Hausmann Study2 - Revision history

Bobhaus: /* Independent variables */

2008-10-10T14:38:21Z

Independent variables

← Older revision		Revision as of 14:38, 10 October 2008
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	[[Prompting]] for an explanation was intended to increase the probability that the individual or dyad will traverse a useful learning-event path.		[[Prompting]] for an explanation was intended to increase the probability that the individual or dyad will traverse a useful learning-event path.

			<b>Figure 1</b>. An example from the Individual Self-explanation condition<Br><Br>
			[[Image:Individual_SE.JPG]]<Br>

			<b>Figure 2</b>. An example from the Joint Self-explanation condition<Br><Br>
			[[Image:Joint_SE.JPG]]<Br>

	=== Hypothesis ===		=== Hypothesis ===

Bobhaus: /* Future plans */

2008-04-08T20:21:44Z

Future plans

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	Our future plans for June 2007 - August 2007:		Our future plans for June 2007 - August 2007:
	* Transcribe the interactions and code them for explanations, either constructed jointly or individually.		* Transcribe the interactions and code them for explanations, either constructed jointly or individually.
	* Submit poster to CogSci 2007 for the learning results

Bobhaus: /* Annotated bibliography */

2008-04-08T20:21:30Z

Annotated bibliography

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	=== Further Information ===		=== Further Information ===
	==== Annotated bibliography ====		==== Annotated bibliography ====
	* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, ~~May~~). ~~Trialog: How Peer Collaboration Helps Remediate Errors in an ITS. Paper~~ presented at the ~~21st~~ meeting of the ~~International FLAIRS Conference~~, ~~Coconut Grove~~, FL.		* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, July). Are self-explaining and coached problem solving more effective when done by pairs of students than alone? Poster presented at the 30th meeting of the Cognitive Science Society, Washington, DC.
	* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, June). Shall we explain? Augmenting Learning from Intelligent Tutoring Systems and Peer Collaboration. Paper presented at the 9th meeting of the International Conference on Intelligent Tutoring Systems, Montréal, Canada.		* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, June). Shall we explain? Augmenting Learning from Intelligent Tutoring Systems and Peer Collaboration. Paper presented at the 9th meeting of the International Conference on Intelligent Tutoring Systems, Montréal, Canada.
	* Hausmann, R. G. M., van de Sande, B., van de Sande, C., & VanLehn, K. (2008, June). Productive Dialog During Collaborative Problem Solving. Paper presented at the 2008 International Conference for the Learning Sciences, Utrecht, Netherlands.		* Hausmann, R. G. M., van de Sande, B., van de Sande, C., & VanLehn, K. (2008, June). Productive Dialog During Collaborative Problem Solving. Paper presented at the 2008 International Conference for the Learning Sciences, Utrecht, Netherlands.
			* Hausmann, R. G. M., van de Sande, B., & VanLehn, K. (2008, May). Trialog: How Peer Collaboration Helps Remediate Errors in an ITS. Paper presented at the 21st meeting of the International FLAIRS Conference, Coconut Grove, FL.
	* Presentation to the PSLC Advisory Board: January, 2008		* Presentation to the PSLC Advisory Board: January, 2008
	* Conference proceeding submitted to CogSci 2008		* Conference proceeding submitted to CogSci 2008

Bobhaus: /* Results */

2008-03-26T19:25:01Z

Results

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	In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.		In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.

	<br>		<tt>
	{\| border="1" cellspacing="0" cellpadding="5" ~~style~~="~~text-align:~~ center;"		{\| border="1" cellspacing="0" cellpadding="5" align="center"
	\|-		\|-
	\|MAG \|\| <center>[[Image:mag_learning_curves.JPG]]</center> \|\| EFIELD \|\| <center>[[Image:efield_learning_curves.JPG]]</center>		\|MAG \|\| <center>[[Image:mag_learning_curves.JPG]]</center> \|\| EFIELD \|\| <center>[[Image:efield_learning_curves.JPG]]</center>
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	\|-		\|-
	\|}		\|}
	'''Table 2'''. Learning curves for each knowledge component.		</tt>
			<center>'''Table 2'''. Learning curves for each knowledge component.</center>
	<br>		<br>

Bobhaus: /* Hypothesis */

2008-03-26T19:18:55Z

Hypothesis

← Older revision		Revision as of 19:18, 26 March 2008
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	=== Hypothesis ===		=== Hypothesis ===
	'''The Interactive Hypothesis''': collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Interactive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.		'''The Interactive Hypothesis''': collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Interactive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.

			This experiment implements the following instructional principle: [[Prompted Self-explanation]]

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

Bobhaus: /* Results */

2008-03-26T19:15:37Z

Results

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	'''Table 2'''. Learning curves for each knowledge component.		'''Table 2'''. Learning curves for each knowledge component.
	<br>		<br>

	For the first KC (the definition of the electric field), JCE exhibited lower normalized assistance scores on the first problem. They also demonstrated better understanding for drawing the electric-field vector after watching the first [[example]] video. Finally, there was a marginal difference on performance for drawing the force vector.

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

Bobhaus: /* Results */

2008-03-26T19:15:03Z

Results

← Older revision		Revision as of 19:15, 26 March 2008
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	In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.		In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.

	~~<center>~~<br>		<br>
	{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
	\|-		\|-

Bobhaus: /* Results */

2008-03-26T19:14:46Z

Results

← Older revision		Revision as of 19:14, 26 March 2008
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	In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.		In an effort to anticipate the replication, we used a data imputation method for estimating missing values for each student (SPSS v14.0: Transform/Replace missing values/Median of nearby points). This provided a complete dataset for all participants. The results are summarized in the table below.

	<br>		<center><br>
	{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
	\|-		\|-
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	\|-		\|-
	\|}		\|}
	'''Table 2'''. ~~Pair-wise comparisons~~ for each knowledge component.		'''Table 2'''. Learning curves for each knowledge component.
	~~Note. Red indicates a significant difference (''p'' < .05); Yellow indicates a marginal difference (''p'' < .10)~~.
	<br>		<br>

Bobhaus: /* Results */

2008-03-26T19:08:46Z

Results

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	{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
	\|-		\|-
	\| \|\| ~~Results~~		\|MAG \|\| <center>[[Image:mag_learning_curves.JPG]]</center> \|\| EFIELD \|\| <center>[[Image:efield_learning_curves.JPG]]</center>
	\|-		\|-
	~~\|MAG \|\| <center>[[Image:mag_learning_curves.JPG]]</center>~~		\|FORCE \|\| <center>[[Image:force_learning_curves.JPG]]</center> \|\|CHARGE \|\| <center>[[Image:charge_learning_curves.JPG]]</center>
	\|-
	~~\|EFIELD \|\| <center>[[Image:efield_learning_curves.JPG]]</center>~~
	\|-
	\|FORCE \|\| <center>[[Image:force_learning_curves.JPG]]</center>
	\|-
	\|CHARGE \|\| <center>[[Image:charge_learning_curves.JPG]]</center>
	\|-		\|-
	\|}		\|}

Bobhaus: /* Results */

2008-03-26T19:05:36Z

Results

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	{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: center;"
	\|-		\|-
	\| \|\| ~~Warm-up \|\| Problem 1 \|\| Problem 2 \|\| Problem 3~~		\| \|\| Results
	\|-		\|-
	\|MAG \|\| ~~JCE=ICE \|\| bgcolor="red"\|JCE~~<~~ICE \|\| JCE=ICE \|\| JCE=ICE~~		\|MAG \|\| <center>[[Image:mag_learning_curves.JPG]]</center>
	\|-		\|-
	\|EFIELD \|\| ~~JCE=ICE \|\| bgcolor="red"\|JCE~~<~~ICE \|\| JCE=ICE \|\| JCE=ICE~~		\|EFIELD \|\| <center>[[Image:efield_learning_curves.JPG]]</center>
	\|-		\|-
	\|FORCE \|\| ~~JCE=ICE \|\| JCE=ICE \|\| JCE=ICE \|\| bgcolor="yellow"\|JCE~~<~~ICE~~		\|FORCE \|\| <center>[[Image:force_learning_curves.JPG]]</center>
	\|-		\|-
	\|CHARGE \|\| ~~JCE=ICE \|\| JCE=ICE \|\| JCE=ICE \|\| JCE=ICE~~		\|CHARGE \|\| <center>[[Image:charge_learning_curves.JPG]]</center>
	\|-		\|-
	\|}		\|}