Nokes - Questionnaires - Revision history

Matthewbernacki: /* Summary Table */

2012-05-29T17:26:25Z

Summary Table

← Older revision		Revision as of 17:26, 29 May 2012
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	\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven		\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven
	\|-		\|-
	\| '''Other Contributors''' \|\| ~~Daniel Belenky,~~ Matthew Bernacki		\| '''Other Contributors''' \|\| Matthew Bernacki
	\|-		\|-
	\| '''Study Start Date''' \|\| ~~October~~ 2010		\| '''Study Start Date''' \|\| Study 1 (Greenville) Oct 2010 Study 2 (Hopewell)- August 2011
	\|-		\|-
	\| '''Study End Date''' \|\|		\| '''Study End Date''' \|\| Data collections are complete, analyses are ongoing.
	\|-		\|-
	\| '''Site''' \|\| Greenville High School		\| '''Site''' \|\| Greenville High School 2010-11; Hopewell SD 2011-12
	\|-		\|-
	\| '''Number of Students''' \|\| 67 students		\| '''Number of Students''' \|\| 67 Greenville Geometry students; ~300-350 Hopewell students (~125 per Bridge/Algebra/Geometry)
	\|-		\|-
	\| '''Total Participant Hours''' \|\|		\| '''Total Participant Hours''' \|\|

Alida: /* Summary Table */

2011-04-11T14:55:17Z

Summary Table

← Older revision		Revision as of 14:55, 11 April 2011
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	\| '''Total Participant Hours''' \|\|		\| '''Total Participant Hours''' \|\|
	\|-		\|-
	\| '''DataShop''' \|\|		\| '''Data available in DataShop''' \|\| [https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=448 Dataset: Affect Detectors and Questionnaires Greenville 2010-11]<br>
			* '''Pre/Post Test Score Data:''' TBD
			* '''Paper or Online Tests:''' TBD
			* '''Scanned Paper Tests:''' TBD
			* '''Blank Tests:''' TBD
			* '''Answer Keys: ''' TBD
	\|}		\|}
	<br>		<br>

Matthewbernacki: /* Summary Table */

2011-01-10T16:03:43Z

Summary Table

← Older revision		Revision as of 16:03, 10 January 2011
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	\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven		\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven
	\|-		\|-
	\| '''Other ~~Contributers~~''' \|\| Daniel Belenky, Matthew Bernacki		\| '''Other Contributors''' \|\| Daniel Belenky, Matthew Bernacki
	\|-		\|-
	\| '''Study Start Date''' \|\| October 2010		\| '''Study Start Date''' \|\| October 2010

Matthewbernacki: /* Summary Table */

2011-01-10T16:03:25Z

Summary Table

← Older revision		Revision as of 16:03, 10 January 2011
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	\| '''Site''' \|\| Greenville High School		\| '''Site''' \|\| Greenville High School
	\|-		\|-
	\| '''Number of Students''' \|\| 79 students		\| '''Number of Students''' \|\| 67 students
	\|-		\|-
	\| '''Total Participant Hours''' \|\|		\| '''Total Participant Hours''' \|\|

Matthewbernacki: /* Summary Table */

2010-10-12T16:06:25Z

Summary Table

← Older revision		Revision as of 16:06, 12 October 2010
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	\| '''Other Contributers''' \|\| Daniel Belenky, Matthew Bernacki		\| '''Other Contributers''' \|\| Daniel Belenky, Matthew Bernacki
	\|-		\|-
	\| '''Study Start Date''' \|\|		\| '''Study Start Date''' \|\| October 2010
	\|-		\|-
	\| '''Study End Date''' \|\|		\| '''Study End Date''' \|\|
	\|-		\|-
	\| '''Site''' \|\|		\| '''Site''' \|\| Greenville High School
	\|-		\|-
	\| '''Number of Students''' \|\|		\| '''Number of Students''' \|\| 79 students
	\|-		\|-
	\| '''Total Participant Hours''' \|\|		\| '''Total Participant Hours''' \|\|

Matthewbernacki: /* Plans to Assess the Relationship Between Motivation and Affect on Robust Learning */

2010-10-12T16:04:32Z

Plans to Assess the Relationship Between Motivation and Affect on Robust Learning

← Older revision		Revision as of 16:04, 12 October 2010
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	We will collect questionnaire data for a range of variables. This effort will have two components. First, we will take a microgenetic approach to collect questionnaire data with a small number of items that are administered frequently (i.e., dense data collection over a range of time periods, providing motivational / affective tracking from minutes to hours to weeks). These questionnaires will be embedded in the learning software and therefore can be administered between problems, or at beginning or end of session (and perhaps, subject to these constraints, randomly). This method of data collection will be applied to affective or motivational variables that are expected to vary more rapidly (e.g., interest, strategies, goal orientation towards the task, attitudes towards the learning materials). This approach will provide very fine-grained data as to how motivation and affective states change based on changes in the learning environment or task structure (e.g., difficulty, problem type, topic, domain, etc.), as well as student interaction with the tutor or peers (e.g., strategies, cognitive processing, etc.).		We will collect questionnaire data for a range of variables. This effort will have two components. First, we will take a microgenetic approach to collect questionnaire data with a small number of items that are administered frequently (i.e., dense data collection over a range of time periods, providing motivational / affective tracking from minutes to hours to weeks). These questionnaires will be embedded in the learning software and therefore can be administered between problems, or at beginning or end of session (and perhaps, subject to these constraints, randomly). This method of data collection will be applied to affective or motivational variables that are expected to vary more rapidly (e.g., interest, strategies, goal orientation towards the task, attitudes towards the learning materials). This approach will provide very fine-grained data as to how motivation and affective states change based on changes in the learning environment or task structure (e.g., difficulty, problem type, topic, domain, etc.), as well as student interaction with the tutor or peers (e.g., strategies, cognitive processing, etc.).

	Second, twice or three times a year we will administer questionnaires focused on constructs that may be semi-stable over time (e.g., self-efficacy, attitudes towards the domain, theory of intelligence, goal orientation towards the domain), a very traditional method in motivational research or research in SRL, although one whose shortcoming are increasingly being noticed (Zimmerman, 2008). Key to the current approach is that this more traditional type of data can be related to fine-grained data on PSLC measures of robust learning (instead of only using grades as a measure of learning which is typically the measure used in the literature in naturalistic learning settings). These questionnaires will be administered ~~on paper, or perhaps~~ electronically ~~using SurveyMonkey or CTAT~~.		Second, twice or three times a year we will administer questionnaires focused on constructs that may be semi-stable over time (e.g., self-efficacy, attitudes towards the domain, theory of intelligence, goal orientation towards the domain), a very traditional method in motivational research or research in SRL, although one whose shortcoming are increasingly being noticed (Zimmerman, 2008). Key to the current approach is that this more traditional type of data can be related to fine-grained data on PSLC measures of robust learning (instead of only using grades as a measure of learning which is typically the measure used in the literature in naturalistic learning settings). These questionnaires will be administered electronically via the tutor in units explicitly designed for questionnaire administration.

	The data from these two approaches will enable the [[Metacognition_and_Motivation \| Metacogniton and Motivation]] thrust to test path and structural equation models of the relation of particular M&M states and behaviors to robust learning (see Blackwell, Trzesniewski, and Dweck, 2007 for an example). Critically we will be linking motivational and affect variables to cognitive processes (by which they are hypothesized to do their work) and robust learning outcome measures. One goal of this work is theoretical integration of past work at the PSLC on instructional principles (macro-level), cognitive processes / knowledge components (mirco-level) and measures of robust learning to research / work and results on motivation and affect. Furthermore, this project provides a unique opportunity to test the generalizability of current and new theories of learning and motivation and affect across a number of academic domains (LearnLabs). This project will also play a critical role in the Theoretical Integration project of the thrust described in section 1.3.		The data from these two approaches will enable the [[Metacognition_and_Motivation \| Metacogniton and Motivation]] thrust to test path and structural equation models of the relation of particular M&M states and behaviors to robust learning (see Blackwell, Trzesniewski, and Dweck, 2007 for an example). Critically we will be linking motivational and affect variables to cognitive processes (by which they are hypothesized to do their work) and robust learning outcome measures. One goal of this work is theoretical integration of past work at the PSLC on instructional principles (macro-level), cognitive processes / knowledge components (mirco-level) and measures of robust learning to research / work and results on motivation and affect. Furthermore, this project provides a unique opportunity to test the generalizability of current and new theories of learning and motivation and affect across a number of academic domains (LearnLabs). This project will also play a critical role in the Theoretical Integration project of the thrust described in section 1.3.
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	*Self-efficacy (Bandura , 1997)		*Self-efficacy (Bandura , 1997)
	*Theory of intelligence (entity, incremental) (Dweck, 2006)		*Theory of intelligence (entity, incremental) (Dweck, 2006)
	*Achievement goals (performance-approach, performance-avoidance, learning) (Darnon, Butera, & Harackiewicz, 2007; Elliot & Dweck, 1988)		*Achievement goals (mastery, performance-approach, performance-avoidance, learning) (Darnon, Butera, & Harackiewicz, 2007; Elliot & Dweck, 1988; Elliot & Murayama, 2008)
	*Interest (Hidi & Renninger, 2006)		*Interest (Hidi & Renninger, 2006; Linnenbrink-Garcia,Durik, Conley, Barron, Tauer, Karabenick, & Harackiewicz, 2010)

	==Results==		==Results==

Matthewbernacki: /* Summary Table */

2010-08-20T20:14:40Z

Summary Table

← Older revision		Revision as of 20:14, 20 August 2010
Line 4:		Line 4:
	\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven		\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven
	\|-		\|-
	\| '''Other Contributers''' \|\| Daniel Belenky, ~~Matt~~ Bernacki		\| '''Other Contributers''' \|\| Daniel Belenky, Matthew Bernacki
	\|-		\|-
	\| '''Study Start Date''' \|\|		\| '''Study Start Date''' \|\|

Matthewbernacki: /* Summary Table */

2010-08-20T20:14:22Z

Summary Table

← Older revision		Revision as of 20:14, 20 August 2010
Line 4:		Line 4:
	\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven		\| '''PIs''' \|\| Timothy Nokes, Vincent Aleven
	\|-		\|-
	\| '''Other Contributers''' \|\| Daniel Belenky		\| '''Other Contributers''' \|\| Daniel Belenky, Matt Bernacki
	\|-		\|-
	\| '''Study Start Date''' \|\|		\| '''Study Start Date''' \|\|

Bvds: /* Plans to Assess the Relationship Between Motivation and Affect on Robust Learning */

2010-07-21T03:41:51Z

Plans to Assess the Relationship Between Motivation and Affect on Robust Learning

← Older revision		Revision as of 03:41, 21 July 2010
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	==Plans to Assess the Relationship Between Motivation and Affect on Robust Learning==		==Plans to Assess the Relationship Between Motivation and Affect on Robust Learning==
	e will collect questionnaire data for a range of variables. This effort will have two components. First, we will take a microgenetic approach to collect questionnaire data with a small number of items that are administered frequently (i.e., dense data collection over a range of time periods, providing motivational / affective tracking from minutes to hours to weeks). These questionnaires will be embedded in the learning software and therefore can be administered between problems, or at beginning or end of session (and perhaps, subject to these constraints, randomly). This method of data collection will be applied to affective or motivational variables that are expected to vary more rapidly (e.g., interest, strategies, goal orientation towards the task, attitudes towards the learning materials). This approach will provide very fine-grained data as to how motivation and affective states change based on changes in the learning environment or task structure (e.g., difficulty, problem type, topic, domain, etc.), as well as student interaction with the tutor or peers (e.g., strategies, cognitive processing, etc.).		We will collect questionnaire data for a range of variables. This effort will have two components. First, we will take a microgenetic approach to collect questionnaire data with a small number of items that are administered frequently (i.e., dense data collection over a range of time periods, providing motivational / affective tracking from minutes to hours to weeks). These questionnaires will be embedded in the learning software and therefore can be administered between problems, or at beginning or end of session (and perhaps, subject to these constraints, randomly). This method of data collection will be applied to affective or motivational variables that are expected to vary more rapidly (e.g., interest, strategies, goal orientation towards the task, attitudes towards the learning materials). This approach will provide very fine-grained data as to how motivation and affective states change based on changes in the learning environment or task structure (e.g., difficulty, problem type, topic, domain, etc.), as well as student interaction with the tutor or peers (e.g., strategies, cognitive processing, etc.).

	Second, twice or three times a year we will administer questionnaires focused on constructs that may be semi-stable over time (e.g., self-efficacy, attitudes towards the domain, theory of intelligence, goal orientation towards the domain), a very traditional method in motivational research or research in SRL, although one whose shortcoming are increasingly being noticed (Zimmerman, 2008). Key to the current approach is that this more traditional type of data can be related to fine-grained data on PSLC measures of robust learning (instead of only using grades as a measure of learning which is typically the measure used in the literature in naturalistic learning settings). These questionnaires will be administered on paper, or perhaps electronically using SurveyMonkey or CTAT.		Second, twice or three times a year we will administer questionnaires focused on constructs that may be semi-stable over time (e.g., self-efficacy, attitudes towards the domain, theory of intelligence, goal orientation towards the domain), a very traditional method in motivational research or research in SRL, although one whose shortcoming are increasingly being noticed (Zimmerman, 2008). Key to the current approach is that this more traditional type of data can be related to fine-grained data on PSLC measures of robust learning (instead of only using grades as a measure of learning which is typically the measure used in the literature in naturalistic learning settings). These questionnaires will be administered on paper, or perhaps electronically using SurveyMonkey or CTAT.
Line 41:		Line 41:
	*Achievement goals (performance-approach, performance-avoidance, learning) (Darnon, Butera, & Harackiewicz, 2007; Elliot & Dweck, 1988)		*Achievement goals (performance-approach, performance-avoidance, learning) (Darnon, Butera, & Harackiewicz, 2007; Elliot & Dweck, 1988)
	*Interest (Hidi & Renninger, 2006)		*Interest (Hidi & Renninger, 2006)

	==Results==		==Results==
	Forthcoming		Forthcoming

Dmbelenk at 17:56, 11 December 2009

2009-12-11T17:56:37Z

← Older revision		Revision as of 17:56, 11 December 2009
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	==Abstract==		==Abstract==
	We will develop infrastructure to collect variables related to metacognition, affect, and motivation across all LearnLabs. The planned LearnLab instrumentation involves two innovations in measurement: We will use microgenetic approaches for the fine-grained sampling of constructs vis-à-vis 1) the repeated administering of brief questionnaire items and less frequent longitudinal sampling using longer questionnaires, and 2) moment-by-moment behavioral data, including automatic monitoring in learning software. Our unique strength in these areas will be the ability to combine rich layers of behavioral measures (cognition, metacognition, affect, motivation), which will be used to create online models that can predict moment-by-moment changes. In doing so, we will leverage DataShop capabilities; the DataShop has been designed explicitly to accommodate multiple interpretations of student interaction data, if necessary at different grain sizes.		We will develop infrastructure to collect variables related to [[metacognition]], affect, and motivation across all LearnLabs. The planned LearnLab instrumentation involves two innovations in measurement: We will use microgenetic approaches for the fine-grained sampling of constructs vis-à-vis 1) the repeated administering of brief questionnaire items and less frequent longitudinal sampling using longer questionnaires, and 2) moment-by-moment behavioral data, including automatic monitoring in learning software. Our unique strength in these areas will be the ability to combine rich layers of behavioral measures (cognition, metacognition, affect, motivation), which will be used to create online models that can predict moment-by-moment changes. In doing so, we will leverage DataShop capabilities; the DataShop has been designed explicitly to accommodate multiple interpretations of student interaction data, if necessary at different grain sizes.

	This project will enable us to collect data on metacognitive, motivational and affective states in naturalistic learning settings at an unprecedented level of fine-grained detail (both temporal as well as type, multiple simultaneous measures). This level of detail will enable the PSLC learning scientists and learning scientists at large (through the DataShop) to test novel questions and theoretical models of the relation between M&M behaviors and states and robust learning that have been previously unable to be tested. Furthermore, this project will enable us to test the generalizability of current theoretical models in the literature (e.g., Blackwell, Trzesniewski, & Dweck, 2007).		This project will enable us to collect data on metacognitive, motivational and affective states in naturalistic learning settings at an unprecedented level of fine-grained detail (both temporal as well as type, multiple simultaneous measures). This level of detail will enable the PSLC learning scientists and learning scientists at large (through the DataShop) to test novel questions and theoretical models of the relation between M&M behaviors and states and robust learning that have been previously unable to be tested. Furthermore, this project will enable us to test the generalizability of current theoretical models in the literature (e.g., Blackwell, Trzesniewski, & Dweck, 2007).