_{i}}}}{n}</math>

	where n = the number of members per bridge, stress is the amount of force loaded on the i-th member of the bridge, and strength is the maximum loading the i-th member can withstand before failure. The optimization score represents the average load per member; thus, higher values indicate better optimized designs.		where n = the number of members per bridge, stress is the amount of force loaded on the i-th member of the bridge, and strength is the maximum loading the i-th member can withstand before failure. The optimization score represents the average load per member; thus, higher values indicate better optimized designs.

Bobhaus: /* Annotated bibliography */

2008-03-26T19:27:30Z

Annotated bibliography

← Older revision		Revision as of 19:27, 26 March 2008
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	=== Further Information ===		=== Further Information ===
	==== Annotated bibliography ====		==== Annotated bibliography ====
			* Submitted to the ''Journal of Educational Psychology'' on March 19, 2008.
	* Presented at the Festschrift for Lauren Resnick, May, 2005 [http://www.pitt.edu/AFShome/c/h/chi/public/html/TalkAndDialogue]		* Presented at the Festschrift for Lauren Resnick, May, 2005 [http://www.pitt.edu/AFShome/c/h/chi/public/html/TalkAndDialogue]
	* Presentation to the NSF Site Visitors, May, 2005		* Presentation to the NSF Site Visitors, May, 2005

Bobhaus: /* Future plans */

2008-03-26T19:26:03Z

Future plans

← Older revision		Revision as of 19:26, 26 March 2008
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	==== Future plans ====		==== Future plans ====
	* ~~Submit manuscript to the special issue~~ of ~~''Contemporary Educational Psychology''~~		* Replicate with stronger manipulation of critical interactions.

Bobhaus: /* Hypothesis */

2008-03-26T19:20:50Z

Hypothesis

← Older revision		Revision as of 19:20, 26 March 2008
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	Critical dialogs are hypothesized to enhance problem solving by avoiding the space of designs that are ineffective, thereby saving time testing fewer bridges. The process of evaluation may also increase learning because the knowledge components used in this task may be more highly specified for particular applications. For instance, the participants are told that shorter bars are stronger under compression. Until they have seen this knowledge component in an actual design, they might not appreciate its importance. Thus, the knowledge component is reified under a concrete application.		Critical dialogs are hypothesized to enhance problem solving by avoiding the space of designs that are ineffective, thereby saving time testing fewer bridges. The process of evaluation may also increase learning because the knowledge components used in this task may be more highly specified for particular applications. For instance, the participants are told that shorter bars are stronger under compression. Until they have seen this knowledge component in an actual design, they might not appreciate its importance. Thus, the knowledge component is reified under a concrete application.

			This experiment implements the following instructional principle: [[Collaboration scripts]]

	=== Dependent variables ===		=== Dependent variables ===

Koedinger: /* Findings */

2007-04-16T17:21:00Z

Findings

← Older revision		Revision as of 17:21, 16 April 2007
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	'''Learning'''		'''Learning'''

	* For ''shallow'' measures of learning, there were no differences between conditions ''F'' (2, 133) < 1. See the text-explicit column in the table below.		* For ''shallow'' measures of learning, that is, the [[normal post-test]] measures, there were no differences between conditions ''F'' (2, 133) < 1. See the text-explicit column in the table below.
	* For ''deep'' measures of learning (see the inferential column), there was a main effect of condition, reflecting a higher score for the elaborative dyads (''M'' = 11.18, ''SD'' = 18.81) than the control dyads (''M'' = 3.31, ''SD'' = 15.33), ''F'' (2, 133) = 3.08, ''p'' < .05.		* For ''deep'' measures of [[transfer]] of learning (see the inferential column), there was a main effect of condition, reflecting a higher score for the elaborative dyads (''M'' = 11.18, ''SD'' = 18.81) than the control dyads (''M'' = 3.31, ''SD'' = 15.33), ''F'' (2, 133) = 3.08, ''p'' < .05.

	<tt>		<tt>

Koedinger: /* Findings */

2007-04-16T17:17:01Z

Findings

← Older revision		Revision as of 17:17, 16 April 2007
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	'''Learning'''		'''Learning'''

	* For ''shallow'' measures of learning, there were no differences between conditions ''F'' (2, 133) < 1.		* For ''shallow'' measures of learning, there were no differences between conditions ''F'' (2, 133) < 1. See the text-explicit column in the table below.
	* For ''deep'' measures of learning, there was a main effect of condition, reflecting a higher score for the elaborative dyads (''M'' = 11.18, ''SD'' = 18.81) than the control dyads (''M'' = 3.31, ''SD'' = 15.33), ''F'' (2, 133) = 3.08, ''p'' < .05.		* For ''deep'' measures of learning (see the inferential column), there was a main effect of condition, reflecting a higher score for the elaborative dyads (''M'' = 11.18, ''SD'' = 18.81) than the control dyads (''M'' = 3.31, ''SD'' = 15.33), ''F'' (2, 133) = 3.08, ''p'' < .05.

	<tt>		<tt>
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	* The elaborative dyads asked fewer ''clarification questions'' than the control condition.		* The elaborative dyads asked fewer ''clarification questions'' than the control condition.
	* The elaborative condition explicitly mentioned and used the ''simulation feedback'' more than the control condition.		* The elaborative condition explicitly mentioned and used the ''simulation feedback'' more than the control condition.
	* The elaborative dyads were marginally more likely to be classified as using a ''positive problem-solving strategy'' than the control dyads.		* The elaborative dyads were marginally more likely to be classified as using a ''positive problem-solving strategy'' than the control dyads.

	=== Explanation ===		=== Explanation ===

Koedinger: /* Learning */

2007-04-16T17:10:59Z

Learning

← Older revision		Revision as of 17:10, 16 April 2007
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	==== Learning ====		==== Learning ====

	* ''Near transfer, immediate'': Text-explicit learning was defined as the acquisition of information explicitly stated in the text. Text-explicit learning was measured by administering identical pre- and post-tests. Standardized gain scores were used to measure learning.		* ''[[Normal post-test]], Near transfer, immediate'': Text-explicit learning was defined as the acquisition of information explicitly stated in the text. Text-explicit learning was measured by administering identical pre- and post-tests. Standardized gain scores were used to measure learning.

	* ''Far transfer, immediate'': Deep, inferential learning was defined as concepts that were not explicitly stated, and thus needed to be inferred from reading the text or interacting with the simulation. Inferential learning was also measured by administering identical pre- and post-tests, using standardized gain scores.		* ''Far [[transfer]], immediate'': Deep, inferential learning was defined as concepts that were not explicitly stated, and thus needed to be inferred from reading the text or interacting with the simulation. Inferential learning was also measured by administering identical pre- and post-tests, using standardized gain scores.

	* ''Retention'': For this laboratory study, there were no [[retention]] measures.

	* ''Acceleration of future learning'': For this laboratory study, there were no measures of [[accelerated future learning]].

	==== Problem Solving ====		==== Problem Solving ====

Bobhaus: /* Learning */

2007-03-30T15:54:14Z

Learning

← Older revision		Revision as of 15:54, 30 March 2007
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	* ''Retention'': For this laboratory study, there were no [[retention]] measures.		* ''Retention'': For this laboratory study, there were no [[retention]] measures.

	* ''Acceleration of future learning'': For this laboratory study, there were no measures of accelerated future learning.		* ''Acceleration of future learning'': For this laboratory study, there were no measures of [[accelerated future learning]].

	==== Problem Solving ====		==== Problem Solving ====