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Robust Learning in Visual/Verbal Problem Solving: Contiguity, Integrated Hints, and Elaborated Explanations. Vincent Aleven & Kirsten Butcher. Multiple Domains Involve Learning with Visual & Verbal Info. PHYSICS. Steif (2004). Physics LearnLab: Andes Tutor.
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Robust Learning in Visual/Verbal Problem Solving: Contiguity, Integrated Hints, and Elaborated Explanations Vincent Aleven & Kirsten Butcher
Multiple Domains Involve Learning with Visual & Verbal Info PHYSICS Steif (2004) Physics LearnLab: Andes Tutor
Multiple Domains Involve Learning with Visual & Verbal Info CHEMISTRY Chemistry LearnLab Buffer Tutorial, Davenport (2006)
Multiple Domains Involve Learning with Visual & Verbal Info GEOMETRY Geometry Cognitive Tutor: Angles and Circles Units.
Research Goals • To understand how coordination between & integration of visual and verbal knowledge influences robust learning • To explore the potential transfer of laboratory-identified multimedia principles to classroom context • To inform the design of effective educational multimedia for classroom use
Relevant Learning Research • Learning with Multimedia • Contiguity Effect (e.g., Mayer, 2001) • Diagrams support inference-generation & integration of information (Butcher, 2006) • Self-explanations & Cognitive Tutors • Self-explanations promote learning (e.g., Chi et al., 1994) • Simple (menu-based) self-explanations support Geometry Learning (Aleven & Koedinger, 2002)
Connections to PSLC Theory • Sense-making • Coordinative Learning: Integrate results from multiple inputs & representations. • Verbal information • Visual information
Connections to PSLC Theory • Sense-making • Interactive Communication: Tutor prompts explanations • Students “explain” geometry principles that justify problem-solving steps • Students receive feedback and hints on explanations
Hypotheses: Visual Scaffolds to Improve Robust Learning • Contiguity • Work & receive feedback in diagram • Elaborated Explanations • Visual “explanations” to justify problem-solving • Integrated Hints • Apply verbal hints to visual problem situation (diagram)
Hypotheses: Sense-making Scaffolds • Contiguity • Work & receive feedback in diagram • Elaborated Explanations • Visual “explanations” to justify problem-solving • Integrated Hints • Apply verbal hints to visual problem situation (diagram)
Importance of PSLC LearnLab • Access to ample participants • 4 geometry teachers in 15 classes (190 students) • High student attrition (50 of 70 students completed study #1) • Classroom context is meaningful & cooperative • Tutor completion is part of normal classwork (graded!) • Study 1 -- 4 hours training, 1.5 hours testing • Student motivation is realistic, learning context is stable • Teachers open to research, comfortable with research software • Research Support • Carnegie Learning -- software QA, install, & support • Math Curriculum Committee -- feedback, coordination of research
Methods: Contiguity (Study 1) • Geometry Cognitive Tutor: 2 conditions • Table (noncontiguous) • Diagram (contiguous) • Procedure • Pretest (in class) • Training (classroom use of tutor, grade-matched pairs randomly assigned to conditions within classes) • Posttest (in class)
Assessment: 3 types of items Answers
Reasons Assessment: 3 types of items
Assessment: 3 types of items Transfer
Preliminary Results: Answers Main effect of test time: F (1, 38) = 29.5, p < .01
Preliminary Results: Reasons Main effect of test time: F (1, 38) = 65.7, p < .01
Preliminary Results: Transfer 3-way interaction: Test Time * Condition * Ability: F (1, 38) = 4.3, p < .05
Preliminary Results: Transfer 3-way interaction: Test Time * Condition * Ability: F (1, 38) = 4.3, p < .05
Preliminary Results: Process • Observational data (to be analyzed with log data) • Longer latency of responses in table condition • Order of solutions differ (table drives superficial order decisions) • Classroom Feedback • Teachers report student preference for diagram tutor • Teachers report better engagement from low ability students • “I like the [diagram] better, because you can see the answers in the diagram. Otherwise it’s easy to get confused with the table, you know, going back and forth and stuff.”
Research Team • Vincent Aleven: Research Scientist, CMU HCII • Kirsten Butcher: Research Postdoc, Pitt LRDC • Shelley Evenson: Assoc Prof, CMU School of Design • Octav Popescu: Research Programmer, CMU HCII • Andy Tzou: Masters Student: CMU HCII Honors Program • Carl Angiolillo: Masters Student: CMU HCII Honors Program • Grace Leonard: Research Associate, CMU HCII • Thomas Bolster: Research Associate, CMU HCII