Roll - Inquiry - Revision history

Ido: /* Summary Table */

2011-02-16T00:07:16Z

Summary Table

← Older revision		Revision as of 00:07, 16 February 2011
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	! Study # !! Focus !! Start Date !! End Date !! LearnLab Site !! # of Students !! Total Participant Hours !! DataShop?		! Study # !! Focus !! Start Date !! End Date !! LearnLab Site !! # of Students !! Total Participant Hours !! DataShop?
	\|-		\|-
	\| '''1''' \|\| Ethnography \|\| 1/2010 \|\| 4/2010 \|\| UBC Physics \|\| 20 \|\| ~~280~~ \|\| No, ~~audio~~ data		\| '''1''' \|\| Ethnography \|\| 1/2010 \|\| 4/2010 \|\| UBC Physics \|\| 14 \|\| 28 \|\| No, ethnographic data
	\|-		\|-
	\| '''2''' \|\| SRL scaffold \|\| 3/2010 \|\| 3/2010 \|\| UBC Physics \|\| 130 \|\| 130 \|\| No, paper data		\| '''2''' \|\| SRL scaffold \|\| 3/2010 \|\| 3/2010 \|\| UBC Physics \|\| 130 \|\| 130 \|\| No, paper data

Ido: /* Summary Table */

2011-02-16T00:06:06Z

Summary Table

← Older revision		Revision as of 00:06, 16 February 2011
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	! Study # !! Focus !! Start Date !! End Date !! LearnLab Site !! # of Students !! Total Participant Hours !! DataShop?		! Study # !! Focus !! Start Date !! End Date !! LearnLab Site !! # of Students !! Total Participant Hours !! DataShop?
	\|-		\|-
	\| '''1''' \|\| Ethnography \|\| 1/2010 \|\| 4/2010 \|\| UBC Physics \|\| 40 \|\| 280 \|\| No, ~~old~~ data		\| '''1''' \|\| Ethnography \|\| 1/2010 \|\| 4/2010 \|\| UBC Physics \|\| 20 \|\| 280 \|\| No, audio data
	\|-		\|-
	\| '''2''' \|\| SRL ~~prompts~~ \|\| 3/2010 \|\| 3/2010 \|\| UBC Physics \|\| ~~170~~ \|\| ~~250~~ \|\| No, ~~still needs compilation~~		\| '''2''' \|\| SRL scaffold \|\| 3/2010 \|\| 3/2010 \|\| UBC Physics \|\| 130 \|\| 130 \|\| No, paper data
	\|-		\|-
	\| '''3''' \|\| SRL training \|\| 1/2011 \|\| 4/2011 \|\| UBC Physics \|\| ~~170~~ \|\| ~~1000~~ \|\| ~~No, no data yet~~		\| '''3''' \|\| SRL training \|\| 1/2011 \|\| 4/2011 \|\| UBC Physics \|\| 150 \|\| 1200 \|\| Yes
	\|}		\|}

Ngholmes: /* Results */

2010-10-25T19:16:49Z

Results

← Older revision		Revision as of 19:16, 25 October 2010
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	=== Results ===		=== Results ===

	We found that students who received prompts were more likely to incorporate key features in their methods. Also, while most students were found to spontaneously add some form of comments to their invented methods (no self-explanation prompts were given), those who received metacognitive prompts were more likely to add ~~high~~ quality self-explanations. That is, their comments discussed key features of the domain (rather than lower-level technical aspects), describing goals for their invention or evaluations of their methods.		We found that students who received prompts were more likely to incorporate key features in their methods. Also, while most students were found to spontaneously add some form of comments to their invented methods (no self-explanation prompts were given), those who received metacognitive prompts were more likely to add higher quality self-explanations. That is, their comments discussed key features of the domain (rather than lower-level technical aspects), describing goals for their invention or evaluations of their methods.
	These results suggest that metacognitive prompts help students apply SRL strategies and reach better results at the domain level. However, we are yet to find out whether such instruction helps students learn better over time – that is, will students acquire better SRL skills given SRL support, and will these skills transfer to new tasks at the domain level?		These results suggest that metacognitive prompts help students apply SRL strategies and reach better results at the domain level. However, we are yet to find out whether such instruction helps students learn better over time – that is, will students acquire better SRL skills given SRL support, and will these skills transfer to new tasks at the domain level?

Ngholmes: /* Results */

2010-10-25T19:10:45Z

Results

← Older revision		Revision as of 19:10, 25 October 2010
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	=== Results ===		=== Results ===

	We found that students who received prompts were more likely to incorporate key features in their methods. Also, students ~~who received metacognitive prompts~~ were ~~more likely~~ to ~~self-explain~~ their methods ~~spontaneously~~ (no self-explanation prompts were given)~~. Furthermore~~, ~~their~~ self-explanations ~~were of higher quality~~, ~~discussing~~ key features of the domain (rather than lower-level technical aspects).		We found that students who received prompts were more likely to incorporate key features in their methods. Also, while most students were found to spontaneously add some form of comments to their invented methods (no self-explanation prompts were given), those who received metacognitive prompts were more likely to add high quality self-explanations. That is, their comments discussed key features of the domain (rather than lower-level technical aspects), describing goals for their invention or evaluations of their methods.
	These results suggest that metacognitive prompts help students apply SRL strategies and reach better results at the domain level. However, we are yet to find out whether such instruction helps students learn better over time – that is, will students acquire better SRL skills given SRL support, and will these skills transfer to new tasks at the domain level?		These results suggest that metacognitive prompts help students apply SRL strategies and reach better results at the domain level. However, we are yet to find out whether such instruction helps students learn better over time – that is, will students acquire better SRL skills given SRL support, and will these skills transfer to new tasks at the domain level?

Ido: /* References */

2010-09-29T15:14:19Z

References

← Older revision		Revision as of 15:14, 29 September 2010
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	===References===		===References===

			Butler, D. L., & Cartier, S. C. (2004). Promoting effective task interpretation as an important work habit: A key to successful teaching and learning. Teachers College Record, 106(9), 1729-1758.

	Kapur, M., & Lee, K. (2009). Designing for productive failure in mathematical problem solving. In Proceedings of the 31st annual conference of the cognitive science society. (pp. 2632-7). Austin, TX: Cognitive Science Society.		Kapur, M., & Lee, K. (2009). Designing for productive failure in mathematical problem solving. In Proceedings of the 31st annual conference of the cognitive science society. (pp. 2632-7). Austin, TX: Cognitive Science Society.

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	Veermans, K., de Jong, T., & van Joolingen, W. R. (2000). Promoting self-directed learning in simulation-based discovery learning environments through intelligent support. Interactive Learning Environments, 8(3), 229-255.		Veermans, K., de Jong, T., & van Joolingen, W. R. (2000). Promoting self-directed learning in simulation-based discovery learning environments through intelligent support. Interactive Learning Environments, 8(3), 229-255.

			Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice. (pp. 277-304). Hillsdale, NJ: Erlbaum.

Ido: /* Study 1: an Ethnography */

2010-09-29T15:13:18Z

Study 1: an Ethnography

← Older revision		Revision as of 15:13, 29 September 2010
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	The goal of Study 1 was to identify key SRL processes during the invention activity. In particular we chose to focus on skills that are crucial to the success of the invention process, and that students often skip or apply ineffectively. The study was done in two college level courses: A first year Physics lab (4 sections, 175 students), and a large first year Biology class (900 students, 3 sections). We collected over 20 hours of data from observations of groups of students working on 8 structured invention activities in these classes. In a qualitative analysis, we identified several key metacognitive behaviors that seem to contribute to the quality of students’ learning and inventions:		The goal of Study 1 was to identify key SRL processes during the invention activity. In particular we chose to focus on skills that are crucial to the success of the invention process, and that students often skip or apply ineffectively. The study was done in two college level courses: A first year Physics lab (4 sections, 175 students), and a large first year Biology class (900 students, 3 sections). We collected over 20 hours of data from observations of groups of students working on 8 structured invention activities in these classes. In a qualitative analysis, we identified several key metacognitive behaviors that seem to contribute to the quality of students’ learning and inventions:

	1. Task ~~perception~~ - Students need to read the task, and understand what is required from them. This has two components - The first is to understand what the target construct is. Students (and scientists) are expected to define what should be invented prior to attempting to invent their methods (Shute & Glaser, 1990). The second component of task ~~perception~~ is to understand the nature of invention activity - the goal is to design a method that captures the deep invariant structure, not to embark on number hunting.		1. Task interpretation - Students need to read the task, and understand what is required from them. This has two components - The first is to understand what the target construct is. Students (and scientists) are expected to define what should be invented prior to attempting to invent their methods (Shute & Glaser, 1990). The second component of task interpretation (Butler & Cartier 2004) is to understand the nature of invention activity - the goal is to design a method that captures the deep invariant structure, not to embark on number hunting. Butler and Cartier (2004) emphasize task interpretation as a work habit, and so do we.

	2. Qualitative analysis / goal setting – Students should interpret existing data and identify requirements that their solution should satisfy (e.g., variability should control for sample size; best fitting lines should take into account uncertainty of measurements; etc). The qualitative analysis informs the later design. It helps the students choose the right tools and activate the right prior knowledge.		2. Qualitative analysis / goal setting – Students should interpret existing data and identify requirements that their solution should satisfy (e.g., variability should control for sample size; best fitting lines should take into account uncertainty of measurements; etc). The qualitative analysis informs the later design. It helps the students choose the right tools and activate the right prior knowledge.

	2. Planning – Inventing methods to capture deep properties is a complex process. Students often ignore the goals they have set for themselves in the hustle-and-bustle of the technical invention and implementation work. The goal of the planning phase is to make sure that their goals are being met. This is done by assigning procedural components to conceptual goals (for example, controlling for sample size can be done by dividing by N).		3. Planning – Inventing methods to capture deep properties is a complex process. Students often ignore the goals they have set for themselves in the hustle-and-bustle of the technical invention and implementation work. The goal of the planning phase is to make sure that their goals are being met. This is done by assigning procedural components to conceptual goals (for example, controlling for sample size can be done by dividing by N).

	3. ~~Verifying – Upon completion of the invention process students (and scientists alike) are expected~~ to ~~revisit~~ their invented ~~method~~ and ~~its outcomes~~, ~~and contrast them with~~ the ~~given data~~ and ~~identified goals. Does their method achieve its purpose? If~~ not~~, students are expected to revise their invented method~~.		4. Implementation - Students then need to carry out their plan. To our great surprise, we often find mismatch between students' general invented methods and their actual implementation in Excel. Yet, this phase of the task is more relevant at the domain level and is not the in the focus of our project.

			5. Debugging – Upon completion of the invention process students (and scientists alike) are expected to revisit their invented method and its outcomes, and contrast them with the given data and identified goals. Does their method achieve its purpose? If not, students are expected to revise their invented method. This process includes verifications (Schoenfeld 1987) and adaptations (Winne & Hadwin, 1998).

			=== Conceptual Framework ===
	Our conceptual framework is based upon those put forward by Winne and Hadwin (1998) and Schoenfeld (1987)		Our conceptual framework is based upon those put forward by Winne and Hadwin (1998) and Schoenfeld (1987)

			{\| border="1"
			! Winne & Hadwin, 1998 !! Schoenfeld, 1987 !! Roll et al
			\|-
			\| Task definition \|\| Read \|\| Task interpretation
			\|-
			\| Goals & Plans \|\| Analyze \|\| Qualitative analysis
			\|-
			\| \|\| Plan \|\| Plan
			\|-
			\| Studying tactics \|\| Implement \|\| Implement
			\|-
			\| \|\| Explore \|\|
			\|-
			\| Adaptations \|\| Verifying \|\| Debugging
			\|}

	== Study 2: SRL prompts ==		== Study 2: SRL prompts ==

Ido: /* Background & Significance */

2010-09-29T15:03:01Z

Background & Significance

← Older revision		Revision as of 15:03, 29 September 2010
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	Scientific reasoning skills consist an important class of SRL behaviors. While traditional inquiry tasks have inherent benefits of letting students practice key self-regulatory skills, they were shown to be inefficient, and often unproductive, means of instruction. In the absence of adequate support, students often flounder and are lost within the infinite range of possibilities (Veermans, de Jong & van Joolingen, 2000). Consequently, students often fail to learn the target concepts, or at least do not learn them as efficiently as with direct instruction (Kirschner, Sweller & Clark, 2006).		Scientific reasoning skills consist an important class of SRL behaviors. While traditional inquiry tasks have inherent benefits of letting students practice key self-regulatory skills, they were shown to be inefficient, and often unproductive, means of instruction. In the absence of adequate support, students often flounder and are lost within the infinite range of possibilities (Veermans, de Jong & van Joolingen, 2000). Consequently, students often fail to learn the target concepts, or at least do not learn them as efficiently as with direct instruction (Kirschner, Sweller & Clark, 2006).

	This project focuses on scientific reasoning behaviors during inquiry, and studies the relationships between scientific reasoning behavior and domain learning and motivation. I focus on the Invention as Preparation for Learning framework (IPL; Schwartz & Taylor, 2004; Roll, Aleven & Koedinger, 2009). In IPL students are asked to invent novel mathematical procedures prior to receiving direct instruction on the canonical procedures. IPL was shown to improve students’ domain knowledge and motivation (Kapur & Lee, 2009; Roll, Aleven & Koedinger, 2009; Schwartz & Taylor, 2004). At the same time, students demonstrated poor metacognitive behavior, and lack of learning at the metacognitive level (Roll, 2009).		This project focuses on scientific reasoning behaviors during inquiry, and studies the relationships between scientific reasoning behavior and domain learning and motivation. We focus on the Invention as Preparation for Learning framework (IPL; Schwartz & Taylor, 2004; Roll, Aleven & Koedinger, 2009). In IPL students are asked to invent novel mathematical procedures prior to receiving direct instruction on the canonical procedures. IPL was shown to improve students’ domain knowledge and motivation (Kapur & Lee, 2009; Roll, Aleven & Koedinger, 2009; Schwartz & Taylor, 2004). At the same time, students demonstrated poor metacognitive behavior, and lack of learning at the metacognitive level (Roll, 2009).


	The current project first seeks to identify the scientific reasoning skills that are being practiced in IPL. The second stage of the project assesses the transferability of these skills (across domain topics, and along time). Last, I will investigate the effect of supporting these skills on students' domain and metacognitive learning.		The current project first seeks to identify the scientific reasoning skills that are being practiced in IPL. The second stage of the project assesses the transferability of these skills (across domain topics, and along time). Last, we will investigate the effect of supporting these skills on students' domain and metacognitive learning.


			Our theoretical framework with regard to metacognitive and SRL skills is detailed under Study I.

	==Glossary==		==Glossary==

Ido: /* Study 1: an Ethnography */

2010-09-29T15:01:15Z

Study 1: an Ethnography

← Older revision		Revision as of 15:01, 29 September 2010
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	The goal of Study 1 was to identify key SRL processes during the invention activity. In particular we chose to focus on skills that are crucial to the success of the invention process, and that students often skip or apply ineffectively. The study was done in two college level courses: A first year Physics lab (4 sections, 175 students), and a large first year Biology class (900 students, 3 sections). We collected over 20 hours of data from observations of groups of students working on 8 structured invention activities in these classes. In a qualitative analysis, we identified several key metacognitive behaviors that seem to contribute to the quality of students’ learning and inventions:		The goal of Study 1 was to identify key SRL processes during the invention activity. In particular we chose to focus on skills that are crucial to the success of the invention process, and that students often skip or apply ineffectively. The study was done in two college level courses: A first year Physics lab (4 sections, 175 students), and a large first year Biology class (900 students, 3 sections). We collected over 20 hours of data from observations of groups of students working on 8 structured invention activities in these classes. In a qualitative analysis, we identified several key metacognitive behaviors that seem to contribute to the quality of students’ learning and inventions:

	1. ~~Goal setting –~~ Students (and scientists) are expected to define what should be invented prior to attempting to invent their methods (Shute & Glaser, 1990). ~~In order~~ to ~~define~~ the ~~goal~~ of ~~the~~ invention ~~students should understand what~~ the ~~target concept refers~~ to, interpret existing data, and identify requirements that their solution should satisfy (e.g., variability should control for sample size; best fitting lines should take into account uncertainty of measurements; etc). ~~Schoenfeld (1987) refers to these skills as “read” and “analyze~~.~~” We build upon his definitions and add that it is not enough to read~~ and ~~to analyze – in addition,~~ the ~~information obtained though these processes should direct the invention process, thus being framed as a goal~~.		1. Task perception - Students need to read the task, and understand what is required from them. This has two components - The first is to understand what the target construct is. Students (and scientists) are expected to define what should be invented prior to attempting to invent their methods (Shute & Glaser, 1990). The second component of task perception is to understand the nature of invention activity - the goal is to design a method that captures the deep invariant structure, not to embark on number hunting.
	2. Planning – Inventing methods to capture deep properties is a complex process. Students often ignore the goals they have set for themselves in the hustle-and-bustle of the technical invention and implementation work~~, referred to by Schoenfeld (1987) as “explore” and “implement.” We use the term “planning” in a similar way to Schoenfeld (1987)~~. The goal of the planning phase is to make sure that their goals are being met. This is done by assigning procedural components to conceptual goals (for example, controlling for sample size can be done by dividing by N).
	3. Verifying – Upon completion of the invention process students (and scientists alike) are expected to revisit their invented method and its outcomes, and contrast them with the given data and identified goals. Does their method achieve its purpose? If not, students are expected to revise their invented method. ~~Also this construct~~ is ~~similar to Schoenfeld’s concept of verifying.~~		2. Qualitative analysis / goal setting – Students should interpret existing data and identify requirements that their solution should satisfy (e.g., variability should control for sample size; best fitting lines should take into account uncertainty of measurements; etc). The qualitative analysis informs the later design. It helps the students choose the right tools and activate the right prior knowledge.

			2. Planning – Inventing methods to capture deep properties is a complex process. Students often ignore the goals they have set for themselves in the hustle-and-bustle of the technical invention and implementation work. The goal of the planning phase is to make sure that their goals are being met. This is done by assigning procedural components to conceptual goals (for example, controlling for sample size can be done by dividing by N).

			3. Verifying – Upon completion of the invention process students (and scientists alike) are expected to revisit their invented method and its outcomes, and contrast them with the given data and identified goals. Does their method achieve its purpose? If not, students are expected to revise their invented method.

			Our conceptual framework is based upon those put forward by Winne and Hadwin (1998) and Schoenfeld (1987)

	== Study 2: SRL prompts ==		== Study 2: SRL prompts ==

Ido: /* References */

2010-09-24T21:49:50Z

References

← Older revision		Revision as of 21:49, 24 September 2010
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	Roll, I., Aleven, V., & Koedinger, K. R. (2009). Helping students know 'further' - increasing the flexibility of students' knowledge using symbolic invention tasks. In N. A. Taatgen, & H. van Rijn (Eds.), Proceedings of the 31st annual conference of the cognitive science society. (pp. 1169-74). Austin, TX: Cognitive Science Society.		Roll, I., Aleven, V., & Koedinger, K. R. (2009). Helping students know 'further' - increasing the flexibility of students' knowledge using symbolic invention tasks. In N. A. Taatgen, & H. van Rijn (Eds.), Proceedings of the 31st annual conference of the cognitive science society. (pp. 1169-74). Austin, TX: Cognitive Science Society.

			Schoenfeld, A. H. (1987). What's all the fuss about metacognition? In Cognitive science and mathematics education. (pp. 189-215). Hillsdale, N.J: Erlbaum.

	Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129-184.		Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129-184.

	Veermans, K., de Jong, T., & van Joolingen, W. R. (2000). Promoting self-directed learning in simulation-based discovery learning environments through intelligent support. Interactive Learning Environments, 8(3), 229-255.		Veermans, K., de Jong, T., & van Joolingen, W. R. (2000). Promoting self-directed learning in simulation-based discovery learning environments through intelligent support. Interactive Learning Environments, 8(3), 229-255.

Ido at 21:49, 24 September 2010

2010-09-24T21:49:04Z

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