Craig questions - Revision history

Mbett: Reverted edits by Unyvice (Talk); changed back to last version by Scraig@pitt.edu

2010-11-15T17:19:05Z

Reverted edits by Unyvice (Talk); changed back to last version by Scraig@pitt.edu

← Older revision		Revision as of 17:19, 15 November 2010
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	>== Investigating the robustness of vicarious learning: Sense making with deep-level reasoning questions ==		== Investigating the robustness of vicarious learning: Sense making with deep-level reasoning questions ==
	Scotty Craig, Kurt VanLehn, and Micki Chi''		Scotty Craig, Kurt VanLehn, and Micki Chi''

	=== Summary Table ===		=== Summary Table ===
	{\| border=~~"~~1~~"~~ cellspacing=~~"~~0~~"~~ cellpadding=~~"~~5~~"~~ style=~~"~~text-align: left;~~"~~		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
	\| '''PI''' \|\| Scotty Craig		\| '''PI''' \|\| Scotty Craig
	\|-		\|-
Line 22:		Line 22:
	\| '''DataShop''' \|\| Target date: June 15, 2007		\| '''DataShop''' \|\| Target date: June 15, 2007
	\|}		\|}
	~~<~~br~~>~~		<br>

	=== Abstract ===		=== Abstract ===
	Earlier work (Craig, at al. 2006; Gholson &~~amp;~~ Craig, in press) found that inserting relevant [[deep-level question]]s into observed video material both increased deep level question asking and improved learning. These lab studies had student learn topics in computer literacy by viewing videos of both monologues and dialogues; some material included [[deep-level question]]s, some included shallow questions and some included no questions. The conditions that included [[deep-level question]]s learned more than the others. However, it is not known how this method works compared to other methods for enhancing learning from observed materials (e.g. prompting for [[self-explanation]]). It is also not known if this effect can be useful for learning outside the lab setting.		Earlier work (Craig, at al. 2006; Gholson & Craig, in press) found that inserting relevant [[deep-level question]]s into observed video material both increased deep level question asking and improved learning. These lab studies had student learn topics in computer literacy by viewing videos of both monologues and dialogues; some material included [[deep-level question]]s, some included shallow questions and some included no questions. The conditions that included [[deep-level question]]s learned more than the others. However, it is not known how this method works compared to other methods for enhancing learning from observed materials (e.g. prompting for [[self-explanation]]). It is also not known if this effect can be useful for learning outside the lab setting.

	Our [[in vivo experiment]] presented identical core content on magnetism using example problems from the Andes tutoring system in three different ways. The material was presented in three formats. All three of these formats were presented as a video of a [[worked examples\|worked example]] with each step corresponding to a [[knowledge component]]. The [[knowledge components]] were preceded by a [[deep-level question]] (e.g. What are the implications of having the magnetic field close to an electrified wire?), a prompt for learners to reflection on the material (i.e. a pause in the video) or a [[self-explanation]] prompt (e.g. Please begin your self explanation). Measures of [[Andes]] transfer, and long term [[robust learning]] were measured. The learners’ interaction with Andes were coded for differences on completion time, within task behavior, and the completion rates of the Andes homework.		Our [[in vivo experiment]] presented identical core content on magnetism using example problems from the Andes tutoring system in three different ways. The material was presented in three formats. All three of these formats were presented as a video of a [[worked examples\|worked example]] with each step corresponding to a [[knowledge component]]. The [[knowledge components]] were preceded by a [[deep-level question]] (e.g. What are the implications of having the magnetic field close to an electrified wire?), a prompt for learners to reflection on the material (i.e. a pause in the video) or a [[self-explanation]] prompt (e.g. Please begin your self explanation). Measures of [[Andes]] transfer, and long term [[robust learning]] were measured. The learners’ interaction with Andes were coded for differences on completion time, within task behavior, and the completion rates of the Andes homework.
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	'''Examples for each condition'''		'''Examples for each condition'''
	{\| border=~~"~~1~~"~~ cellspacing=~~"~~0~~"~~ cellpadding=~~"~~0~~"~~ style=~~"~~text-align: left;~~"~~		{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"
	\|		\|
	\|-		\|-
Line 53:		Line 53:
	concentric circles with the wire at their center		concentric circles with the wire at their center
	\|}		\|}
	~~<~~br~~>~~		<br>

	=== Hypothesis ===		=== Hypothesis ===
	A guided learning hypothesis would predict that since the deep-level questions provided a constant cognitive guide the deep-level question condition would improve learning over the reflection condition and possibly the self-explanation condition if the students could not produce the guidance while producing the self-explanations. Alternatively, a content equivalency hypothesis would be that since all three conditions provide the same content they should all produce learning of the material (Klahr &~~amp;~~ Nigam, 2004).		A guided learning hypothesis would predict that since the deep-level questions provided a constant cognitive guide the deep-level question condition would improve learning over the reflection condition and possibly the self-explanation condition if the students could not produce the guidance while producing the self-explanations. Alternatively, a content equivalency hypothesis would be that since all three conditions provide the same content they should all produce learning of the material (Klahr & Nigam, 2004).

	=== Dependent variables ===		=== Dependent variables ===
Line 63:		Line 63:

	=== Results ===		=== Results ===
	Participants’ homework performance was investigated by looking at Andes homework scores and completion time data. There were no differences found on Andes homework scores among the three groups. However, there was a marginally significant trend found on the completion time data in favor of participants in the deep-level question condition over those in the reflection condition (t (9) = 2.14, p = .07). This difference for completion time became significant when participants in the two unguided conditions were collapsed and compared against participants in the guided condition (t (15) = 2.41, p ~~<~~ .05). This significant differences represented a 55% savings in time to complete the problem for the participants in the deep-level questions condition.		Participants’ homework performance was investigated by looking at Andes homework scores and completion time data. There were no differences found on Andes homework scores among the three groups. However, there was a marginally significant trend found on the completion time data in favor of participants in the deep-level question condition over those in the reflection condition (t (9) = 2.14, p = .07). This difference for completion time became significant when participants in the two unguided conditions were collapsed and compared against participants in the guided condition (t (15) = 2.41, p < .05). This significant differences represented a 55% savings in time to complete the problem for the participants in the deep-level questions condition.

	=== Explanation ===		=== Explanation ===
Line 75:		Line 75:
	* Presented at PSLC Roadshow - Memphis November, 2006		* Presented at PSLC Roadshow - Memphis November, 2006
	* Presented at LRDC Graduate student recruitment - Pittsburgh Feburary, 2007		* Presented at LRDC Graduate student recruitment - Pittsburgh Feburary, 2007
	* Presented at VanLehn, K., Hausmann, R., &~~amp;~~ Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.
	* Presented at VanLehn, K., Hausmann, R., &~~amp;~~ Craig, S. (2007). PSLC EARLI Symposium.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.
	* Craig, S. D., VanLehn, K., &~~amp;~~ Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin, R. Weber, R. Weber, R. Carlsen, &~~amp;~~ D.A. Willis (Eds.). The proceedings of the 19th International conference for the Society for Information Technology &~~amp;~~ Teacher Education. (pp. 1065-1068). Chesapeake, VA: AACE.		* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin, R. Weber, R. Weber, R. Carlsen, & D.A. Willis (Eds.). The proceedings of the 19th International conference for the Society for Information Technology & Teacher Education. (pp. 1065-1068). Chesapeake, VA: AACE.

	=== References ===		=== References ===

	* Chi, M. T. H., Hausmann, R. G. M., &~~amp;~~ Roy, M. (under revision). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. ''Cognitive Science.''		* Chi, M. T. H., Hausmann, R. G. M., & Roy, M. (under revision). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. ''Cognitive Science.''
	* Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., &~~amp;~~ Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. ''Cognitive Science, 13'', 145-182.		* Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. ''Cognitive Science, 13'', 145-182.
	* Chi, M. T. H., de Leew, N., Chiu, M., &~~amp;~~ LaVancher, C. (1994). Eliciting self-explanations improves understanding. ''Cognitive Science, 18'', 439-477.		* Chi, M. T. H., de Leew, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. ''Cognitive Science, 18'', 439-477.
	* Craig, S. D., Driscoll, D., &~~amp;~~ Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. ''Journal of Educational Multimedia and Hypermedia, 13'', 163-183. [http://andes3.lrdc.pitt.edu/~scraig/publications/Craigetal2004VL.pdf]		* Craig, S. D., Driscoll, D., & Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. ''Journal of Educational Multimedia and Hypermedia, 13'', 163-183. [http://andes3.lrdc.pitt.edu/~scraig/publications/Craigetal2004VL.pdf]
	* Craig, S. D., Sullins, J., Witherspoon, A. &~~amp;~~ Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.		* Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.
	* Gholson, B. &~~amp;~~ Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&~~amp;~~Craig2006.pdf]		* Gholson, B. & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&Craig2006.pdf]
	* Klahr, D. &~~amp;~~ Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. ''Psychological Science, 15'', 661-667.		* Klahr, D. & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. ''Psychological Science, 15'', 661-667.

	=== Connections ===		=== Connections ===
Line 98:		Line 98:

	Self explanations during learning		Self explanations during learning
	* [[Hausmann Study2 \| The Effects of Interaction on Robust Learning (Hausmann &~~amp;~~ Chi)]]		* [[Hausmann Study2 \| The Effects of Interaction on Robust Learning (Hausmann & Chi)]]
	* [[Hausmann Study \| A comparison of self-explanation to instructional explanation (Hausmann &~~amp;~~ Vanlehn)]]		* [[Hausmann Study \| A comparison of self-explanation to instructional explanation (Hausmann & Vanlehn)]]

	Learning by Observing		Learning by Observing
	* [[Craig_observing \| Learning from Problem Solving while Observing Worked Examples (Craig, Gadgil, &~~amp;~~ Chi)]]		* [[Craig_observing \| Learning from Problem Solving while Observing Worked Examples (Craig, Gadgil, & Chi)]]
	--[[User:Scraig@pitt.edu\|Scotty]] 12:12, 19 September 2006 (EDT)		--[[User:Scraig@pitt.edu\|Scotty]] 12:12, 19 September 2006 (EDT)

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	~~=[http://etisowope.co.cc UNDER COSTRUCTION, PLEASE SEE THIS POST IN RESERVE COPY]=~~
	~~----~~
	~~=[http://etisowope.co.cc CLICK HERE]=~~
	~~----~~
	~~</div>~~

Unyvice at 09:13, 15 November 2010

2010-11-15T09:13:45Z

← Older revision		Revision as of 09:13, 15 November 2010
Line 1:		Line 1:
	== Investigating the robustness of vicarious learning: Sense making with deep-level reasoning questions ==		>== Investigating the robustness of vicarious learning: Sense making with deep-level reasoning questions ==
	Scotty Craig, Kurt VanLehn, and Micki Chi''		Scotty Craig, Kurt VanLehn, and Micki Chi''

	=== Summary Table ===		=== Summary Table ===
	{\| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"		{\| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
	\| '''PI''' \|\| Scotty Craig		\| '''PI''' \|\| Scotty Craig
	\|-		\|-
Line 22:		Line 22:
	\| '''DataShop''' \|\| Target date: June 15, 2007		\| '''DataShop''' \|\| Target date: June 15, 2007
	\|}		\|}
	<br>		<br>

	=== Abstract ===		=== Abstract ===
	Earlier work (Craig, at al. 2006; Gholson & Craig, in press) found that inserting relevant [[deep-level question]]s into observed video material both increased deep level question asking and improved learning. These lab studies had student learn topics in computer literacy by viewing videos of both monologues and dialogues; some material included [[deep-level question]]s, some included shallow questions and some included no questions. The conditions that included [[deep-level question]]s learned more than the others. However, it is not known how this method works compared to other methods for enhancing learning from observed materials (e.g. prompting for [[self-explanation]]). It is also not known if this effect can be useful for learning outside the lab setting.		Earlier work (Craig, at al. 2006; Gholson & Craig, in press) found that inserting relevant [[deep-level question]]s into observed video material both increased deep level question asking and improved learning. These lab studies had student learn topics in computer literacy by viewing videos of both monologues and dialogues; some material included [[deep-level question]]s, some included shallow questions and some included no questions. The conditions that included [[deep-level question]]s learned more than the others. However, it is not known how this method works compared to other methods for enhancing learning from observed materials (e.g. prompting for [[self-explanation]]). It is also not known if this effect can be useful for learning outside the lab setting.

	Our [[in vivo experiment]] presented identical core content on magnetism using example problems from the Andes tutoring system in three different ways. The material was presented in three formats. All three of these formats were presented as a video of a [[worked examples\|worked example]] with each step corresponding to a [[knowledge component]]. The [[knowledge components]] were preceded by a [[deep-level question]] (e.g. What are the implications of having the magnetic field close to an electrified wire?), a prompt for learners to reflection on the material (i.e. a pause in the video) or a [[self-explanation]] prompt (e.g. Please begin your self explanation). Measures of [[Andes]] transfer, and long term [[robust learning]] were measured. The learners’ interaction with Andes were coded for differences on completion time, within task behavior, and the completion rates of the Andes homework.		Our [[in vivo experiment]] presented identical core content on magnetism using example problems from the Andes tutoring system in three different ways. The material was presented in three formats. All three of these formats were presented as a video of a [[worked examples\|worked example]] with each step corresponding to a [[knowledge component]]. The [[knowledge components]] were preceded by a [[deep-level question]] (e.g. What are the implications of having the magnetic field close to an electrified wire?), a prompt for learners to reflection on the material (i.e. a pause in the video) or a [[self-explanation]] prompt (e.g. Please begin your self explanation). Measures of [[Andes]] transfer, and long term [[robust learning]] were measured. The learners’ interaction with Andes were coded for differences on completion time, within task behavior, and the completion rates of the Andes homework.
Line 41:		Line 41:

	'''Examples for each condition'''		'''Examples for each condition'''
	{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"		{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"
	\|		\|
	\|-		\|-
Line 53:		Line 53:
	concentric circles with the wire at their center		concentric circles with the wire at their center
	\|}		\|}
	<br>		<br>

	=== Hypothesis ===		=== Hypothesis ===
	A guided learning hypothesis would predict that since the deep-level questions provided a constant cognitive guide the deep-level question condition would improve learning over the reflection condition and possibly the self-explanation condition if the students could not produce the guidance while producing the self-explanations. Alternatively, a content equivalency hypothesis would be that since all three conditions provide the same content they should all produce learning of the material (Klahr & Nigam, 2004).		A guided learning hypothesis would predict that since the deep-level questions provided a constant cognitive guide the deep-level question condition would improve learning over the reflection condition and possibly the self-explanation condition if the students could not produce the guidance while producing the self-explanations. Alternatively, a content equivalency hypothesis would be that since all three conditions provide the same content they should all produce learning of the material (Klahr & Nigam, 2004).

	=== Dependent variables ===		=== Dependent variables ===
Line 63:		Line 63:

	=== Results ===		=== Results ===
	Participants’ homework performance was investigated by looking at Andes homework scores and completion time data. There were no differences found on Andes homework scores among the three groups. However, there was a marginally significant trend found on the completion time data in favor of participants in the deep-level question condition over those in the reflection condition (t (9) = 2.14, p = .07). This difference for completion time became significant when participants in the two unguided conditions were collapsed and compared against participants in the guided condition (t (15) = 2.41, p < .05). This significant differences represented a 55% savings in time to complete the problem for the participants in the deep-level questions condition.		Participants’ homework performance was investigated by looking at Andes homework scores and completion time data. There were no differences found on Andes homework scores among the three groups. However, there was a marginally significant trend found on the completion time data in favor of participants in the deep-level question condition over those in the reflection condition (t (9) = 2.14, p = .07). This difference for completion time became significant when participants in the two unguided conditions were collapsed and compared against participants in the guided condition (t (15) = 2.41, p < .05). This significant differences represented a 55% savings in time to complete the problem for the participants in the deep-level questions condition.

	=== Explanation ===		=== Explanation ===
Line 75:		Line 75:
	* Presented at PSLC Roadshow - Memphis November, 2006		* Presented at PSLC Roadshow - Memphis November, 2006
	* Presented at LRDC Graduate student recruitment - Pittsburgh Feburary, 2007		* Presented at LRDC Graduate student recruitment - Pittsburgh Feburary, 2007
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.
	* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin, R. Weber, R. Weber, R. Carlsen, & D.A. Willis (Eds.). The proceedings of the 19th International conference for the Society for Information Technology & Teacher Education. (pp. 1065-1068). Chesapeake, VA: AACE.		* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin, R. Weber, R. Weber, R. Carlsen, & D.A. Willis (Eds.). The proceedings of the 19th International conference for the Society for Information Technology & Teacher Education. (pp. 1065-1068). Chesapeake, VA: AACE.

	=== References ===		=== References ===

	* Chi, M. T. H., Hausmann, R. G. M., & Roy, M. (under revision). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. ''Cognitive Science.''		* Chi, M. T. H., Hausmann, R. G. M., & Roy, M. (under revision). Learning from observing tutoring collaboratively: Insights about tutoring effectiveness from vicarious learning. ''Cognitive Science.''
	* Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. ''Cognitive Science, 13'', 145-182.		* Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. ''Cognitive Science, 13'', 145-182.
	* Chi, M. T. H., de Leew, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. ''Cognitive Science, 18'', 439-477.		* Chi, M. T. H., de Leew, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. ''Cognitive Science, 18'', 439-477.
	* Craig, S. D., Driscoll, D., & Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. ''Journal of Educational Multimedia and Hypermedia, 13'', 163-183. [http://andes3.lrdc.pitt.edu/~scraig/publications/Craigetal2004VL.pdf]		* Craig, S. D., Driscoll, D., & Gholson, B. (2004). Constructing knowledge from dialog in an intelligent tutoring system: Interactive learning, vicarious learning, and pedagogical agents. ''Journal of Educational Multimedia and Hypermedia, 13'', 163-183. [http://andes3.lrdc.pitt.edu/~scraig/publications/Craigetal2004VL.pdf]
	* Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.		* Craig, S. D., Sullins, J., Witherspoon, A. & Gholson, B. (2006). Deep-Level Reasoning Questions effect: The Role of Dialog and Deep-Level Reasoning Questions during Vicarious Learning. ''Cognition and Instruction, 24(4)'', 565-591.
	* Gholson, B. & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&Craig2006.pdf]		* Gholson, B. & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. ''Educational Psychology Review, 18'', 119-139. [http://andes3.lrdc.pitt.edu/~scraig/publications/Gholson&Craig2006.pdf]
	* Klahr, D. & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. ''Psychological Science, 15'', 661-667.		* Klahr, D. & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. ''Psychological Science, 15'', 661-667.

	=== Connections ===		=== Connections ===
Line 98:		Line 98:

	Self explanations during learning		Self explanations during learning
	* [[Hausmann Study2 \| The Effects of Interaction on Robust Learning (Hausmann & Chi)]]		* [[Hausmann Study2 \| The Effects of Interaction on Robust Learning (Hausmann & Chi)]]
	* [[Hausmann Study \| A comparison of self-explanation to instructional explanation (Hausmann & Vanlehn)]]		* [[Hausmann Study \| A comparison of self-explanation to instructional explanation (Hausmann & Vanlehn)]]

	Learning by Observing		Learning by Observing
	* [[Craig_observing \| Learning from Problem Solving while Observing Worked Examples (Craig, Gadgil, & Chi)]]		* [[Craig_observing \| Learning from Problem Solving while Observing Worked Examples (Craig, Gadgil, & Chi)]]
	--[[User:Scraig@pitt.edu\|Scotty]] 12:12, 19 September 2006 (EDT)		--[[User:Scraig@pitt.edu\|Scotty]] 12:12, 19 September 2006 (EDT)

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			=[http://etisowope.co.cc UNDER COSTRUCTION, PLEASE SEE THIS POST IN RESERVE COPY]=
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			=[http://etisowope.co.cc CLICK HERE]=
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			</div>

Scraig@pitt.edu: /* Annotated bibliography */

2008-03-18T22:03:59Z

Annotated bibliography

← Older revision		Revision as of 22:03, 18 March 2008
Line 77:		Line 77:
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.
	* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. The 19th International ~~conferences~~ for the ~~society~~ for Information ~~technology~~ & ~~teacher education~~.		* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. In K. McFerrin, R. Weber, R. Weber, R. Carlsen, & D.A. Willis (Eds.). The proceedings of the 19th International conference for the Society for Information Technology & Teacher Education. (pp. 1065-1068). Chesapeake, VA: AACE.

	=== References ===		=== References ===

Scraig@pitt.edu: /* Annotated bibliography */

2008-01-09T17:32:15Z

Annotated bibliography

← Older revision		Revision as of 17:32, 9 January 2008
Line 77:		Line 77:
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007, April). PSLC AERA Symposium: In vivo experimentation for understanding robust learning: Pros and cons.
	* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.		* Presented at VanLehn, K., Hausmann, R., & Craig, S. (2007). PSLC EARLI Symposium.
			* Craig, S. D., VanLehn, K., & Chi. M.T.H. (2008). Promoting learning by observing deep-level reasoning questions on quantitative physics problem solving with Andes. The 19th International conferences for the society for Information technology & teacher education.

	=== References ===		=== References ===

Scraig@pitt.edu: /* Independent variables */

2007-05-23T18:34:35Z

Independent variables

← Older revision		Revision as of 18:34, 23 May 2007
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	\|		\|
	\|-		\|-
	\| ''Deep question'' \|\| ''Self Explanation'' \|\| ''Reflection''		\| ''Deep-level question'' \|\| ''Self Explanation'' \|\| ''Reflection''
	\|-		\|-
	\| What effect does a straight current-carrying wire have on magnetic field lines? \|\| Please begin your self-explanation \|\| Pause for 10 seconds		\| What effect does a straight current-carrying wire have on magnetic field lines? \|\| Please begin your self-explanation \|\| Pause for 10 seconds

Scraig@pitt.edu: /* Research question */

2007-05-23T18:34:16Z

Research question

← Older revision		Revision as of 18:34, 23 May 2007
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	=== Research question ===		=== Research question ===
	Is robust learning better achieved by observing multimedia displays integrated with [[deep-level ~~questions~~]], prompts for [[reflection questions\|reflection]], or [[self-explanation]]?		Is robust learning better achieved by observing multimedia displays integrated with [[deep-level question]]s, prompts for [[reflection questions\|reflection]], or [[self-explanation]]?

	=== Independent variables ===		=== Independent variables ===

Scraig@pitt.edu: /* Research question */

2007-05-23T18:33:08Z

Research question

← Older revision		Revision as of 18:33, 23 May 2007
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	=== Research question ===		=== Research question ===
	Is robust learning better achieved by observing multimedia displays integrated with [[deep-level ~~reasoning~~ questions]], prompts for [[reflection questions\|reflection]], or [[self-explanation]]?		Is robust learning better achieved by observing multimedia displays integrated with [[deep-level questions]], prompts for [[reflection questions\|reflection]], or [[self-explanation]]?

	=== Independent variables ===		=== Independent variables ===

Scraig@pitt.edu: /* Dependent variables */

2007-04-18T17:29:06Z

Dependent variables

← Older revision		Revision as of 17:29, 18 April 2007
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	=== Dependent variables ===		=== Dependent variables ===

	* ''[[Long term retention]], homework on Andes'': After training, students did their regular homework problems using Andes. Students could do them whenever they wanted, but most students normally completed them just before the exam (''M'' = 39 days after training). The more similar homework problems (near transfer) were analyzed.		* ''[[Long-term retention]], homework on Andes'': After training, students did their regular homework problems using Andes. Students could do them whenever they wanted, but most students normally completed them just before the exam (''M'' = 39 days after training). The more similar homework problems (near transfer) were analyzed.

	=== Results ===		=== Results ===

Scraig@pitt.edu: /* Independent variables */

2007-04-18T14:46:40Z

Independent variables

← Older revision		Revision as of 14:46, 18 April 2007
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	The current study varied the level of guidance provided. The level of guidance was varied by presenting students with a deep-level reasoning condition, a self-explanation condition and a reflection condition. The deep-level reasoning questions provided a step-by-step guide that scaffolded the learner during the learning process. The self-explanation condition asked that students build the links of these scaffolds by self-explaining the steps. As a control for time on task, the reflection condition presented materials to the participants with a pause before each step.		The current study varied the level of guidance provided. The level of guidance was varied by presenting students with a deep-level reasoning condition, a self-explanation condition and a reflection condition. The deep-level reasoning questions provided a step-by-step guide that scaffolded the learner during the learning process. The self-explanation condition asked that students build the links of these scaffolds by self-explaining the steps. As a control for time on task, the reflection condition presented materials to the participants with a pause before each step.

			'''Examples for each condition'''
	{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"		{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"
	\| ~~'''Examples for each condition'''~~		\|
	\|-		\|-
	\| ''Deep question'' \|\| ''Self Explanation'' \|\| ''Reflection''		\| ''Deep question'' \|\| ''Self Explanation'' \|\| ''Reflection''

Scraig@pitt.edu: /* Independent variables */

2007-04-18T14:45:46Z

Independent variables

← Older revision		Revision as of 14:45, 18 April 2007
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	{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"		{\| border="1" cellspacing="0" cellpadding="0" style="text-align: left;"
	~~\| \|~~\| '''Examples for each condition'''		\| '''Examples for each condition'''
	\|-		\|-
	\| ''Deep question'' \|\| ''Self Explanation'' \|\| ''Reflection''		\| ''Deep question'' \|\| ''Self Explanation'' \|\| ''Reflection''
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	\| What effect does a straight current-carrying wire have on magnetic field lines? \|\| Please begin your self-explanation \|\| Pause for 10 seconds		\| What effect does a straight current-carrying wire have on magnetic field lines? \|\| Please begin your self-explanation \|\| Pause for 10 seconds
	\|-		\|-
	~~\| \|~~\| ''Corresponding Example text''		\| ''Corresponding Example text''
	\|-		\|-
	\| Magnetic field lines near a straight current-carrying wire take the form of		\| Magnetic field lines near a straight current-carrying wire take the form of