Vincent Aleven & Kirsten Butcher Robust Learning in Visual/Verbal Problem Solving: Contiguity, Integrated Hints, and Elaborated Explanations

Multiple Domains Involve Learning with Visual & Verbal Info Steif (2004) PHYSICS 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

Existing Tutor: Multiple Verbal Inputs

Existing Tutor: Multiple Visual Inputs

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

Existing Tutor: Explanations are verbal-only

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 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)

Table Condition = Noncontiguous

Diagram Condition = Contiguous

Assessment: 3 types of items

Answers

Assessment: 3 types of items Reasons

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

Questions?

Extra Slides

Continued Work: Elaborated Explanations

Elaborated Explanations Interface