https://learnlab.org/research/wiki/api.php?action=feedcontributions&user=Kirsten-Butcher&feedformat=atomLearnLab - User contributions [en]2024-03-29T01:32:27ZUser contributionsMediaWiki 1.31.12https://learnlab.org/wiki/index.php?title=Geometry_Greatest_Hits&diff=9455Geometry Greatest Hits2009-05-18T16:50:14Z<p>Kirsten-Butcher: /* Independent variables */</p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 98<br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from four instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following four principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* [[Accurate knowledge estimates principle]] <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these four principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
The PSLC’s in-vivo methodology, as well as standard practice in learning science, generally focuses on testing one principle at a time. This approach is useful for understanding which principles work, how they work, and what their boundary conditions are. However, it is also useful to test combinations of principles, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic. <br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
=== Glossary ===<br />
<br />
=== Hypotheses ===<br />
<br />
;H1<br />
: A tutor that uses multiple PSLC learning principles in combination, each of which have been validated to lead to better robust learning when applied, will achieve a significantly higher effect size compared to an unmodified tutor than the principles achieve on their own.<br />
<br />
=== Completed experiments===<br />
<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to a modified tutor with all four improvements). Measures of learning gains (including robust learning measures) and learning efficiency (time taken to complete tutor) were utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research<br />
<br />
* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)<br />
[[Image:VisVerb.GIF]]<br />
* non-interactive conceptual example sets at the beginning of each curricular unit<br />
* interactive worked examples at the beginning of each curricular, faded in an individualized manner<br />
[[Image:WorkedExample.GIF]]<br />
* diagrammatic self-explanations of incorrect steps<br />
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)<br />
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)<br />
<br />
The Greatest Hits version of the tutor is compared to a control condition, which features standard tutor interactions and instruction:<br />
[[Image:ControlCondition.GIF]]<br />
<br />
====Dependent Variables====<br />
*<b>Problem-Solving Items.</b><br><br />
Problem Solving items have a similar format to the tutor – students must use known information to calculate the measure of an angle and should justify their problem solving step with a relevant geometry rule. These problem solving items also contain several types of new tasks: First, student must make a solvability judgment to determine if enough information is known to solve the step. Second, for “false answers, students must explain how the unsolvable problem could be makes solvable. Third, for solvable items, students must explain which diagram elements apply to the geometry rule that was used in the problem solving step.<br><br />
[[Image:ProblemSolvingItem.GIF]]<br />
<br><br><br />
*<b>Reasoning Items.</b><br><br />
Students also complete reasoning items, which assess how well they understand the conceptual geometry relationships by which one feature is used to solve others. For these items, students should indicate whether they can find the angles of a certain geometry rule.<br><br />
[[Image:ReasoningItem.GIF]]<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
<br />
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995).<br />
Cognitive tutors: Lessons learned. The Journal of the Learning<br />
Sciences, 4 (2) 167-207.<br />
<br />
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson,<br />
D. C., & Soloway, E. (2004). A scaffolding design framework for software to support<br />
science inquiry. The Journal of the Learning Sciences, 13(3), 337-386.<br />
<br />
==== Future Plans ====</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:ControlCondition.GIF&diff=9454File:ControlCondition.GIF2009-05-18T16:48:22Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:WorkedExample.GIF&diff=9453File:WorkedExample.GIF2009-05-18T16:47:48Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisVerb.GIF&diff=9452File:VisVerb.GIF2009-05-18T16:47:21Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Geometry_Greatest_Hits&diff=9451Geometry Greatest Hits2009-05-18T16:44:14Z<p>Kirsten-Butcher: /* Dependent Variables */</p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 98<br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from four instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following four principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* [[Accurate knowledge estimates principle]] <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these four principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
The PSLC’s in-vivo methodology, as well as standard practice in learning science, generally focuses on testing one principle at a time. This approach is useful for understanding which principles work, how they work, and what their boundary conditions are. However, it is also useful to test combinations of principles, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic. <br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
=== Glossary ===<br />
<br />
=== Hypotheses ===<br />
<br />
;H1<br />
: A tutor that uses multiple PSLC learning principles in combination, each of which have been validated to lead to better robust learning when applied, will achieve a significantly higher effect size compared to an unmodified tutor than the principles achieve on their own.<br />
<br />
=== Completed experiments===<br />
<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to a modified tutor with all four improvements). Measures of learning gains (including robust learning measures) and learning efficiency (time taken to complete tutor) were utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research<br />
<br />
* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)<br />
* non-interactive conceptual example sets at the beginning of each curricular unit<br />
* interactive worked examples at the beginning of each curricular, faded in an individualized manner<br />
* diagrammatic self-explanations of incorrect steps<br />
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)<br />
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)<br />
<br />
====Dependent Variables====<br />
*<b>Problem-Solving Items.</b><br><br />
Problem Solving items have a similar format to the tutor – students must use known information to calculate the measure of an angle and should justify their problem solving step with a relevant geometry rule. These problem solving items also contain several types of new tasks: First, student must make a solvability judgment to determine if enough information is known to solve the step. Second, for “false answers, students must explain how the unsolvable problem could be makes solvable. Third, for solvable items, students must explain which diagram elements apply to the geometry rule that was used in the problem solving step.<br><br />
[[Image:ProblemSolvingItem.GIF]]<br />
<br><br><br />
*<b>Reasoning Items.</b><br><br />
Students also complete reasoning items, which assess how well they understand the conceptual geometry relationships by which one feature is used to solve others. For these items, students should indicate whether they can find the angles of a certain geometry rule.<br><br />
[[Image:ReasoningItem.GIF]]<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
<br />
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995).<br />
Cognitive tutors: Lessons learned. The Journal of the Learning<br />
Sciences, 4 (2) 167-207.<br />
<br />
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson,<br />
D. C., & Soloway, E. (2004). A scaffolding design framework for software to support<br />
science inquiry. The Journal of the Learning Sciences, 13(3), 337-386.<br />
<br />
==== Future Plans ====</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:ReasoningItem.GIF&diff=9450File:ReasoningItem.GIF2009-05-18T16:43:07Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Geometry_Greatest_Hits&diff=9449Geometry Greatest Hits2009-05-18T16:42:02Z<p>Kirsten-Butcher: /* Independent variables */</p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 98<br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from four instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following four principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* [[Accurate knowledge estimates principle]] <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these four principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
The PSLC’s in-vivo methodology, as well as standard practice in learning science, generally focuses on testing one principle at a time. This approach is useful for understanding which principles work, how they work, and what their boundary conditions are. However, it is also useful to test combinations of principles, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic. <br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
=== Glossary ===<br />
<br />
=== Hypotheses ===<br />
<br />
;H1<br />
: A tutor that uses multiple PSLC learning principles in combination, each of which have been validated to lead to better robust learning when applied, will achieve a significantly higher effect size compared to an unmodified tutor than the principles achieve on their own.<br />
<br />
=== Completed experiments===<br />
<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to a modified tutor with all four improvements). Measures of learning gains (including robust learning measures) and learning efficiency (time taken to complete tutor) were utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research<br />
<br />
* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)<br />
* non-interactive conceptual example sets at the beginning of each curricular unit<br />
* interactive worked examples at the beginning of each curricular, faded in an individualized manner<br />
* diagrammatic self-explanations of incorrect steps<br />
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)<br />
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)<br />
<br />
====Dependent Variables====<br />
*<b>Problem-Solving items.</b><br><br />
Problem Solving items have a similar format to the tutor – students must use known information to calculate the measure of an angle and should justify their problem solving step with a relevant geometry rule. These problem solving items also contain several types of new tasks: First, student must make a solvability judgment to determine if enough information is known to solve the step. Second, for “false answers, students must explain how the unsolvable problem could be makes solvable. Third, for solvable items, students must explain which diagram elements apply to the geometry rule that was used in the problem solving step.<br><br />
[[Image:ProblemSolvingItem.GIF]]<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
<br />
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995).<br />
Cognitive tutors: Lessons learned. The Journal of the Learning<br />
Sciences, 4 (2) 167-207.<br />
<br />
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson,<br />
D. C., & Soloway, E. (2004). A scaffolding design framework for software to support<br />
science inquiry. The Journal of the Learning Sciences, 13(3), 337-386.<br />
<br />
==== Future Plans ====</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:ProblemSolvingItem.GIF&diff=9448File:ProblemSolvingItem.GIF2009-05-18T16:38:57Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9169Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:39:12Z<p>Kirsten-Butcher: /* Future Plans */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesized that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*August 2009: Symposium presentation at EARLI 2009<br />
*July - September 2009: Prepare article and submit to journal<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9168Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:38:27Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesized that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9167Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:38:03Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9166Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:37:49Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9165Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:37:19Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
<b><i>Study 1 </b></i><br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
<b><i>Study 2</b></i><br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9164Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:36:37Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
<b><i>Study 1</b></i><br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
<b><i>Study 2</b></i><br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9163Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:35:48Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
<i><b>No Highlighting</b></i>: Following an error, no highlighting in the visual diagram is provided to students.<br><br />
[[Image:NoHighlight.jpg]]<br><br><br />
<i><b>Tutor Highlighting</b></i>: Following an error, the tutor highlighted the features of the problem diagram that are relevant to the selected geometry principle.<br />
[[Image:TutorHighlight.jpg]]<br><br><br />
<i><b>Student Highlighting</b></i>: Following an error, the student is required to highlight the relevant visual information associated with geometry principles (namely, the features in the diagram to which the selected rule applies).<br />
[[Image:StudentHighlight.jpg]]<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:TutorHighlight.jpg&diff=9162File:TutorHighlight.jpg2009-05-09T00:32:01Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:StudentHighlight.jpg&diff=9161File:StudentHighlight.jpg2009-05-09T00:31:43Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:NoHighlight.jpg&diff=9160File:NoHighlight.jpg2009-05-09T00:31:21Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9159Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:19:01Z<p>Kirsten-Butcher: /* Annotated Bibliography */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
*Butcher, K. R., & Aleven, V. A. (in press 2009). Visual self-explanation during intelligent tutoring? More than attentional focus? <i>European Association for Research on Learning and Instruction</i>, 13th Biennial Conference. August 25-29, 2009: Amsterdam, the Netherlands.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9158Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:14:47Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
[[Image:EarliGraph.jpg]]<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:EarliGraph.jpg&diff=9157File:EarliGraph.jpg2009-05-09T00:14:21Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9156Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:12:58Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9155Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:12:35Z<p>Kirsten-Butcher: /* Results */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- would best support [[robust learning]]. Although tutor highlighting was hypothesized to be better than the no highlighting (control) condition, we expected that visual-verbal coordination would be best supported by student interaction with diagrams.<br />
<br />
Overall, student progress was slower than anticipated by the experimenters or the classroom teacher. Of the 83 students working in the intelligent tutor, 31 students (11 Control, 10 Visual Highlighting, 10 Visual Cueing) reached the last instructional unit (unit 3) during the experiment. For these students, results show that benefits of visual self-explanation for problem solving change over the course of tutoring practice (see the figure below). <br />
In the first instructional unit, students provided with visual cueing by the tutor are most accurate in their problem solving answers (M = .89, SD = .05) compared to students in the control condition (M = .83, SD = .06) or the visual-explanation condition (M = .85, SD = .05). Results demonstrated an overall effect of condition (F (2, 27) = 4.01, p = .03); post-hoc Bonferroni comparisons demonstrated that visual cueing significantly outperformed the control condition (p = .03) but not the visual self-explanation condition (p = .15), which fell between the two other groups. In contrast, by unit 3, students who visually self-explained the geometry principles (M = .86, SD = .08) were most accurate in their problem-solving answers, followed by the visual cueing condition (M = .83, SD = .11), and then the control condition (M = .73, p = .10). Results again demonstrated an overall effect of condition (F(2, 27) = 4.84, p = .016); post-hoc Bonferroni comparisons showed that the control condition was outperfomed by the visual self-explanations (p = .03) and the visual cueing (p = .05) conditions. <br><br />
We analyzed overall posttest and delayed posttest results for students who had also taken the pretest. Posttest results demonstrated an overall improvement from pre- to posttest (F(1, 65) = 9.68, p = .03), but no significant condition differences (F<1). At delayed posttest, result suggested a test time (pretest vs. delayed posttest) by condition interaction (F(2. 37) = 2.87, p = .07). At delayed posttest (see Figure 2), students in the visual self-explanation condition outperformed students from the visual cueing condition and the control (interactive diagram) condition. <br><br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9154Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:08:24Z<p>Kirsten-Butcher: /* Hypotheses */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Results ===<br />
''Study 1''<br />
<br />
Although we hypothesized that coordinative support in linking verbal definitions of geometry concepts to visual examples would support the development of robust knowledge, visual only training was found to be as effective as coordinated visual-verbal training in this study. During early use of the tutor (as seen in the Concept Quiz 1 and Unit 1 Problem Solving results), the pattern of results showed an advantage for students trained in the visual-only condition. However, by posttest (see Overall True/False in the graph), students performed similarly on knowledge assessments.<br><br />
[[Image:VisVerbTrainingResults.jpg]]<br />
<br />
<br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9153Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:05:10Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.jpg]]<br />
<br><br />
[[Image:VisualVerbalTraining.jpg]]<br />
<br><br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor.<br />
<br />
<b>Study 2: Independent Variables</b><br><br />
Following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisVerbTrainingResults.jpg&diff=9152File:VisVerbTrainingResults.jpg2009-05-09T00:03:26Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisualVerbalTraining.jpg&diff=9151File:VisualVerbalTraining.jpg2009-05-09T00:03:07Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisualOnlyTraining.jpg&diff=9150File:VisualOnlyTraining.jpg2009-05-09T00:02:38Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9149Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-09T00:02:00Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
<br />
<br />
<br>As seen in this image, in the visual only training condition, visual examples and non-examples were provided to students to support self-generation of relevant verbal definitions. Students were instructed to study the visual examples and then to press "Done" to continue to the next geometry concept.<br />
<br />
<br />
<br>As seen in this image, the visual-verbal training condition required students to select the relevant text statements that appropriately defined the visual examples and non-examples. Students were required to complete each definition correctly before moving on to the next geometry concept.<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9148Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:54:00Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.gif]]<br />
<br>As seen in this image, in the visual only training condition, visual examples and non-examples were provided to students to support self-generation of relevant verbal definitions. Students were instructed to study the visual examples and then to press "Done" to continue to the next geometry concept.<br />
[[Image:VisualVerbalTraining.gif]]<br />
<br>As seen in this image, the visual-verbal training condition required students to select the relevant text statements that appropriately defined the visual examples and non-examples. Students were required to complete each definition correctly before moving on to the next geometry concept.<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9147Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:53:43Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
[[Image:VisualOnlyTraining.gif]]<br />
As seen in this image, in the visual only training condition, visual examples and non-examples were provided to students to support self-generation of relevant verbal definitions. Students were instructed to study the visual examples and then to press "Done" to continue to the next geometry concept.<br />
[[Image:VisualVerbalTraining.gif]]<br />
As seen in this image, the visual-verbal training condition required students to select the relevant text statements that appropriately defined the visual examples and non-examples. Students were required to complete each definition correctly before moving on to the next geometry concept.<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9146Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:51:09Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
<i>Visual-Only Training:</i> Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
[[Image:VisualOnlyTraining.gif]]<br />
<br />
<i>Visual-Verbal Training >/i> Students were required to select relevant text statements that appropriately defined the visual examples and non-examples <br />
[[Image:VisualVerbalTraining.gif]]<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisualVerbalTraining.gif&diff=9145File:VisualVerbalTraining.gif2009-05-08T23:50:22Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=File:VisualOnlyTraining.gif&diff=9144File:VisualOnlyTraining.gif2009-05-08T23:49:51Z<p>Kirsten-Butcher: </p>
<hr />
<div></div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9143Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:48:47Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
<i>Visual-Only Training:</i> Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
<br />
<br />
<i>Visual-Verbal Training >/i> Students were required to select relevant text statements that appropriately defined the visual examples and non-examples <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving. This study was conducted at CWCTC, beginning in late January 2008. The study curriculum was the Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we varied the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlighted relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlighted the relevant visual information following a student error, or (c) no highlighting was provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9142Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:47:09Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
<i>Visual-Only Training:</i> Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
<br />
<br />
<i>Visual-Verbal Training >/i> Students were required to select relevant text statements that appropriately defined the visual examples and non-examples <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9141Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:46:21Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br><br />
'''Visual-Only Training''': Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
<br />
<br />
(b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9140Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:46:00Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
<b>Study 1: Independent Variables</b><br />
'''Visual-Only Training''': Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
<br />
<br />
(b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9139Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:45:21Z<p>Kirsten-Butcher: /* Research questions */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice. This study was conducted at Riverview High School, in Winter 2007-2008 (testing ended in January 2008). In a two condition study, we varied the type of conceptual training that students receive before beginning problem solving activities. <br />
<br />
'''Study 1: Independent Variables'''<br />
<b>Visual-Only Training</b>: Visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions<br />
<br />
<br />
(b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9138Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:23:01Z<p>Kirsten-Butcher: /* Study 1 */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 50<br />
|-<br />
| '''Total Participant Hours''' || 200<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice.<br />
<br />
This study is being conducted at Riverview High School, in Winter 2007-2008 (ending in January 2008). In a two condition study, we vary the type of conceptual training that students receive before beginning problem solving activities. Students receive either (a) Visual-Only training, where visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions, or (b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9137Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:20:39Z<p>Kirsten-Butcher: /* Study 2 */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || Approximately 60<br />
|-<br />
| '''Total Participant Hours''' || Approximately 240<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 83<br />
|-<br />
| '''Total Participant Hours''' || 415<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice.<br />
<br />
This study is being conducted at Riverview High School, in Winter 2007-2008 (ending in January 2008). In a two condition study, we vary the type of conceptual training that students receive before beginning problem solving activities. Students receive either (a) Visual-Only training, where visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions, or (b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual_Feature_Focus_in_Geometry:_Instructional_Support_for_Visual_Coordination_During_Learning_(Butcher_%26_Aleven)&diff=9136Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)2009-05-08T23:17:55Z<p>Kirsten-Butcher: /* Study 1 */</p>
<hr />
<div>==Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning ==<br />
''Kirsten Butcher & Vincent Aleven''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || December 2007<br />
|-<br />
| '''Study End Date''' || February 2008<br />
|-<br />
| '''LearnLab Site''' || Riverview High School<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || Approximately 60<br />
|-<br />
| '''Total Participant Hours''' || Approximately 240<br />
|-<br />
| '''DataShop''' || Yes.<br />
|}<br />
<br><br />
<br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Kirsten R. Butcher & Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Thomas Bolster (Research Associate, CMU HCII), Michael Nugent, Research Programmer CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 28, 2008<br />
|-<br />
| '''Study End Date''' || March 2008<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || Approximately 90<br />
|-<br />
| '''Total Participant Hours''' || Approximately 360<br />
|-<br />
| '''DataShop''' || N/A (Study has not yet begun)<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Is [[Visual-verbal integration | visual-verbal integration]] a major source of difficulty for students learning geometry? Further, how can coordinative learning with visual and verbal [[knowledge components]] in geometry be supported by instructional events that vary the support for and type of [[sense making]] in which learners engage during problem solving? In geometry, students may have difficulty integrating visual and verbal information sources for two reasons: first, they may lack deep understanding of geometry concepts (e.g., what is an interior angle?) that are relevant to problem-solving principles (e.g., the interior angles theorem for circles); second, students may be unable to coordinate visual problem features with verbal principles during problem solving. Our research explores the [[robust learning]] effects associated with visual-verbal training of geometry features and varied levels of instructional assistance in coordinating visual diagram features with verbal geometry principles during problem solving.<br />
<br />
=== Background & Significance ===<br />
''Successful Learning is Supported by Coordinated Visual-Verbal Knowledge''<br />
<br />
Research with both experts and more novice learners has shown that integrated visual-verbal knowledge supports successful problem solving. In geometry, for example, experts use key diagram configurations to cue retrieval of relevant schemas, and these visual configurations help successfully model expert proof (Koedinger & Anderson, 1990). In mathematics, experts are more likely than novices to generate diagrams and to use these visual representations to guide their reasoning about problem-solving steps (Stylianou, 2002). <br />
<br />
Even for more novice learners, learning benefits are seen when visual and verbal information is processed jointly instead of in isolation. In geometry, superficial visual similarities between geometry diagrams can decrease a novice’s likelihood of problem-solving success because novices focus on irrelevant visual similarities at the expense of conceptual problem differences (Lovett & Anderson, 1994). Even when visualizations depict helpful (rather than misleading) information for learning, verbal explanations support deeper understanding. For example, the value of graphical feedback when using a physics simulation is greatly enhanced by the presence of short, embedded verbal explanations that focus learners on key principles (Rieber, Tzeng, & Tribble, 2004). Similarly, learners suffer when verbal information is processed alone. Visual representations that are designed to be informationally-equivalent to a given piece of text or audio nevertheless support deeper understanding of the text (Ainsworth & Loizou, 2003; Butcher, 2006) or audio explanations (e.g., Moreno & Mayer, 2002). Further, students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
The potential importance of connecting visual and verbal information also is supported by the literature on knowledge transfer following example learning, where the use of abstract rules can combat problems associated with focus on superficial similarity. Although examples often support problem solving, students frequently are unable to successfully solve transfer problems that are not superficially very similar to the trained examples (for a review, see Reeves & Weissberg, 1994). Research in reasoning and transfer has found that student performance is better supported by examples that include instruction on abstract rules when compared to learning with examples alone or instruction alone (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991). Thus, we should expect that when students connect geometry diagrams (examples) to relevant geometry principles (abstract rules), robust learning will be supported.<br />
<br />
=== Glossary ===<br />
<br />
=== Research questions ===<br />
''Study 1: Does coordinated visual-verbal training on geometry concepts prior to problem solving support learning?''<br />
<br />
This in vivo student extends our understanding of coordinative learning by addressing whether concept learning can be supported by visual-verbal coordination before problem solving practice.<br />
<br />
This study is being conducted at Riverview High School, in Winter 2007-2008 (ending in January 2008). In a two condition study, we vary the type of conceptual training that students receive before beginning problem solving activities. Students receive either (a) Visual-Only training, where visual examples and non-examples are provided to students to support self-generation of relevant verbal definitions, or (b) Visual-Verbal training, where students must select relevant text statements that appropriately define the visual examples and non-examples. <br />
<br />
<br />
''Study 2: How does coordination of visual and verbal information sources support visual feature understanding and application?''<br />
<br />
This in vivo study extends our understanding of coordinative learning by addressing whether visual-verbal coordination maximizes robust learning when coordination is tied to student errors during problem solving.<br />
<br />
This study is being conducted at CWCTC, beginning in late January 2008; the study curriculum will be Angles units of the Geometry Cognitive Tutor. In a 3 condition study, we vary the coordinative learning activities following student errors in the tutor: (a) following an error, the student highlights relevant visual information associated with geometry principles (namely, the features in the diagram to which the rule applies), (b) the tutor highlights the relevant visual information following a student error, or (c) no highlighting is provided.<br />
<br />
=== Hypotheses ===<br />
''Study 1''<br />
<br />
We hypothesize that coordinative support in linking verbal definitions of geometry concepts to visual examples will support the development of robust knowledge that supports improved problem solving and transfer. <br />
<br />
''Study 2''<br />
<br />
We hypothesize that active [[coordination]] -- where students highlight relevant diagram elements following problem-solving errors -- will best support [[robust learning]]. Although tutor highlighting should support learning better than the no highlighting (control) condition, we expect that visual-verbal coordination will be best supported by student interaction with diagrams.<br />
<br />
=== Explanation ===<br />
From a [[Coordinative Learning|Coordinative Learning Cluster]] perspective, [[coordination]] between visual and verbal information supports foundational skill building, because attending to both representations simultaneously associates [[features]] from both with the learned [[knowledge components]]. This association increases feature validity and promotes [[robust learning]].<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<br />
<br />
==== Annotated Bibliography ====<br />
<br />
*Butcher, K. R., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
<br />
<br />
<br />
<br />
====Future Plans====<br />
*January 2008: Finish study at Riverview, begin study at CWCTC<br />
*February 2008: Work with Datashop to upload Riverview data; monitor study progress at CWCTC<br />
*March 2008: Analyze data from Riverview; finish study at CWCTC<br />
*April 2008: Administer long-term retention test at CWCTC; work with Datashop to upload CWCTC data<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Contiguous_Representations_for_Robust_Learning_(Aleven_%26_Butcher)&diff=9070Contiguous Representations for Robust Learning (Aleven & Butcher)2009-04-22T16:20:50Z<p>Kirsten-Butcher: /* Annotated Bibliography */</p>
<hr />
<div>== Learning with Diagrams in Geometry: Strategic Support for Robust Learning ==<br />
''Vincent Aleven and Kirsten Butcher''<br />
<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven & Kirsten R. Butcher<br />
|-<br />
| '''Other Contributers''' || <b>Graduate Students:</b> Andy Tzou (CMU HCII)<br><br />
<b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Grace Lee Leonard (Research Associate, CMU HCII), Thomas Bolster (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 24, 2006<br />
|-<br />
| '''Study End Date''' || February 22, 2006<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 65<br />
|-<br />
| '''Total Participant Hours''' || 390<br />
|-<br />
| '''DataShop''' || Log data is uploaded and available in the DataShop<br />
|}<br />
<br><br />
====Study 2====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven & Kirsten R. Butcher<br />
|-<br />
| '''Other Contributers''' || <b>Graduate Students:</b> Andy Tzou (CMU HCII), Carl Angioli (CMU HCII), Michael Nugent (Pitt, Computer Science)<br><br />
<b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Grace Lee Leonard (Research Associate, CMU HCII), Thomas Bolster (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || April 28, 2006<br />
|-<br />
| '''Study End Date''' || May 26, 2006<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 130<br />
|-<br />
| '''Total Participant Hours''' || 780<br />
|-<br />
| '''DataShop''' || Log data is uploaded and available in the DataShop<br />
|}<br />
<br><br />
<br />
=== Abstract ===<br />
Does integration of visual and verbal knowledge during learning support deep understanding? Can student interactions with visual information during problem-solving support [[robust learning]]? The overall goal of this project is to gain a better understanding of 1) visual and verbal [[knowledge components]] in a problem-solving environment and, 2) how interacting with visual information can support the development of deep understanding. Ultimately, we are interested in [[coordination]] and [[integration]] processes in learning with visual and verbal [[knowledge components]], and how these processes may support [[robust learning]].<br />
<br />
We are using the Geometry Cognitive Tutor as a research vehicle for our project. In geometry, visual information is represented in a problem diagram and verbal/symbolic information is represented in text that contains given and goal information as well as in conceptual rules/principles of geometry. The research described here investigates whether [[implicit instruction]] (via direct interaction with visual information during learning) can support [[robust learning]] through [[Visual-verbal coordination|visual-verbal coordination]] during learning. This [[implicit instruction]] is achieved via interactive instructional events in an intelligent tutoring environment, where students receive feedback on error and perform a simple (menu-based) form of [[self-explanation]] during practice.<br />
<br />
=== Background & Significance ===<br />
In this research, we draw upon previous work in learning with [[multimedia sources]], [[self-explanation]]s, and Cognitive Tutors. We hypothesize that two key cognitive processes support integrated knowledge development and [[robust learning]] when using visual and verbal representations. These processes are: 1) Successful [[mapping]] between visual and verbal information, and 2) [[Integration]] processes that combine visual and verbal representations into integrated [[knowledge components]]. Previous research has suggested that contiguous representations -- those that provide close temporal and physical proximity between visual and verbal elements during learning -- can support understanding of multimedia materials (e.g., Mayer, 2001); these benefits have been hypothesized to result from the easing of cognitive load required for [[mapping]] between visual and verbal information. <br />
<br />
We are investigating if these benefits can be seen during real classroom learning when students engage in extended practice with learning materials. Our research examines the potential benefits of [[contiguity]] during intelligent tutoring for robust learning in classroom environments. <br />
<br />
We hypothesize that [[implicit instruction]] that supports interaction with visual information will support [[coordination]] between and [[integration]] of visual and verbal information, promoting [[robust learning]] as measured by knowledge [[retention]] and [[transfer]]. <br />
<br />
By [[coordination]], we mean the processes that support [[mapping]] between relevant visual and verbal information as well as the processes that keep relevant [[knowledge components]] active. For example, in geometry a student needs to map between text references to angles and their location in a diagram and will need to maintain the numerical (given or solved) value of that angle to use in problem solving. By [[visual-verbal integration]], we mean knowledge construction events that involve generating a representation that includes both visual and verbal knowledge components. For example, in geometry a student may need to construct an understanding of linear angles that includes both a verbal definition (e.g., “two adjacent angles that form a line”) and a visual situation description (e.g., a visual representation of the two angles formed by intersection of a line).<br />
<br />
In the context of the Geometry Cognitive Tutor, [[contiguity]] is achieved by placing related representations, such as a diagram and a workspace in which answers are entered, in close proximity that reduces (and in some cases, removes) the need for [[mapping]] between visual and verbal information. Although contiguous representations may reduce the initial cognitive load associated with [[mapping]] between representations, cognitive load demands may be less influential in classroom environments where practice is extended and distributed (Olina, Reiser, Huang, Lim, & Park, 2006). Thus, we assume that contiguous representations can support robust learning by promoting [[integration]] of visual and verbal information during practice. That is, [[contiguity]] may support students' connection between and [[integration]] of visual and verbal information leading to more robust knowledge of geometry principles. If these assumptions are true, we would expect to see similar performance on practiced problems for students who trained with [[Contiguous Representation|contiguous]] vs. noncontiguous representations. However, we would expect students using the contiguous representations to demonstrate better knowledge [[transfer]].<br />
<br />
=== Glossary ===<br />
See [[:Category:Visual-Verbal Learning (Aleven & Butcher Project)|Visual-Verbal Learning Project Glossary]]<br />
<br />
=== Research questions ===<br />
#Do [[Contiguous Representation|contiguous representations]] in geometry support students' [[Retention|retention]] and [[transfer]] of [[knowledge components]]?<br />
#Are the effects of [[Contiguous Representation|contiguous representations]] stronger for [[transfer]] than for [[retention]]?<br />
<br />
=== Dependent variables ===<br />
*Pretest, [[normal post-test]], and [[transfer]] test measuring student performance on:<br />
**Problem-solving items isomorphic to the practiced problems ([[normal post-test]])<br />
**Complex and demanding problem-solving items unlike those seen during problem practice ([[transfer]])<br />
<br />
*Log data collected during tutor use, used to assess:<br />
**Learning curves<br />
**Time on task<br />
**Error rates<br />
**Latency of responses<br />
<br />
*(Planned) Log data collected during subsequent tutor use, will use to assess:<br />
**[[Accelerated future learning]] <br />
***(Note: Not available for studies conducted in "Circles" unit of the Geometry Cognitive Tutor, since the Circles unit is completed at the end of the school year.)<br />
<br />
=== Independent Variables ===<br />
*Contiguity of Representation<br />
:''Contiguous representation (students work in diagram) vs. Non-contiguous representation (students work in separate table)''<br />
<br />
Figure 1. Noncontiguous representation: Screen shot of tutor interface.<br><br />
[[Image:Butcher_TableScreenShot2.jpg]]<br />
<br />
Figure 2. Contiguous representation: Screen shot of tutor interface.<br><br />
[[Image:Butcher_DiagramScreenShot.jpg]]<br />
<br />
=== Hypotheses ===<br />
<br />
*Contiguous representations increase strategic inferences and [[integration]] of visual and verbal [[knowledge components]] during problem-solving. The resulting [[visual-verbal integration | Visual-verbal Integration]] will support deep learning as evidenced by transfer items the require joint analysis with geometry principles and diagrams.<br />
<br />
=== Findings ===<br />
<br />
Current findings suggest that interaction with visual representations during problem-solving supports deep [[transfer]] during learning. <br />
<br />
====Study 1 (In Vivo, Geometry Cognitive Tutor) ====<br />
*Summary<br />
**In Vivo Study: 10th grade geometry classes in rural Pennsylvannia school<br />
**Domain: Angles curriculum in the Geometry Cognitive Tutor<br />
**Grade-matched pairs of students were randomly assigned to one of two conditions:<br />
***Diagram (Contiguous) Condition: Students interacted directly with geometry diagrams and accepted answers are displayed directly in the diagram<br />
***Table (Noncontiguous) Condition: Students work separate from the diagrams, in a distally located table<br />
<br />
*Findings<br />
**No overall effect of experimental condition on students' performance on geometry answers or reasons at posttest<br />
**Although working in the Diagram condition improved lower-knowledge students' explanations at posttest, higher-knowledge students performed best when working in the Table condition. The result was evidenced by a significant 3-way interaction of Test Time (Pre- vs. Posttest) X Condition (Table vs. Diagram) X Prior Knowledge (Higher vs. Lower) for students' performance on geometry rules at posttest (F(1,39) = 6.2, p < .02).<br />
<br />
====Study 2 (In Vivo, Geometry Cognitive Tutor) ====<br />
*Summary<br />
**In Vivo Study: 10th grade geometry classes in rural Pennsylvannia school<br />
**Domain: Circles curriculum in the Geometry Cognitive Tutor<br />
**Assessment was expanded to include not only answers and explanations for problem-solving items (as in Study 1), but also explanations on deep transfer items (explanations of unsolvable problems) and non-numerical reasoning items (true/false items that require students to judge whether a geometry rule is appropriate to relate named diagram elements).<br />
**Grade-matched pairs of students were randomly assigned to one of two conditions:<br />
***Diagram (Contiguous) Condition: Students interacted directly with geometry diagrams and accepted answers are displayed directly in the diagram<br />
***Table (Noncontiguous) Condition: Students work separate from the diagrams, in a distally located table<br />
<br />
*Findings<br />
**Problem-solving: No condition differences for numerical answers (F(1, 89) = 1.03, p > .3) or explanations for solvable problems (F(1, 89) <1).<br />
**Deep Transfer Explanations: There was a significant effect of condition on students' explanations of unsolvable problems (F(1, 89) = 4.1, p = .046). Students in the Diagram (Contiguous) condition explained unsolvable problems better (M = .13, SE = .03) than students in the Table (Noncontiguous) condition (M = .06, SE = .02).<br />
<br />
<br><br />
Figure 3. Mean performance on explanations for unsolvable problems by experimental condition, at pre- and posttest.<br><br />
[[Image:Butcher_UnsolvableExplanations.jpg]]<br />
<br />
*True/False items: Although there were no condition difference for performance on "true" items (F(1,89) = 2.4, p = .13), students in the Diagram (Contiguous) condition better recognized and explained false answers at posttest (F (1, 89) = 4.3, p = .04). That is, students from both conditions were equally able to recognize statements that gave valid relationships between geometry rules and diagram elements (Diagram, M = .71, SE = .04; Table, M = .72, SE = .03). However, students who interacted with diagrams during practice were better able to recognize when and explain why given geometry rules were inappropriate to relate named diagram elements (M = .23, SE = .02) than students who worked separately from diagrams during practice (M = .17, SE = .02).<br />
<br />
<br><br />
Figure 4. Mean performance on recognizing/explaining inappropriate applications of geometry rules, by experimental condition at pre- and posttest.<br />
[[Image:Butcher_FalseExplanations.jpg]]<br />
<br />
=== Explanation ===<br />
<br />
The deep [[transfer]] benefits seen in Experiment 2 suggest that contiguous representations may help students [[Integration|integrate]] visual and verbal [[knowledge components]] during learning. From a Coordinative Learning perspective, the contiguous tutor interface provides [[implicit instruction]]al support for [[coordination]] of visual-verbal knowledge during tutored problem solving. Although the same diagram (an implicit/passive form of instruction) is present in both the contiguous and the noncontiguous representations, active interaction with the diagram (an active/implicit form of instruction) supports knowledge [[transfer]] following tutored practice. Active integration may cause students to attend to both representations simultaneously and thereby better distinguish relevant from irrelevant features. Enhanced attention to both representations may facilitate a process like [[co-training]]: Through easier [[coordination]] of feature interpretations across the visual and verbal representations, the student may be more likely to prune irrelevant features (e.g., the apparent size of an angle) that may be absent or inconsistent across representations and notice relevant features (e.g., the given geometric constraints on an angle) that may be present or consistent across representations. Such instructional facilitation of [[coordination]] should increase [[feature validity]] of [[knowledge components]] and promote [[robust learning]].<br />
<br />
Although we cannot rule out the possibility that contiguous representations may support [[mapping]] between visual and verbal information in problem-solving, we see little evidence for substantial performance-based effects of mapping support on our [[normal post-test]]. All students performed equally well on trained problem-solving skills. Especially for higher-knowledge learners, interactive tutored practice may support mapping sufficiently to promote at least near-term [[retention]] of [[knowledge components]].<br />
<br />
In terms of the micro-level of the theoretical framework, the contiguous representations should reduce the effort of deep learning paths in the [[learning event space]] by supporting strategic inferences and reasoning directly with the diagram. Our data may also suggest that contiguous representations can have a learning path effect: students who are able to reason directly with diagram representations may attend more closely to the geometric features and relations to which geometry principles apply. This could impact meaningful learning by increasing [[feature validity]] of the visual and verbal [[knowledge components]].<br />
<br />
===Further Information===<br />
==== Connections ====<br />
<b>Interactive Communication as Support for Visual-Verbal Integration</b>:<br>Our research is investigating multiple methods with which student learning can be supported by interactions with pictorial information during geometry learning -- see also our work on Integrated Hints in geometry: [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]]. However, our work also includes more a more explicit method for supporting student integration visual and verbal knowledge components. This method involves interactive support for students' [[Elaborated Explanations | elaborated explanations]] during geometry learning. Research investigating this explicit support is part of the [[Interactive Communication]] Cluster: [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]<br />
<br />
<b>Visual Representations for Robust Learning in Other Domains</b>: Our efforts to support students' integration of visual and verbal knowledge are informed by and related to efforts investigating the use of visual representations to support [[robust learning]] in other domains. A closely related PSLC project is [[Visual Representations in Science Learning|Visual Representations in Science Learning (Davenport, Klahr, & Koedinger)]], in which researchers are exploring whether coordination between verbal and visual representations can help students refine initially shallow understandings into meaningful chemical concepts.<br />
<br />
==== Annotated Bibliography ====<br />
*Presentation to the PSLC Advisory Board, Fall 2006. [http://www.learnlab.org/uploads/mypslc/talks/butchercontiguity_ab2006_final_distribute.ppt Link to Powerpoint slides]<br />
*Butcher, K., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [http://www.learnlab.org/uploads/mypslc/publications/op557-butcher.pdf PDF File]<br />
*Butcher, K., & Aleven, V. (2008). Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Understanding. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1736-1741). Austin, TX: Cognitive Science Society.<br />
<br />
==== References ====<br />
*Mayer, R. E. (2001). Multimedia Learning. Cambridge, Cambridge University Press.<br />
*Olina, Z., Reiser, R., Huang, X., Lim, J., & Park, S. (2006). Problem format and presentation sequence: Effects on learning and mental effort among U.S. high school students Applied Cognitive Psychology, 20, 299-309.<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Instructional_Principles_and_Hypotheses&diff=8261Instructional Principles and Hypotheses2008-09-10T20:52:49Z<p>Kirsten-Butcher: /* Generalization Hierarchy of Principles */</p>
<hr />
<div>===Generalization Hierarchy of Principles===<br />
<br />
* [[Coordinative Learning]]<br />
** [[Example-rule coordination principle]] <br />
*** [[Worked example principle]]<br />
*** [[Prompted self-explanation hypothesis]]<br />
**** [[Corrective self-explanation]]<br />
*** [[Analogical comparison principle]]<br />
** [[Visual-verbal integration]] <br />
** [[Personalization]]<br />
<br />
* [[Interactive Communication]]<br />
** [[Prompted self-explanation principle]]<br />
<br />
* [[Refinement and Fluency]]<br />
** [[Optimized scheduling]]<br />
** [[Feature focusing]]<br />
<br />
See also [[:Category:Instructional Principle]]. Other possibilities for principles can be found further below and also at other web sites:<br />
* [http://www.edu-design-principles.org Design Principles Database] maintained by the NSF-funded [http://www.telscenter.org/ TELS (Technology Enhanced Learning in Science)] project<br />
* [http://www.psyc.memphis.edu/learning/principles/ Principles of Learning] from Lifelong Learning at Work and at Home<br />
* [http://ies.ed.gov/ncee/wwc/practiceguides/ Organizing Instruction and Study to Improve Student Learning], one of the Practice Guides of the Department of Education, Institute for Education Sciences<br />
* [http://www.cmu.edu/teaching/principles/ Principles of Teaching and Learning] from CMU's Eberly Center for Teaching and Learning<br />
<br />
===Creating Instructional Principle and Hypothesis Pages===<br />
Each instructional principle page is structured with the following headers:<br />
<br />
#Brief statement of the principle<br />
#Description of the principle<br />
##Operational definition<br />
##Examples<br />
#Experimental support<br />
##Laboratory experiment support<br />
##In vivo experiment support<br />
##Level of support (either low, medium, or high) (See the IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)<br />
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])<br />
#Conditions of application<br />
##Failed replications (which suggest conditions of application are needed)<br />
#Caveats, limitations, open issues, or dissenting views<br />
#Variations (descendants)<br />
#Generalizations (ascendants)<br />
#References<br />
<br />
If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it at least (1) has the structure above (even if all sections aren't filled in -- a template you can copy is provided further below) and (2) points to your study with a brief summary of the results. You should also (3) read the entry carefully and fill in or edit sections so they are consistent with your findings and with relevant theory. <br />
<br />
We want to keep the number of principles down, at least at the highest level of generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.<br />
<br />
Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.<br />
<br />
Please also add references to literature beyond your own work to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers you have written. By doing so, you can help others (and others can help you) identify relevant research in the field.<br />
<br />
====Template====<br />
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.<br />
<br />
<pre><br />
==Brief statement of principle==<br />
==Description of principle==<br />
===Operational definition===<br />
===Examples===<br />
==Experimental support==<br />
===Laboratory experiment support===<br />
===In vivo experiment support===<br />
===Level of support===<br />
==Theoretical rationale== <br />
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)<br />
==Conditions of application==<br />
==Caveats, limitations, open issues, or dissenting views==<br />
==Variations (descendants)==<br />
==Generalizations (ascendants)==<br />
==References==<br />
[[Category:Glossary]]<br />
[[Category:Instructional Principle]]<br />
</pre><br />
<br />
====Editing instructional principle pages====<br />
<br />
An [[:Category:Instructional Principle|instructional principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instructional principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's [[multimedia principle]] states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method is a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Use your best judgment. <br />
<br />
If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.<br />
<br />
Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have: <br />
<br />
* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.<br />
<br />
* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above. <br />
<br />
If some of the structure above does not exist, please create it.<br />
<br />
=== Candidate Instructional Principles ===<br />
<br />
The following instructional method or [[:Category:Independent Variables|independent variable]] pages are candidates that you might convert to a structured principle page. See directions on structuring a instructional principle or hypothesis page further below.<br />
<br />
* Cross-cutting all 3 clusters (move above when written as principle/hypoth page)<br />
** [[Tutoring feedback]] <br />
*** [[Peer tutoring]]<br />
<br />
* [[Coordinative Learning]] (move above when written as principle/hypoth page)<br />
**[[Visual-verbal integration]] - This has been promoted, but a page for the descendant, [[Multimedia principle]], has not yet been created.<br />
***[[Multimedia principle]]<br />
<br />
* [[Interactive Communication]] (move above when written as principle/hypoth page)<br />
**[[Collaboration]]<br />
***[[Peer tutoring]]<br />
***[[Collaboration scripts]]<br />
***[[Collaboratively observe]]<br />
**[[Vicarious learning]]<br />
**[[Deep/Reflection questions]]. (NOTE: See the "deep questioning" recommendation in [http://ies.ed.gov/ncee/wwc/practiceguides/)<br />
**[[Reflection questions]]<br />
***[[Post-practice reflection]]<br />
**[[deep-level question]]s<br />
**[[Knowledge Construction Dialogues]]<br />
**[[Prompted Self-explanation]]<br />
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).<br />
***[[Jointly constructed explanation]] - also perhaps a learning process? <br />
**[[Instructional explanation]]<br />
<br />
*[[Refinement and Fluency]] (move above when written as principle/hypoth page)<br />
**[[Feature focusing]] - This has been promoted, but the descendant, explicit instruction, is not expressed as a hypothesis or principle<br />
***[[Explicit instruction]] - Not clear this leads to a separate principle<br />
**[[Fluency Pressure]]<br />
**[[Feedback Timing]] in matrix, but not in glossary. <br />
**[[Error correction support]] <br />
**[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]<br />
<br />
* Unclassified<br />
**[[Assistance]]<br />
**[[Availability]]<br />
**[[Fading]]<br />
**[[Implicit instruction]]<br />
**[[Scaffolding]]<br />
<br />
===Learning Processes===<br />
<br />
Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.<br />
<br />
[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]<br />
<br />
A potentially different list of learning processes can be found at [[:Category:Learning Processes]].</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Prompted_self-explanation_principle&diff=8260Prompted self-explanation principle2008-09-10T20:52:34Z<p>Kirsten-Butcher: New page: == Brief statement of principle == When students are given a worked example or text to study, prompting them to self-explain each step of the worked example or each lin...</p>
<hr />
<div>== Brief statement of principle ==<br />
When students are given a [[worked examples|worked example]] or text to study, prompting them to self-explain each step of the worked example or each line of the text causes higher learning gains than having them study the material without such prompting.<br />
<br />
== Description of principle ==<br />
Many empirical studies have shown that there is a large amount of variance when it comes to individually produced [[Self-explanation|self-explanations]] (Chi et al., 1989). Some students have a natural tenancy to self-explain, while other students do little more than repeat the content of the example or expository text. The quality of the self-explanations themselves can be highly variable (Lovett, 1992; Renkl, 1997). One instructional intervention that has been shown to be effective is to prompt students to self-explain (Chi et al., 1994). [[Prompting]] can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Aleven & Koedinger, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).<br />
<br />
In the context of studying an example or reading a text, prompting for [[Self-explanation|self-explanations]] leads to greater learning gains than naturally occuring student practices.<br />
<br />
<br />
=== Operational definition ===<br />
* <b>Self-explaining</b> is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.<br />
** A <b>meta-cognitive statement</b> is an assessment, made by the student, of his or her own current understanding of the line of text or example step.<br />
** A <b>self-explanation inference</b> is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).<br />
*<b>Prompting</b> is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.<br />
<br />
=== Examples ===<br />
Here are the instructions to self-explain, taken from Chi et al. (1994):<br />
<br />
"We would like you to read each sentence out loud and then explain what it means to you. That is, what<br><br />
new information does each line provide for you, how does it relate to what you've already read, does it give<br><br />
you a new insight into your understanding of how the circulatory system works, or does it raise a question<br><br />
in your mind. Tell us whatever is going through your mind–even if it seems unimportant."<br><br />
<br />
These prompts were reworded to be used in Hausmann & VanLehn (2007):<br />
<br />
* What new information does each step provide for you?<br />
* How does it relate to what you've already seen?<br />
* Does it give you a new insight into your understanding of how to solve the problems?<br />
* Does it raise a question in your mind?<br />
<br />
These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:<br />
<br />
<Blockquote>A charged particle is in a region where there is an electric field E of magnitude<br><br />
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particle<br><br />
is -7.9 C, find the magnitude of the force on the particle P due to the electric field E.</Blockquote><br />
<br />
<br><br />
<br />
{| cellspacing="0" cellpadding="5" border="1"<br />
|+ '''An example of prompting for self-explanining'''<br />
|-<br />
| style="border-bottom: 3px solid grey;" | <br />
&nbsp; &nbsp; Now that all the given information has been entered, we need to apply<br> our knowledge of physics to solve the problem.<br><br />
<br />
&nbsp; &nbsp; One way to start is to ask ourselves, “What quantity is the problem seeking?” <br> In this case, the answer is the magnitude of the force on the particle due to <br> the electric field.<br><br />
<br />
&nbsp; &nbsp; We know that there is an electric field. If there is an electric field, <br> and there is a charged particle located in that region, then we can infer <br> that there is an electric force on the particle. The direction of the <br> electric force is in the opposite direction as the electric field because <br> the charge on the particle is negative.<br />
<br />
&nbsp; &nbsp; We use the Force tool from the vector tool bar to draw the electric force. <br> This brings up a dialog box. The force is on the particle and it is due to some <br> unspecified source. We do know, however, that the type of force is electric, so <br> we choose “electric” from the pull-down menu. For the orientation, we need to <br> add 180 degrees to 22 degrees to get a force that is in a direction that is <br> opposite of the direction of the electric field. Therefore we put 202 degrees. <br> Finally, we use “Fe” to designate this as an electric force.<br />
<br />
<center>[ PROMPT ]</center><br />
<br />
&nbsp; &nbsp; Now that the direction of the electric force has been indicated, we can work on <br>finding the magnitude. We must choose a principle that relates the magnitude <br> of the electric force to the strength of the electric field, and the charge on the <br> particle. The definition of an electric field is only equation that relates these <br> three variables. We write this equation, in the equation window.<br />
<br />
<center>[ PROMPT ]</center><br />
<br />
|}<br />
Note. PROMPT = "Please begin your self-explanation."<br />
<br />
== Experimental support ==<br />
<br />
=== Laboratory experiment support ===<br />
Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).<br />
<br />
=== In vivo experiment support ===<br />
<br />
Several in vivo experiments have leveraged laboratory work for inclusion of self-explaining in the classroom. Some in vivo experiments include:<br />
<br />
*[[Hausmann_Study|Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)]]<br />
*[[Hausmann_Study2|The effects of interaction on robust learning (Hausmann & VanLehn, 2007)]]<br />
*[[Craig_questions|Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)]]<br />
*[[Bridging_Principles_and_Examples_through_Analogy_and_Explanation|Bridging Principles and Examples through Analogy and Explanation (Nokes & VanLehn, 2007)]]<br />
<br />
== Theoretical rationale ==<br />
<br />
Prompting for self-explaining should increase the probability that a student engages in self-explaining, which includes an increase in the amount and accuracy of meta-cognitive monitoring statements and self-explanation inferences. Prompting for self-explaining is an attempt to increase the likelihood of traversing deep learning events.<br />
<br />
{| border="1" cellpadding="5" cellspacing="0"<br />
|-<br />
|Start<br />
# Process the line shallowly, e.g., paraphrasing it<br><br />
## There is nothing more to learn => Exit, with learning<br><br />
## The line is incomplete; its explanation is missing => Exit, with little learning<br><br />
# Try to process the line deeply, e.g., self-explain it<br><br />
## There is nothing missing from the line => Exit, with learning<br><br />
## The line is incomplete; its explanation is missing<br><br />
### The attempted self-explanation succeeds => Exit, with learning<br><br />
### The attempted self-explanation fails => Exit, with perhaps less learning<br><br />
|-<br />
|}<br />
<br />
== Conditions of application ==<br />
<br />
When should a prompt for self-explanation be delivered? In many of the studies described on this page, prompts for self-explanation were offered after each step of a worked-out solution. The timing of the prompt may depend on the domain. For example, in Hausmann and VanLehn (2007), the domain was physics, which requires the acquisition of procedure knowledge. The prompt to self-explain was issued after each solution step. For a more conceptual domain, such as the circulatory system, the experimenter in Chi et al. (1994) prompted the students to self-explain after reading each page of a text on the circulatory system. Roughly one line (or idea) was contained on each page of the text. After several pages, the participants became accustomed to the procedure, and turning the page became an implicit prompt for the students to begin self-explaining (Chi, personal communication).<br />
<br />
== Caveats, limitations, open issues, or dissenting views ==<br />
Examples typically precede problem solving. For example, in Sweller and Cooper (1985; Experiment 2), they asked students to study 2 examples in preparation to solve 8 problems. Similarly, Chi et al. (1989) asked students to read through 4 chapters of a physics text, which contained several examples. After studying each chapter, the students were asked to solve problems related to the content that they just studied. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied 6 examples, then solved 6 problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until all problems and examples were completed.<br />
<br />
The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.<br />
<br />
== Variations (descendants) ==<br />
[[Corrective self-explanation]]<br />
<br />
== Generalizations (ascendants) ==<br />
[[Example-rule coordination principle]]<br />
<br />
== References ==<br />
<br />
Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explain with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179. [http://dx.doi.org/10.1016/S0364-0213%2802%2900061-7]<br />
<br />
Bielaczyc, K., Pirolli, P., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221-252. [http://scholar.google.com/scholar?hl=en&client=firefox-a&rls=org.mozilla:en-US:official&hs=zUR&q=%22training+in+self-explanation+and+self-regulation+strategies:+Investigating+the+effects+of+knowledge+acquisition+activities+on+problem+solving%22&um=1&ie=UTF-8&sa=N&tab=ws]<br />
<br />
Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., &amp; LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [http://www.pitt.edu/~chi/papers/ChiBassokLewisReimannGlaser.pdf]<br />
<br />
Hausmann, R. G. M., &amp; Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [http://www.pitt.edu/~bobhaus/hausmann2002.pdf]<br />
<br />
Hausmann, R. G. M., &amp; VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger &amp; J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [http://learnlab.org/uploads/mypslc/publications/hausmannvanlehn2007_final.pdf]<br />
<br />
Lovett, M. C. (1992). Learning by problem solving versus by examples: The benefits of generating and receiving information. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (pp. 956-961). Hillsdale, NJ: Erlbaum.<br />
<br />
McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222-233. [http://www.ingentaconnect.com/content/psocpubs/brm/2004/00000036/00000002/art00007]<br />
<br />
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [http://dx.doi.org/10.1016/S0364-0213(99)80017-2]<br />
<br />
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89. [http://scholar.google.com/scholar?hl=en&lr=&client=firefox-a&cluster=16552570726007249431]<br />
<br />
Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. [http://citeseer.ist.psu.edu/rd/40331946%2C149956%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/2910/http:zSzzSzwww.aic.nrl.navy.milzSz%7EtraftonzSzpaperszSzcogsci93-exp1.pdf/the-contributions-of-studying.pdf]<br />
<br />
VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.<br />
<br />
<br />
[[Category:Glossary]]<br />
[[Category:Instructional Principle]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Instructional_Principles_and_Hypotheses&diff=8259Instructional Principles and Hypotheses2008-09-10T20:52:15Z<p>Kirsten-Butcher: /* Generalization Hierarchy of Principles */</p>
<hr />
<div>===Generalization Hierarchy of Principles===<br />
<br />
* [[Coordinative Learning]]<br />
** [[Example-rule coordination principle]] <br />
*** [[Worked example principle]]<br />
*** [[Prompted self-explanation hypothesis]]<br />
**** [[Corrective self-explanation]]<br />
*** [[Analogical comparison principle]]<br />
** [[Visual-verbal integration]] <br />
** [[Personalization]]<br />
<br />
* [[Interactive Communication]]<br />
** [[Prompted self-explanation hypothesis]]<br />
** [[Prompted self-explanation principle]]<br />
<br />
* [[Refinement and Fluency]]<br />
** [[Optimized scheduling]]<br />
** [[Feature focusing]]<br />
<br />
See also [[:Category:Instructional Principle]]. Other possibilities for principles can be found further below and also at other web sites:<br />
* [http://www.edu-design-principles.org Design Principles Database] maintained by the NSF-funded [http://www.telscenter.org/ TELS (Technology Enhanced Learning in Science)] project<br />
* [http://www.psyc.memphis.edu/learning/principles/ Principles of Learning] from Lifelong Learning at Work and at Home<br />
* [http://ies.ed.gov/ncee/wwc/practiceguides/ Organizing Instruction and Study to Improve Student Learning], one of the Practice Guides of the Department of Education, Institute for Education Sciences<br />
* [http://www.cmu.edu/teaching/principles/ Principles of Teaching and Learning] from CMU's Eberly Center for Teaching and Learning<br />
<br />
===Creating Instructional Principle and Hypothesis Pages===<br />
Each instructional principle page is structured with the following headers:<br />
<br />
#Brief statement of the principle<br />
#Description of the principle<br />
##Operational definition<br />
##Examples<br />
#Experimental support<br />
##Laboratory experiment support<br />
##In vivo experiment support<br />
##Level of support (either low, medium, or high) (See the IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] for definitions of levels of support.)<br />
#Theoretical rationale (these entries should link to one or more [[:Category:Learning Processes|learning processes]])<br />
#Conditions of application<br />
##Failed replications (which suggest conditions of application are needed)<br />
#Caveats, limitations, open issues, or dissenting views<br />
#Variations (descendants)<br />
#Generalizations (ascendants)<br />
#References<br />
<br />
If you have a study page, your hypothesis section should make reference to at least one of these instructional principle pages. You should edit your hypothesis section to be sure it points to an instructional principle page. Then you should edit that instructional principle page so that it at least (1) has the structure above (even if all sections aren't filled in -- a template you can copy is provided further below) and (2) points to your study with a brief summary of the results. You should also (3) read the entry carefully and fill in or edit sections so they are consistent with your findings and with relevant theory. <br />
<br />
We want to keep the number of principles down, at least at the highest level of generalization, so try to reference the most general instructional principle that is appropriate. In addition to facilitating our goal of greater shared vocabulary and unification, doing so will also make it so you have less editing work to do! By pointing to more general instructional principles, others will be contributing to structuring and filling in that page in addition to you. You may also point to (from your hypothesis section) more specific instructional principle pages relevant to your study.<br />
<br />
Be sure that the *Examples* and *Experimental Support* sections of the instructional principle page you point to also points back to your study page.<br />
<br />
Please also add references to literature beyond your own work to the *Reference* section of instructional principles pages you edit. You might simply copy these from your study page's reference section and/or papers you have written. By doing so, you can help others (and others can help you) identify relevant research in the field.<br />
<br />
====Template====<br />
You can copy the following into an instructional principle page you want to edit and then insert existing text into appropriate sections and add text in other sections.<br />
<br />
<pre><br />
==Brief statement of principle==<br />
==Description of principle==<br />
===Operational definition===<br />
===Examples===<br />
==Experimental support==<br />
===Laboratory experiment support===<br />
===In vivo experiment support===<br />
===Level of support===<br />
==Theoretical rationale== <br />
(These entries should link to one or more [[:Category:Learning Processes|learning processes]].)<br />
==Conditions of application==<br />
==Caveats, limitations, open issues, or dissenting views==<br />
==Variations (descendants)==<br />
==Generalizations (ascendants)==<br />
==References==<br />
[[Category:Glossary]]<br />
[[Category:Instructional Principle]]<br />
</pre><br />
<br />
====Editing instructional principle pages====<br />
<br />
An [[:Category:Instructional Principle|instructional principle]] is usually so closely related to an independent variable that it is hard to tell them apart. An instructional principle is a general hypothesis, usually about how one [[instructional method]] is better than some other baseline or control method. For example, Mayer's [[multimedia principle]] states that using diagrams in text (one instructional method) leads to better learning than text alone (another instructional method) under certain circumstances. When a study varies the instructional method, then the instruction method is a kind of [[:Category:Independent Variables|independent variable]], so in this wiki, they are usually described on independent variable wiki pages. However, an instructional principle is often so closely related to one of its independent variables/methods that the two wiki pages share considerable content. If so, then maybe it would be best to just have one page for both. Use your best judgment. <br />
<br />
If you do choose to use separate pages for an instructional principle and a related independent variable, please put "principle" or "hypothesis" in the title of the instructional principle. For instance, the [[Worked example principle]] page is different from but related to the [[worked examples]] page. The [[Prompted self-explanation hypothesis]] page is different from the [[Prompted Self-explanation]] page.<br />
<br />
Instructional principles are related to the *hypothesis* section of study pages. The hypothesis of a study may be more study- or domain-specific whereas the associated instructional principle will be study-neutral and likely more domain general. Therefore, the wiki page documenting a project or study should have: <br />
<br />
* an independent variables section that refers to the wiki pages of general independent variables. These are found in the column headers of the matrix that appears on your cluster's page.<br />
<br />
* a hypothesis section that refers to the wiki pages of general instructional principles. These instructional principles should reference the general independent variables mentioned above. <br />
<br />
If some of the structure above does not exist, please create it.<br />
<br />
=== Candidate Instructional Principles ===<br />
<br />
The following instructional method or [[:Category:Independent Variables|independent variable]] pages are candidates that you might convert to a structured principle page. See directions on structuring a instructional principle or hypothesis page further below.<br />
<br />
* Cross-cutting all 3 clusters (move above when written as principle/hypoth page)<br />
** [[Tutoring feedback]] <br />
*** [[Peer tutoring]]<br />
<br />
* [[Coordinative Learning]] (move above when written as principle/hypoth page)<br />
**[[Visual-verbal integration]] - This has been promoted, but a page for the descendant, [[Multimedia principle]], has not yet been created.<br />
***[[Multimedia principle]]<br />
<br />
* [[Interactive Communication]] (move above when written as principle/hypoth page)<br />
**[[Collaboration]]<br />
***[[Peer tutoring]]<br />
***[[Collaboration scripts]]<br />
***[[Collaboratively observe]]<br />
**[[Vicarious learning]]<br />
**[[Deep/Reflection questions]]. (NOTE: See the "deep questioning" recommendation in [http://ies.ed.gov/ncee/wwc/practiceguides/)<br />
**[[Reflection questions]]<br />
***[[Post-practice reflection]]<br />
**[[deep-level question]]s<br />
**[[Knowledge Construction Dialogues]]<br />
**[[Prompted Self-explanation]]<br />
***[[Elaborated Explanations]] - should this be a learning process (something a student does) rather than an instructional method (something instruction does)? "Prompting for X" can make a learning process into an instructional method (whether the method works or not is a separate question).<br />
***[[Jointly constructed explanation]] - also perhaps a learning process? <br />
**[[Instructional explanation]]<br />
<br />
*[[Refinement and Fluency]] (move above when written as principle/hypoth page)<br />
**[[Feature focusing]] - This has been promoted, but the descendant, explicit instruction, is not expressed as a hypothesis or principle<br />
***[[Explicit instruction]] - Not clear this leads to a separate principle<br />
**[[Fluency Pressure]]<br />
**[[Feedback Timing]] in matrix, but not in glossary. <br />
**[[Error correction support]] <br />
**[[Knowledge Accessibility]] in matrix, but not in glossary. See [[Accessibility]]<br />
<br />
* Unclassified<br />
**[[Assistance]]<br />
**[[Availability]]<br />
**[[Fading]]<br />
**[[Implicit instruction]]<br />
**[[Scaffolding]]<br />
<br />
===Learning Processes===<br />
<br />
Here's a (probably incomplete) list of learning processes with entries in the glossary. These should be used in the "theoretical rationale" section of instructional principles pages.<br />
<br />
[[Co-training]], [[Cognitive headroom]], [[Integration]], [[Refinement]], [[Sense making]], [[self-explanation]]<br />
<br />
A potentially different list of learning processes can be found at [[:Category:Learning Processes]].</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Using_Elaborated_Explanations_to_Support_Geometry_Learning_(Aleven_%26_Butcher)&diff=8033Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)2008-05-12T18:03:42Z<p>Kirsten-Butcher: /* Annotated Bibliography */</p>
<hr />
<div>== Using Elaborated Explanations to Support Geometry Learning ==<br />
''Vincent Aleven and Kirsten Butcher'' <br />
<br />
=== Summary Tables ===<br />
====Pilot Study====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven & Kirsten R. Butcher<br />
|-<br />
| '''Other Contributers''' || <b>Graduate Students:</b> Andy Tzou (CMU HCII), Carl Angioli (CMU HCII), Michael Nugent (Pitt, Computer Science)<br><br />
<b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Grace Lee Leonard (Research Associate, CMU HCII), Thomas Bolster (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January 24, 2006<br />
|-<br />
| '''Study End Date''' || January 31, 2006<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 90<br />
|-<br />
| '''Total Participant Hours''' || 90<br />
|-<br />
| '''DataShop''' || n/a (Pencil & Paper Test)<br />
|}<br />
<br />
<br><br />
====Classroom Study====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven & Kirsten R. Butcher<br />
|-<br />
| '''Other Contributers''' || <b>Graduate Students:</b> Carl Angioli (CMU HCII), Michael Nugent (Pitt, Computer Science)<br><br />
<b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Grace Lee Leonard (Research Associate, CMU HCII), Thomas Bolster (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || November 28, 2006<br />
|-<br />
| '''Study End Date''' || January 15, 2007<br />
|-<br />
| '''LearnLab Site''' || Central Westmoreland Career & Technology Center (CWCTC)<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 90<br />
|-<br />
| '''Total Participant Hours''' || 540<br />
|-<br />
| '''DataShop''' || Status: In Progress. Log files and CTAT online test files have been anonymized, passed to DataShop, and are awaiting conversion and input.<br />
|}<br />
<br />
<BR><br />
=== Abstract ===<br />
Does integration of visual and verbal knowledge during learning support deep understanding? Can interactive communication using visual and verbal [[knowledge component]]s support the development of integrated representations? In our research, we are exploring how student explanations in an intelligent tutoring environment that includes visual (pictorial) and verbal (textual) information can support [[robust learning]]. In domains like Geometry, where visual (diagram) and verbal (textual and conceptual) information is critical to deep understanding and successful problem solving, focusing student communication acts on conceptual relationships between critical [[knowledge component]]s should support deep understanding. Thus, our research is investigating explanations that link the diagram features (visual knowledge components) and geometry rules (verbal knowledge components) related to successful problem-solving steps. We call these visual-verbal explanations "[[Elaborated Explanations]]." In our studies, students give a verbally-focused explanation (by stating a relevant geometry rule) as well as a visually-focused explanation (by stating the diagram features that used to apply the stated rule). The intelligent tutor provides feedback and hints on these elaborated, verbal-visual explanations. We hypothesize that this type of interactive communication will support [[visual-verbal integration|integration of visual and verbal knowledge]] during learning, leading to better [[transfer]] and [[long-term retention]].<br />
<br />
=== Background & Significance ===<br />
<br />
A rich body of prior research has demonstrated that students develop deeper understanding of instructional materials when they [[self-explanation | self-explain]] to themselves during learning (e.g., Bielaczyc, Pirolli, & Brown, 1995; Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Chi, de Leeuw, Chiu, & LaVancher, 1994). Although much of this research has focused on students self-generated and natural language explanations, research has also shown benefits of simple, menu-based explanations in supporting learning. An existing version of the Geometry Cognitive Tutor implements student self-explanations in this simple manner. After correctly answering a geometry problem step, students must select the geometry rule or theorem that justifies their answer from a glossary menu of terms. <br />
<br />
Despite the limitations of simple, menu-based explanations, they have been shown to promote student learning in the Geometry Cognitive Tutor (Aleven & Koedinger, 2002). However, these menu-based explanations may not be supporting the development of deep, expert-like connections between conceptual geometry principles and visual features of geometry problem diagrams. Students complete these explanations after they have successfully solved a numerical problem-solving step. There there is no requirement that students make explicit connections between the rule being applied and the visual features that are required for application. Because prior work has shown that experts' geometry problem solving is driven by recognition of key diagram features that cue relevant conceptual knowledge (Koedinger & Anderson, 1990), supporting deep student understanding in geometry may require that students explicitly connect relevant conceptual knowledge to visual diagram features. <br />
<br />
Our approach is to support students' connections between and integration of visual and verbal [[knowledge component]]s through interactive communication, where students' explanations of their problem solving steps reference both verbal, conceptual knowledge (in the form of a relevant geometry rule) and its relationship to key diagram features. Thus, our question is whether explicit forms of communication that link verbal and visual knowledge components can be more successful at promoting [[robust learning]] than communication that requires expression of verbal, [[declarative]] knowledge only.<br />
<br />
=== Glossary ===<br />
See [[:Category:Visual-Verbal Learning (Aleven & Butcher Project)|Visual-Verbal Learning Project Glossary]]<br />
<br />
=== Research questions ===<br />
# Can [[Elaborated Explanations|elaborated explanations]] that connect visual (pictorial) and verbal (textual/conceptual) [[knowledge components]] support [[robust learning]] better than explanations that reference verbal information only?<br />
# Are the effects of [[Elaborated Explanations|elaborated explanations]] strongest when considering [[long-term retention]] and [[transfer]] as opposed to results on a [[normal post-test]]?<br />
<br />
=== Independent Variables ===<br />
*Type of Explanation<br />
**Simple, Textual Explanations (students state geometry principles only) <br />
<br />
Figure 1. Screen shot of Geometry Cognitive Tutor interface with simple, verbal-only explanations.<br><br />
[[Image:Diagram_SimpleExpl.jpg]]<br />
<br>vs.<br> <br />
<br />
*[[Elaborated Explanations]] (students state geometry principles and the diagram features which allow the principle to be applied)<br />
<br />
Figure 2. Screen shot of Geometry Cognitive Tutor interface with elaborated explanations<br><br />
[[Image:Diagram_ElabExpl.jpg]]<br />
<br />
=== Dependent variables ===<br />
*[[Normal post-test]], and immediate [[transfer]] test measuring student performance on:<br />
**Problem-solving items isomorphic to the practiced problems (immediate [[retention]])<br />
**Problem-solving items unlike those seen during problem practice (immediate [[transfer]])<br />
*Delayed [[long-term retention]] posttest, measuring student performance on:<br />
**Problem-solving items isomorphic to the practiced problems ([[long-term retention]])<br />
**Problem-solving items unlike those seen during problem practice (long-term [[transfer]])<br />
*Log data collected during study-related tutor use, used to assess:<br />
**[[Learning curve]]s<br />
**Time on task<br />
**Error rates<br />
**Latency of responses<br />
<br />
*(Planned): Log data collected during Cognitive Tutor completion, after study. Will be used to assess:<br />
**[[Accelerated future learning]]<br />
<br />
=== Hypothesis ===<br />
<br />
*[[Elaborated Explanations|Elaborated explanations]] promote integration of visual and verbal [[knowledge component]]s during problem-solving. Thus, students who engage in this form of interactive communication (explaining verbal and visual information, and receiving feedback on all explanations) will demonstrate deeper understanding as evidenced by improved [[transfer]] and [[long-term retention]].<br />
<br />
=== Findings ===<br />
<br />
====Pilot Study: Paper-based Difficulty Factors Analysis ====<br />
*Study Summary<br />
**<b>Participants: </b> Three 10th grade geometry classes in a rural Pennsylvannia school<br />
**<b>Design:</b> [[In vivo experiment]]: Paper-based Difficulty Factors Analysis (DFA) covering Angles content from the Geometry Cognitive Tutor<br />
**Problems varied along two dimensions:<br />
***Problem Format: Diagram vs. Table. In Diagram-format problems, students entered answers in the appropriate location within the geometry diagram. In the Table format, students entered answers in a table separate from the geometry diagram.<br />
***Explanation Type: Simple vs. [[Elaborated Explanations]]. Simple explanations required students to name only the geometry rule that justified their problem-solving steps. Elaborated explanations required students to name the geometry rule and the known diagram features that allowed them to use the stated geometry rule.<br />
**<b>Implementation:</b> Students using the standard version of the Geometry Cognitive Tutor took the paper-based DFA test midway through their completion of the Angles unit on the tutor.<br />
<br />
*Findings<br />
**Although there is an overall trend that, with no practice, students perform best in the familiar Table format that they were using during their Cognitive Tutor practice (F (1, 88) = 3.47, p = .07), the type of explanations that students provided significantly influenced performance (F (1, 88), = 6.75, p = .01). Students who gave elaborated explanations (the geometry rule <i>and</i> relevant diagram features) performed better on problem-solving (M = .42, SE = .04) than students who gave simple explanations (the geometry rule only, M = .34, SE = .04).<br />
<br />
====Classroom: Elaborated Explanations with Tutored Practice ====<br />
*Study Summary<br />
**<b>Participants: </b>Six 10th grade geometry classes in a rural Pennsylvannia school<br />
**<b>Design:</b> [[In vivo experiment]]: 2 (Simple vs. Elaborated Explanations) X 2 ([[Contiguous Representations for Robust Learning (Aleven & Butcher)|Contiguous vs. Noncontiguous tutor interface]]) <br />
**<b>Implementation:</b> Students were randomly assigned to one of the four experimental conditions. Time in tutor was held constant: students completed three classroom sessions with their assigned version of the tutor over a three week period. Immediate posttesting was conducted one week after the final tutor session. Delayed posttesting was completed after a four-week delay.<br />
<br />
*Findings<br />
**<b>Practiced Skills</b> At posttest, success with problem-solving skills practiced in the tutor (finding numerical answers and stating geometry rules) were not different for any experimental condition. These practiced results are consistent with what should be expected given mastery practice in a successful intelligent tutor.<br />
**<b>Transfer Tasks</b><br />
***<i>Solvability Judgments. </i> At posttest students who had interacted with diagrams tended to outperform students who had worked with tables in detecting solvable and unsolvable problems (F (1, 76) = 2.70, p = .10). The type of explanation completed did not affect solvability decisions (F < 1).<br />
***<i>Diagram-Rule Analysis. </i> These items require students to indicate the diagram features (typically one or two angles) that are used in the application of the geometry rule selected in their answer. Experimental conditions differed in whether or not they had practiced this skill in the tutor. Students in the geometry rule and diagram application explanation condition did practice this skill during tutoring; they selected a geometry rule and the relevant diagram features at every problem-solving step during practice and the tutor provided feedback for these explanations. However, students in the geometry rule only explanation condition received no practice or feedback in selecting diagram elements relevant to geometry rules.<p>As seen in Figure 3 below, students in the diagram interaction conditions performed better on diagram application items than students in the table interaction condition (F (1, 76) = 6.23, p = .02). This effect is driven by student performance in the geometry rule only explanation condition, where the diagram application items are a measure of transfer. Separate analysis of the geometry rule only explanation conditions shows that diagram interaction is powerful in supporting effective connections between visual representations and domain knowledge. When diagram application explanations are not required during practice, diagram interaction leads to significantly greater success linking diagrams to rules (F (1, 34) = 11.51, p = .002).<br />
<br />
Figure 3: Posttest Transfer Performance: Diagram-Rule Analysis<br />
[[Image:PosttestDiagramAnalysis_Contig3.jpg]]<br />
**<b>Delayed Posttest</b> At delayed posttest, students who worked with the interactive diagrams showed a significant advantage in the accuracy of their problem-solving answers compared to students who worked with the interactive tables (F (1, 53) = 4.03, p = .05; see Figure 4). In contrast, the type of explanation produced during practice had no effect on long-term retention of problem-solving skills.<br />
<br />
Figure 4: Delayed Posttest Problem-Solving Performance<br />
[[Image:DelayPosttest_Contig3.jpg]]<br />
<br />
=== Explanation ===<br />
Why didn’t the addition of explanations that focused on diagram elements have more impact? One possibility may be that diagram application explanations typically were the last action performed on each problem-solving step and may have been largely redundant with processing that occurred earlier (in determining the numerical answer and geometry rule for the same step). In fact, on average, students’ diagram application explanations were correct 88% of the time. More meaningful connection of visual representations may occur as students struggle to find appropriate solutions to problems, making it necessary to embed visual interaction at these earlier, critical times. <br />
<br />
It is also possible that the diagram application explanations did not prompt students to attend to relevant diagram features in the way we anticipated. Students may have focused not on diagram configurations, but on the numerical values in the diagram (or table) that they used in their calculation of the numerical answer for the same step. In current work, we are exploring the addition of diagram interaction at key opportunities for learning, by supporting student highlighting of relevant diagram elements following student errors (see [[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]]).<br />
<br />
Our results demonstrate powerful support for robust learning from diagram interaction. These findings replicate and extend results from our previous research, in demonstrating that interacting with diagrams during problem-solving promotes deeper understanding of geometry principles. (See [[Coordinative Learning]], [[Contiguous Representations for Robust Learning (Aleven & Butcher)]]).<br />
<br />
===Further Information===<br />
<br />
==== Connections ====<br />
<b>Implicit Support for Visual-Verbal Knowledge Integration</b>:<br>Our research is investigating multiple methods with which student learning can be supported by interactions with pictorial information during geometry learning. Our work also includes more implicit methods for supporting student integration visual and verbal knowledge components. These methods include [[ Contiguous Representations for Robust Learning (Aleven & Butcher) | Contiguous vs. Noncontiguous Representations]] and Integrated Hints [Link to be added].<br />
<br />
<b>Specificity in Interactive Communication</b>:<br>The addition of visually-related explanations may force students to be more <i>specific</i> in explaining how a geometry principle applies to a problem-solving step. This potential interpretation is related to the hypothesis that [[elaborative interaction]] supports learning by increasing the specificity of another person's comments. In fact, [[Hausmann Diss | Hausmann & Chi]] showed that [[elaborative interaction]] improves deep learning.<br />
<br />
<b>Application Information in Interactive Communication</b>:<br>The addition of visually-related explanations may help students by directing their attention to content that governs the application of information during problem solving. This potential interpretation is related to the hypothesis being tested by [[Ringenberg Examples-as-Help| Ringenberg & VanLehn]], that [[Completely justified example | completely justified examples]] support robust learning by demonstrating the reasoning and all application steps needed for problem-solving.<br />
<br />
==== Annotated Bibliography ====<br />
*Butcher, K.R., & Aleven, V. (in press) Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Understanding. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Cognitive Science Society. [http://www.learnlab.org/uploads/mypslc/publications/pp894-butcher.pdf Link to PDF]<br />
*Presentation to the PSLC Advisory Board, Fall 2006. [http://www.learnlab.org/uploads/mypslc/talks/butchercontiguity_ab2006_final_distribute.ppt Link to Powerpoint slides]<br />
*Presentation to the PSLC Interactive Communication Cluster, Fall 2006.<br />
<br />
==== References ====<br />
*Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. <i>Cognitive Science, 26(2),</i> 147-179.<br />
*Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. <i>Cognition & Instruction, 13,</i> 221-252.<br />
*Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. <i>Cognitive Science, 13,</i> 145-182.<br />
*Chi, M. T. H., de Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. <i>Cognitive Science, 18,</i> 439-477.<br />
*Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. <i>Cognitive Science, 14,</i> 511-550.<br />
<br />
[[Category:Study]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual-verbal_integration&diff=7585Visual-verbal integration2008-03-28T20:14:45Z<p>Kirsten-Butcher: /* Conditions of application */</p>
<hr />
<div>==Brief statement of principle==<br />
Visual-verbal integration principle: Instruction that includes both visual and verbal information leads to robust learning (the development of coherent, flexible knowledge representations) when the instruction supports learners as they coordinate information from both sources and the representations guide student attention to deep features.<br />
<br />
==Description of principle==<br />
===Operational definition===<br />
Instruction encourages students to link or coordinate visual information (e.g., diagrams) and verbal information (e.g., text) by:<br />
*Supporting direct interaction with diagrams during problem solving<br />
**For more information, see Butcher & Aleven studies: [[Contiguous Representations for Robust Learning (Aleven & Butcher)|Contiguous Representations]]; [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|Elaborated Explanations]]; [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|Integrated Hints]]<br />
*Presenting diagrams that make explicit key features of an expert mental model<br />
**For more information, see [[Visual Representations in Science Learning|Davenport et al. studies]]<br />
<br />
===Examples===<br />
In geometry, students need to connect the conceptual definition of a geometry principle (e.g., a verbal description of "Vertical Angles") with the relevant visual diagram features and configurations (e.g., the visual instantiation of "Vertical Angles" formed by two crossing lines where the angles share a common vertex but no common sides). Visual-verbal integration can be tested by having students analyze the appropriateness of geometry rules to a particular diagram.<br />
<br />
==Experimental support==<br />
===Laboratory experiment support===<br />
Prior research has shown that students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
Even relatively simple forms of coordination between visual and verbal information can impact student learning. Benefits have been found for the temporal association of visual and verbal information, where presenting visual and verbal information at the same time leads to better learning than presenting them at different times (Mayer & Anderson, 1992; Mayer, Moreno, Boire, & Vagge, 1999). Research also has identified the importance of spatial association, where learning is supported by placing visual and verbal materials in close physical proximity or integrating them into a single, combined representation (Hegarty & Just, 1993; Mayer, 1989; Moreno & Mayer, 1999).<br />
<br />
===In vivo experiment support===<br />
Butcher and Aleven's (2007; submitted) in vivo research has demonstrated that the addition of interactive diagrams to an intelligent tutor in geometry supports deep understanding of geometry principles and long-term retention of problem-solving skills. The interactive diagrams were designed as a method to support visual-verbal integration; they allow students to work directly with the diagrams during problem solving. Results show that students who used the interactive diagrams are better able to work with new diagrams and geometry principles to 1) explain when and why geometry principles are inappropriately applied to a diagram, and 2) to explain how unsolvable problems could be made solvable. For more details on these studies, please see [[Contiguous Representations for Robust Learning (Aleven & Butcher)]] and [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]].<br />
[[Image:Butcher_UnsolvableExplanations2.gif]]<br />
[[Image:Butcher_FalseExplanations2.gif]]<br />
<br />
Butcher and Aleven also have been studying scaffolds that directly connect relevant visual and verbal information. Results of these studies are ongoing; for more information, please see [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]] and [[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]].<br />
<br />
Davenport et al. (2007, 2008) tested the role of visual-verbal integrate in chemistry instruction and found that instruction that includes diagrams and text only leads to learning gains when the representations are clearly aligned with an expert mental model. A knowledge decomposition of chemical equilibrium (informed by Lab Studies #1 and #2) as well as discussions with our Chemistry working group revealed that a key knowledge component of "progress of reaction" is left implicit in many types of traditional chemistry instruction. In one study two sets of online lectures were created by Prof. Yaron to determine if instruction that uses multiple representations to convey the notion of progress of reaction would lead to more robust learning of chemistry concepts. Traditional instruction described equilibrium using chemical notations and text, the New instruction described equilibrium using molecular diagrams depicting the progress of reaction. [[Transfer]] measures of open-ended responses and conceptual multiple choice questions were collected and revealed that diagrams that were aligned with the progress of reaction framework increased learning, particularly for low knowledge students. For more information about additional studies see: [[Visual Representations in Science Learning|Visual Representations in Science Learning, Davenport, Klahr & Koedinger]].<br />
<br />
[[Image:Text dia low.gif]]<br />
<br />
==Theoretical rationale== <br />
One proposed theoretical rationale for visual-verbal coordination benefits is that temporal and spatial coordination reduces the cognitive load demands associated with working memory maintenance and visual search (Mayer, 2001). The reduction in cognitive effort needed to find and maintain multiple sources of information allows students to engage in deeper processing.<br />
<br />
However, reducing cognitive load in and of itself does not mean that students will engage in [[:Category:Learning Processes|robust learning processes]]. Another interpretation of the learning benefits found when materials support connections between visual and verbal representations is that these materials prompt students to engage in cognitive processing that integrates visual and verbal information with existing knowledge representations. That is, support for visual-verbal integration prompts student to engage in [[Active Processing|active processes]] that support deep understanding, such as [[Self-explanation|self-explanation]] or other [[Sense making|sense-making]] processes. Previous research has found that adding diagrams to a text increases the number of correct inferences that integrate to-be-learned information (Butcher, 2006).<br />
<br />
==Conditions of application==<br />
Instruction that promotes Visual-verbal integration will only be successful if students actively process information from both the pictures and text and if the informational content of pictures and text are clearly aligned with instructional objectives.<br />
<br />
*Visual Representations Must Target Deep Features<br />
**In a laboratory study, Davenport et al. [[Visual Representations in Science Learning|wiki page]] found that pictures that were not aligned to an expert model of equilibrium processes did not support learning beyond text alone.<br />
<br />
*Visual-Verbal Information Should be Actively Integrated<br />
**Butcher & Aleven [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|wiki page]] found that adding explanations that linked geometry principles to diagram features did not improve learning beyond direct interaction with diagrams. Log data analysis suggests that the visually-related explanations may not have been actively processed, especially when students were already working with the diagrams.<br />
<br />
*Format is Less Important than Content<br />
**Visual representations in a variety of formats can support learning, as long as the informational content is relevant and consistent [[Static vs. Animated Visual Representations for Science Learning (Kaye, Small, Butcher, & Chi)]]<br />
<br />
==Caveats, limitations, open issues, or dissenting views==<br />
<br />
==Variations (descendants)==<br />
<br />
==Generalizations (ascendants)==<br />
<br />
==References==<br />
Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14, 325-341.<br />
<br />
Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182-197.<br />
<br />
Butcher, K., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [http://www.learnlab.org/uploads/mypslc/publications/op557-butcher.pdf PDF File]<br />
<br />
Butcher, K., & Aleven, V. (submitted). Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Transfer. Submitted to CogSci 2008. [http://www.learnlab.org/uploads/mypslc/publications/butcheraleven_cogsci2008submitted.pdf Link to PDF]<br />
<br />
Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. (2008). When do diagrams enhance learning? A framework for designing relevant representations. Paper accepted for the 2008 International Conference of the Learning Sciences, June 2008. [http://learnlab.org/uploads/mypslc/publications/davenporticls08final.pdf download]<br />
<br />
Davenport, J.L., Klahr, D. & Koedinger (2007). The influence of diagrams on chemistry learning. Paper presented at the 12th Biennial Conference of the European Association for Research on Learning and Instruction. August 2007. [http://www.learnlab.org/uploads/mypslc/publications/davenportearli07.pdf download] <br />
<br />
Hegarty, M. & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32, 717-742.<br />
<br />
Mayer, R. E. (1989). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240-246.<br />
<br />
Mayer, R. E. (2001). Multimedia learning. Cambridge: Cambridge University Press.<br />
<br />
Mayer, R. E. & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444-452.<br />
<br />
Mayer, R. E., Moreno, R., Boire, M. & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638-643.<br />
<br />
Moreno, R. & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358-368.<br />
<br />
Van Meter, P. (2001). Drawing construction as a strategy for learning from text. Journal of Educational Psychology, 93(1), 129-140.<br />
<br />
Van Meter, P., Aleksic, M., Schwartz, A., & Garner, J. (2006). Learner-generated drawing as a strategy for learning from content area text. Contemporary Educational Psychology, 31, 142-166.<br />
<br />
<br />
[[Category:Glossary]]<br />
[[Category:Instructional Principle]]<br />
<br />
See also [[integration]] and [[coordination]].<br />
<br />
[[Category:Glossary]]<br />
<br />
[[Category:Independent Variables]]<br />
<br />
[[Category:Visual-Verbal Learning (Aleven & Butcher Project)]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual-verbal_integration&diff=7584Visual-verbal integration2008-03-28T20:08:50Z<p>Kirsten-Butcher: /* Conditions of application */</p>
<hr />
<div>==Brief statement of principle==<br />
Visual-verbal integration principle: Instruction that includes both visual and verbal information leads to robust learning (the development of coherent, flexible knowledge representations) when the instruction supports learners as they coordinate information from both sources and the representations guide student attention to deep features.<br />
<br />
==Description of principle==<br />
===Operational definition===<br />
Instruction encourages students to link or coordinate visual information (e.g., diagrams) and verbal information (e.g., text) by:<br />
*Supporting direct interaction with diagrams during problem solving<br />
**For more information, see Butcher & Aleven studies: [[Contiguous Representations for Robust Learning (Aleven & Butcher)|Contiguous Representations]]; [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|Elaborated Explanations]]; [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|Integrated Hints]]<br />
*Presenting diagrams that make explicit key features of an expert mental model<br />
**For more information, see [[Visual Representations in Science Learning|Davenport et al. studies]]<br />
<br />
===Examples===<br />
In geometry, students need to connect the conceptual definition of a geometry principle (e.g., a verbal description of "Vertical Angles") with the relevant visual diagram features and configurations (e.g., the visual instantiation of "Vertical Angles" formed by two crossing lines where the angles share a common vertex but no common sides). Visual-verbal integration can be tested by having students analyze the appropriateness of geometry rules to a particular diagram.<br />
<br />
==Experimental support==<br />
===Laboratory experiment support===<br />
Prior research has shown that students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
Even relatively simple forms of coordination between visual and verbal information can impact student learning. Benefits have been found for the temporal association of visual and verbal information, where presenting visual and verbal information at the same time leads to better learning than presenting them at different times (Mayer & Anderson, 1992; Mayer, Moreno, Boire, & Vagge, 1999). Research also has identified the importance of spatial association, where learning is supported by placing visual and verbal materials in close physical proximity or integrating them into a single, combined representation (Hegarty & Just, 1993; Mayer, 1989; Moreno & Mayer, 1999).<br />
<br />
===In vivo experiment support===<br />
Butcher and Aleven's (2007; submitted) in vivo research has demonstrated that the addition of interactive diagrams to an intelligent tutor in geometry supports deep understanding of geometry principles and long-term retention of problem-solving skills. The interactive diagrams were designed as a method to support visual-verbal integration; they allow students to work directly with the diagrams during problem solving. Results show that students who used the interactive diagrams are better able to work with new diagrams and geometry principles to 1) explain when and why geometry principles are inappropriately applied to a diagram, and 2) to explain how unsolvable problems could be made solvable. For more details on these studies, please see [[Contiguous Representations for Robust Learning (Aleven & Butcher)]] and [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]].<br />
[[Image:Butcher_UnsolvableExplanations2.gif]]<br />
[[Image:Butcher_FalseExplanations2.gif]]<br />
<br />
Butcher and Aleven also have been studying scaffolds that directly connect relevant visual and verbal information. Results of these studies are ongoing; for more information, please see [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]] and [[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]].<br />
<br />
Davenport et al. (2007, 2008) tested the role of visual-verbal integrate in chemistry instruction and found that instruction that includes diagrams and text only leads to learning gains when the representations are clearly aligned with an expert mental model. A knowledge decomposition of chemical equilibrium (informed by Lab Studies #1 and #2) as well as discussions with our Chemistry working group revealed that a key knowledge component of "progress of reaction" is left implicit in many types of traditional chemistry instruction. In one study two sets of online lectures were created by Prof. Yaron to determine if instruction that uses multiple representations to convey the notion of progress of reaction would lead to more robust learning of chemistry concepts. Traditional instruction described equilibrium using chemical notations and text, the New instruction described equilibrium using molecular diagrams depicting the progress of reaction. [[Transfer]] measures of open-ended responses and conceptual multiple choice questions were collected and revealed that diagrams that were aligned with the progress of reaction framework increased learning, particularly for low knowledge students. For more information about additional studies see: [[Visual Representations in Science Learning|Visual Representations in Science Learning, Davenport, Klahr & Koedinger]].<br />
<br />
[[Image:Text dia low.gif]]<br />
<br />
==Theoretical rationale== <br />
One proposed theoretical rationale for visual-verbal coordination benefits is that temporal and spatial coordination reduces the cognitive load demands associated with working memory maintenance and visual search (Mayer, 2001). The reduction in cognitive effort needed to find and maintain multiple sources of information allows students to engage in deeper processing.<br />
<br />
However, reducing cognitive load in and of itself does not mean that students will engage in [[:Category:Learning Processes|robust learning processes]]. Another interpretation of the learning benefits found when materials support connections between visual and verbal representations is that these materials prompt students to engage in cognitive processing that integrates visual and verbal information with existing knowledge representations. That is, support for visual-verbal integration prompts student to engage in [[Active Processing|active processes]] that support deep understanding, such as [[Self-explanation|self-explanation]] or other [[Sense making|sense-making]] processes. Previous research has found that adding diagrams to a text increases the number of correct inferences that integrate to-be-learned information (Butcher, 2006).<br />
<br />
==Conditions of application==<br />
Instruction that promotes Visual-verbal integration will only be successful if students actively process information from both the pictures and text and if the informational content of pictures and text are clearly aligned with instructional objectives.<br />
<br />
*Visual Representations Must Target Deep Features<br />
**In a laboratory study, Davenport et al. [[Visual Representations in Science Learning|wiki page]] found that pictures that were not aligned to an expert model of equilibrium processes did not support learning beyond text alone.<br />
<br />
*Visual-Verbal Information Should be Actively Integrated<br />
**Butcher & Aleven [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|wiki page]] found that adding explanations that linked geometry principles to diagram features did not improve learning beyond direct interaction with diagrams. Log data analysis suggests that the visually-related explanations may not have been actively processed, especially when students were already working with the diagrams.<br />
<br />
==Caveats, limitations, open issues, or dissenting views==<br />
<br />
==Variations (descendants)==<br />
<br />
==Generalizations (ascendants)==<br />
<br />
==References==<br />
Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14, 325-341.<br />
<br />
Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182-197.<br />
<br />
Butcher, K., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [http://www.learnlab.org/uploads/mypslc/publications/op557-butcher.pdf PDF File]<br />
<br />
Butcher, K., & Aleven, V. (submitted). Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Transfer. Submitted to CogSci 2008. [http://www.learnlab.org/uploads/mypslc/publications/butcheraleven_cogsci2008submitted.pdf Link to PDF]<br />
<br />
Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. (2008). When do diagrams enhance learning? A framework for designing relevant representations. Paper accepted for the 2008 International Conference of the Learning Sciences, June 2008. [http://learnlab.org/uploads/mypslc/publications/davenporticls08final.pdf download]<br />
<br />
Davenport, J.L., Klahr, D. & Koedinger (2007). The influence of diagrams on chemistry learning. Paper presented at the 12th Biennial Conference of the European Association for Research on Learning and Instruction. August 2007. [http://www.learnlab.org/uploads/mypslc/publications/davenportearli07.pdf download] <br />
<br />
Hegarty, M. & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32, 717-742.<br />
<br />
Mayer, R. E. (1989). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240-246.<br />
<br />
Mayer, R. E. (2001). Multimedia learning. Cambridge: Cambridge University Press.<br />
<br />
Mayer, R. E. & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444-452.<br />
<br />
Mayer, R. E., Moreno, R., Boire, M. & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638-643.<br />
<br />
Moreno, R. & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358-368.<br />
<br />
Van Meter, P. (2001). Drawing construction as a strategy for learning from text. Journal of Educational Psychology, 93(1), 129-140.<br />
<br />
Van Meter, P., Aleksic, M., Schwartz, A., & Garner, J. (2006). Learner-generated drawing as a strategy for learning from content area text. Contemporary Educational Psychology, 31, 142-166.<br />
<br />
<br />
[[Category:Glossary]]<br />
[[Category:Instructional Principle]]<br />
<br />
See also [[integration]] and [[coordination]].<br />
<br />
[[Category:Glossary]]<br />
<br />
[[Category:Independent Variables]]<br />
<br />
[[Category:Visual-Verbal Learning (Aleven & Butcher Project)]]</div>Kirsten-Butcherhttps://learnlab.org/wiki/index.php?title=Visual-verbal_integration&diff=7583Visual-verbal integration2008-03-28T20:07:51Z<p>Kirsten-Butcher: /* Conditions of application */</p>
<hr />
<div>==Brief statement of principle==<br />
Visual-verbal integration principle: Instruction that includes both visual and verbal information leads to robust learning (the development of coherent, flexible knowledge representations) when the instruction supports learners as they coordinate information from both sources and the representations guide student attention to deep features.<br />
<br />
==Description of principle==<br />
===Operational definition===<br />
Instruction encourages students to link or coordinate visual information (e.g., diagrams) and verbal information (e.g., text) by:<br />
*Supporting direct interaction with diagrams during problem solving<br />
**For more information, see Butcher & Aleven studies: [[Contiguous Representations for Robust Learning (Aleven & Butcher)|Contiguous Representations]]; [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|Elaborated Explanations]]; [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|Integrated Hints]]<br />
*Presenting diagrams that make explicit key features of an expert mental model<br />
**For more information, see [[Visual Representations in Science Learning|Davenport et al. studies]]<br />
<br />
===Examples===<br />
In geometry, students need to connect the conceptual definition of a geometry principle (e.g., a verbal description of "Vertical Angles") with the relevant visual diagram features and configurations (e.g., the visual instantiation of "Vertical Angles" formed by two crossing lines where the angles share a common vertex but no common sides). Visual-verbal integration can be tested by having students analyze the appropriateness of geometry rules to a particular diagram.<br />
<br />
==Experimental support==<br />
===Laboratory experiment support===<br />
Prior research has shown that students benefit from activities that coordinate both visual and verbal sources; these activities include verbal comparison of self-generated and ideal diagrams (Van Meter, 2001; Van Meter, Aleksic, Schwartz, & Garner, 2006) as well as dragging and dropping verbal information into a diagram to create an integrated representation (Bodemer, Ploetzner, Feuerlein, & Spada, 2004).<br />
<br />
Even relatively simple forms of coordination between visual and verbal information can impact student learning. Benefits have been found for the temporal association of visual and verbal information, where presenting visual and verbal information at the same time leads to better learning than presenting them at different times (Mayer & Anderson, 1992; Mayer, Moreno, Boire, & Vagge, 1999). Research also has identified the importance of spatial association, where learning is supported by placing visual and verbal materials in close physical proximity or integrating them into a single, combined representation (Hegarty & Just, 1993; Mayer, 1989; Moreno & Mayer, 1999).<br />
<br />
===In vivo experiment support===<br />
Butcher and Aleven's (2007; submitted) in vivo research has demonstrated that the addition of interactive diagrams to an intelligent tutor in geometry supports deep understanding of geometry principles and long-term retention of problem-solving skills. The interactive diagrams were designed as a method to support visual-verbal integration; they allow students to work directly with the diagrams during problem solving. Results show that students who used the interactive diagrams are better able to work with new diagrams and geometry principles to 1) explain when and why geometry principles are inappropriately applied to a diagram, and 2) to explain how unsolvable problems could be made solvable. For more details on these studies, please see [[Contiguous Representations for Robust Learning (Aleven & Butcher)]] and [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]].<br />
[[Image:Butcher_UnsolvableExplanations2.gif]]<br />
[[Image:Butcher_FalseExplanations2.gif]]<br />
<br />
Butcher and Aleven also have been studying scaffolds that directly connect relevant visual and verbal information. Results of these studies are ongoing; for more information, please see [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]] and [[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]].<br />
<br />
Davenport et al. (2007, 2008) tested the role of visual-verbal integrate in chemistry instruction and found that instruction that includes diagrams and text only leads to learning gains when the representations are clearly aligned with an expert mental model. A knowledge decomposition of chemical equilibrium (informed by Lab Studies #1 and #2) as well as discussions with our Chemistry working group revealed that a key knowledge component of "progress of reaction" is left implicit in many types of traditional chemistry instruction. In one study two sets of online lectures were created by Prof. Yaron to determine if instruction that uses multiple representations to convey the notion of progress of reaction would lead to more robust learning of chemistry concepts. Traditional instruction described equilibrium using chemical notations and text, the New instruction described equilibrium using molecular diagrams depicting the progress of reaction. [[Transfer]] measures of open-ended responses and conceptual multiple choice questions were collected and revealed that diagrams that were aligned with the progress of reaction framework increased learning, particularly for low knowledge students. For more information about additional studies see: [[Visual Representations in Science Learning|Visual Representations in Science Learning, Davenport, Klahr & Koedinger]].<br />
<br />
[[Image:Text dia low.gif]]<br />
<br />
==Theoretical rationale== <br />
One proposed theoretical rationale for visual-verbal coordination benefits is that temporal and spatial coordination reduces the cognitive load demands associated with working memory maintenance and visual search (Mayer, 2001). The reduction in cognitive effort needed to find and maintain multiple sources of information allows students to engage in deeper processing.<br />
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However, reducing cognitive load in and of itself does not mean that students will engage in [[:Category:Learning Processes|robust learning processes]]. Another interpretation of the learning benefits found when materials support connections between visual and verbal representations is that these materials prompt students to engage in cognitive processing that integrates visual and verbal information with existing knowledge representations. That is, support for visual-verbal integration prompts student to engage in [[Active Processing|active processes]] that support deep understanding, such as [[Self-explanation|self-explanation]] or other [[Sense making|sense-making]] processes. Previous research has found that adding diagrams to a text increases the number of correct inferences that integrate to-be-learned information (Butcher, 2006).<br />
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==Conditions of application==<br />
Instruction that promotes Visual-verbal integration will only be successful if students actively process information from both the pictures and text and if the informational content of pictures and text are clearly aligned with instructional objectives.<br />
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*Visual Representations Must Target Deep Features<br />
**In a laboratory study, Davenport et al. [[Visual Representations in Science Learning|wiki page]] found that pictures that were not aligned to an expert model of equilibrium processes did not support learning beyond text alone.<br />
:[[Image:CMUabtut.jpg]]<br />
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*Visual-Verbal Information Should be Actively Integrated<br />
**Butcher & Aleven [[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)|wiki page]] found that adding explanations that linked geometry principles to diagram features did not improve learning beyond direct interaction with diagrams. Log data analysis suggests that the visually-related explanations may not have been actively processed, especially when students were already working with the diagrams.<br />
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==Caveats, limitations, open issues, or dissenting views==<br />
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==Variations (descendants)==<br />
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==Generalizations (ascendants)==<br />
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==References==<br />
Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14, 325-341.<br />
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Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182-197.<br />
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Butcher, K., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 137-142). Austin, TX: Cognitive Science Society. [http://www.learnlab.org/uploads/mypslc/publications/op557-butcher.pdf PDF File]<br />
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Butcher, K., & Aleven, V. (submitted). Diagram Interaction during Intelligent Tutoring in Geometry: Support for Knowledge Retention and Deep Transfer. Submitted to CogSci 2008. [http://www.learnlab.org/uploads/mypslc/publications/butcheraleven_cogsci2008submitted.pdf Link to PDF]<br />
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Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. (2008). When do diagrams enhance learning? A framework for designing relevant representations. Paper accepted for the 2008 International Conference of the Learning Sciences, June 2008. [http://learnlab.org/uploads/mypslc/publications/davenporticls08final.pdf download]<br />
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Davenport, J.L., Klahr, D. & Koedinger (2007). The influence of diagrams on chemistry learning. Paper presented at the 12th Biennial Conference of the European Association for Research on Learning and Instruction. August 2007. [http://www.learnlab.org/uploads/mypslc/publications/davenportearli07.pdf download] <br />
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Hegarty, M. & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32, 717-742.<br />
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Mayer, R. E. (1989). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240-246.<br />
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Mayer, R. E. (2001). Multimedia learning. Cambridge: Cambridge University Press.<br />
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Mayer, R. E. & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444-452.<br />
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Mayer, R. E., Moreno, R., Boire, M. & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638-643.<br />
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Moreno, R. & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358-368.<br />
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Van Meter, P. (2001). Drawing construction as a strategy for learning from text. Journal of Educational Psychology, 93(1), 129-140.<br />
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Van Meter, P., Aleksic, M., Schwartz, A., & Garner, J. (2006). Learner-generated drawing as a strategy for learning from content area text. Contemporary Educational Psychology, 31, 142-166.<br />
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[[Category:Glossary]]<br />
[[Category:Instructional Principle]]<br />
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See also [[integration]] and [[coordination]].<br />
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[[Category:Glossary]]<br />
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[[Category:Independent Variables]]<br />
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[[Category:Visual-Verbal Learning (Aleven & Butcher Project)]]</div>Kirsten-Butcher