Difference between revisions of "Enhancing Learning through Computer Animation"

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====Study 1====
 
====Study 1====
 
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
 
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"
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| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden
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| '''PIs''' || Stephen Reed, Albert Corbett, Bob Hoffman
 
|-
 
|-
 
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Ben MacLaren (Research Programmer, CMU HCII), Angela Wagner (Research Associate, CMU HCII)
 
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Ben MacLaren (Research Programmer, CMU HCII), Angela Wagner (Research Associate, CMU HCII)
  
 
|-
 
|-
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| '''Study Start Date''' || January, 2009
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| '''Study Start Date''' || January, 2008
 
|-
 
|-
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| '''Study End Date''' || March, 2009
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| '''Study End Date''' || December, 2008
 
|-
 
|-
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| '''LearnLab Site''' || Greenville, Riverview, Steel Valley
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| '''LearnLab Site''' || Central Westmoreland, Riverview, Saltsburg
 
|-
 
|-
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| '''LearnLab Course''' || Geometry
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| '''LearnLab Course''' || Algebra II
 
|-
 
|-
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| '''Number of Students''' ||  
+
| '''Number of Students''' || 126
 
|-
 
|-
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| '''Total Participant Hours''' ||  
+
| '''Total Participant Hours''' || 504
 
|-
 
|-
 
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop
 
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop
 
|}
 
|}
 
=== Abstract ===
 
=== Abstract ===
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The main idea in the current project is to combine instructional interventions derived from five 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). This project will test this hypothesis, focusing on the following five principles:
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This project investigates the possible combined strengths of graphically-oriented (Animation Tutor) and procedurally-oriented (Cognitive Tutor) instructional software. Students in Algebra II Cognitive Tutor classrooms are randomly assigned to one of four instructional groups on constructing equations for mixture problems. Three of the instructional groups study worked examples in which they received a verbal explanation of the solution with quantities represented by either (1) a table of values, (2) a static bar graph, or (3) a dynamic bar graph linked to the equation. The fourth group solve the same example problems using the Algebra Cognitive Tutor.
 +
Each of the example problems are followed by a test problem on the Algebra Cognitive Tutor that serves to both evaluate the different instructional conditions and provide additional opportunities for learning. Students enter quantities into a table and use these quantities (following feedback) to construct an equation to represent the problem. Two days of instruction are followed a week later, by (1) a Model Analysis Cognitive Tutor activity in which students are tested (with feedback) on their ability to construct equations for different problem structures and interpret the meaning of the terms in the equations; and (2) a paper-and-pencil test to measure retention and transfer. This variety of evaluation measures will help in identify how the different instructional formats help students learn the various knowledge components needed to construct equations for problems represented by general linear models.
  
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• Visual-verbal integration principle
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An extended summary of the study design is in this pdf ...[[Media:ReedHoffmanCorbettWorkExSummary.pdf]]
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• Worked example principle
 
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• Prompted self-explanation principle
 
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• Accurate knowledge decomposition principle
 
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(part of the complete and efficient practice principle)
 
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• Accurate knowledge estimates principle
 
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(part of the complete and efficient practice principle)
 
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Building on our prior work that tested these principles individually, we will create a new version of the Geometry Cognitive Tutor that implements these five principles. We will use this new version to conduct both a lab experiment and an in vivo experiment to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor.
 
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Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, 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. Further, the project, if successful, will demonstrate that the studied combination of principles leads to dramatically greater effectiveness of one particular intelligent tutoring system. Since these principles are drawn from PSLC theory and research evidence, the successful combination of principles has important implications for the development of PSLC theory, and inference about its eventual impact on learning outcomes. From a theoretical perspective, individual instructional design principles are a convenient way of stating theory; findings related to synergy (or lack thereof) of individual principles impose constraints on the theoretical rationale of each. Thus, this project contributes to both the applied and the theoretical missions of the PSLC.
 
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A summary of the project is in this pdf ...[[Media:ReedHoffmanCorbettWorkExSummary.pdf]]
 
  
 
=== Background & Significance ===
 
=== Background & Significance ===
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===Planned experiments===
 
===Planned experiments===
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* Lab study (2 phases):
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*  
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**(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).
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**
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**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.
 
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* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.
 
  
 
=== Hypotheses ===
 
=== Hypotheses ===

Latest revision as of 20:55, 19 May 2009

Summary Table

Study 1

PIs Stephen Reed, Albert Corbett, Bob Hoffman
Other Contributers Research Programmers/Associates: Ben MacLaren (Research Programmer, CMU HCII), Angela Wagner (Research Associate, CMU HCII)
Study Start Date January, 2008
Study End Date December, 2008
LearnLab Site Central Westmoreland, Riverview, Saltsburg
LearnLab Course Algebra II
Number of Students 126
Total Participant Hours 504
DataShop Log data soon to be uploaded and available in the DataShop

Abstract

This project investigates the possible combined strengths of graphically-oriented (Animation Tutor) and procedurally-oriented (Cognitive Tutor) instructional software. Students in Algebra II Cognitive Tutor classrooms are randomly assigned to one of four instructional groups on constructing equations for mixture problems. Three of the instructional groups study worked examples in which they received a verbal explanation of the solution with quantities represented by either (1) a table of values, (2) a static bar graph, or (3) a dynamic bar graph linked to the equation. The fourth group solve the same example problems using the Algebra Cognitive Tutor. Each of the example problems are followed by a test problem on the Algebra Cognitive Tutor that serves to both evaluate the different instructional conditions and provide additional opportunities for learning. Students enter quantities into a table and use these quantities (following feedback) to construct an equation to represent the problem. Two days of instruction are followed a week later, by (1) a Model Analysis Cognitive Tutor activity in which students are tested (with feedback) on their ability to construct equations for different problem structures and interpret the meaning of the terms in the equations; and (2) a paper-and-pencil test to measure retention and transfer. This variety of evaluation measures will help in identify how the different instructional formats help students learn the various knowledge components needed to construct equations for problems represented by general linear models.

An extended summary of the study design is in this pdf ...Media:ReedHoffmanCorbettWorkExSummary.pdf

Background & Significance

Glossary

Research questions

Planned experiments

Hypotheses

Explanation

Further Information

Connections

Annotated Bibliography

References

Future Plans

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