Geometry Greatest Hits

Geometry Greatest Hits

Summary Table

Study 1

PIs	Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden
Other Contributers	Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)
Study Start Date	January, 2009
Study End Date	March, 2009
LearnLab Site	Greenville, Riverview, Steel Valley
LearnLab Course	Geometry
Number of Students	98
Total Participant Hours
DataShop	Log data soon to be uploaded and available in the DataShop

Abstract

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), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:

• Visual-verbal integration principle
• Worked example principle
• Prompted self-explanation principle
• Accurate knowledge decomposition principle (part of the complete and efficient practice principle)
• Accurate knowledge estimates principle (part of the complete and efficient practice principle)

Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five 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.

Background & Significance

PSLC’s in vivo methodology 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. Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.

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.

Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction. Can principle-oriented research make them even more effective? Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies? And will such a combination lead to a large effect size?

Completed experiments

• 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.

Planned experiments

• Lab study (2 phases):
  • (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).
  • (2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.

Independent variables

The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research

• integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)
• non-interactive conceptual example sets at the beginning of each curricular unit
• interactive worked examples at the beginning of each curricular, faded in an individualized manner
• diagrammatic self-explanations of incorrect steps
• tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)
• 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)

Results

Not yet available.