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All slides copyright 1999 by Andrew JohnsonElectronic Visualization LaboratoryUniversity of Illinois at Chicago

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Andrew Johnson, Thomas Moher, Stellan Ohlsson, Mark Gillingham,The Round Earth Project - Deep Learning in a Collaborative Virtual World Paper presentation at the IEEE VR ‘99 Conference, Houston, TX, 1999.

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Electronic Visualization Laboratory University of Illinois at Chicago

The Round Earth Project:Deep Learning in a

Collaborative Virtual World

Andrew Johnson, Thomas Moher, Stellan Ohlsson, Mark Gillingham

Electronic Visualization Laboratory University of Illinois at Chicago

4 Criteria For Learning Problems

• Learning goal must be important - identified in national goals, standards or benchmarks

• Learning goal must be hard

• Learning goal must be plausibly enhanced by immersive virtual reality technology

• Learning environments must be informed by contemporary research in learning science and contemporary practice in education

Electronic Visualization Laboratory University of Illinois at Chicago

The ‘flat’ Earth

• Children believe the Earth is flat – sky layer and earth layer– absolute ‘up’ and ‘down’

• Telling children that the Earth is spherical– new knowledge assimilated into prior model– conclusion is often a pancake shaped Earth

or a dual model

Electronic Visualization Laboratory University of Illinois at Chicago

Good Match for 4 Criteria

• AAAS Project 2061 Benchmarks for Science Literacy - 5th graders should know the Earthis approximately spherical, things on or near theEarth are pulled toward it by gravity

• Existing literature by Vosniadou and Brewer that this is a hard concept to learn

• Use VR to feel like you are on another world

• Emphasize role differentiation with positive interdependence, collaboration

Electronic Visualization Laboratory University of Illinois at Chicago

Deep Ideas

• Spherical Earth is example of a Deep Idea - fundamental concept

• Paradox - When trying to learn a concept that is different and more fundamental than existing ideas, those ideas are the learner’s only tools to acquire the new idea

• Typical outcome is distortion

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Deep Learning

• Learning of deep ideas may requirean alternative cognitive starting pointwhere knowledge is established

• Virtual reality is used to create this alternative cognitive starting point

• After the experience, bridging activitiesbring this new knowledge into contactwith prior knowledge

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Compare 2 Learning Strategies in VR

• Selectionist - use alternative cognitive starting point to establish the idea -Asteroid World

• Transformationalist - new knowledge created via operators on prior knowledge - Earth World

Electronic Visualization Laboratory University of Illinois at Chicago

Collaborative Task

• Task of collecting 10 objects scattered around the sphere forces the astronaut to move around the sphere

• Children talk to each other

• Mission control can see the objects and guides the astronaut from the spherical view

• Children must integrate the two viewpoints

• Children switch roles - see both perspectives

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Asteroid World - Selectionist

Mission Control View

Astronaut View

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Earth World - Transformationalist

Astronaut View

Mission Control View

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Studies

• 3 pilot studies - summer ‘98

• 1 main study - December ‘98

Electronic Visualization Laboratory University of Illinois at Chicago

Pilot Studies

• 3 pilot studies with 34 children

• 40 Minute VR experience

• 20 question pretest & posttest

• Conducted at EVL

• Astronaut in CAVE, Mission Control at IDesk

• Children - small urban Chicago public school– Below state averages in reading, writing, math– Failed Iowa Test of Basic Skills Grade 3 Exam

Electronic Visualization Laboratory University of Illinois at Chicago

Pilot Study 1 and 2

• Pilot study 1 focused on interaction details

• Pilot study 2 focused on learning issues

• Children– actively talking– engaged– too task oriented

• Focus on 2D evaluation methods

• Appeared to be very little learning going on

Electronic Visualization Laboratory University of Illinois at Chicago

Pilot Study 3

• Increased effort to point out features of landscape before the experience

• Focus on 3D evaluation methods, andfollow up probing questions

• Cases where learning happened and post-test appears to reflect fundamental change

• Mission control concentrated on CG and did not use video window of astronaut

Electronic Visualization Laboratory University of Illinois at Chicago

Main Study

• Conducted at Abraham Lincoln Elementary School - Oak Park, IL

• Astronaut at anImmersaDesk

• Mission Control ata stereo monitor

Electronic Visualization Laboratory University of Illinois at Chicago

Main Study

• 84 second graders • 76 took pretest• 18 questions on 5 topics

verbally, paper, playDoh

• Divided the children into 3 groupshigh group >14 correct - 22 kidsintermediate group 11-14 correct - 22 kidslow group <11 correct - 29 kids

Electronic Visualization Laboratory University of Illinois at Chicago

Main Study

• 14 pairs in Asteroid World

• 30 minute VR + 10 minutebridging activities

• 1 child reported dizziness as mission control

• A few children reported being initially scared about walking on the asteroid

• 1 child was unable to continue as astronaut

Electronic Visualization Laboratory University of Illinois at Chicago

Main Study

• Treatment group - VR and bridging7.3 correct -> 12.9 correctstatistically significant

• Intermediate group - no VR or bridging12.1 correct -> 14.0 correctstatistically significant

• Pretest difference statistically significant

• Posttest difference not statistically significant

Electronic Visualization Laboratory University of Illinois at Chicago

Further Studies

• Delayed post-test in late March ‘99

• Comparing the two strategies and Investigating the relative influence ofVR and bridging activities in June ‘99

Electronic Visualization Laboratory University of Illinois at Chicago

In the School

• Conduct studies faster, with fewer staff

• Principal and teachers very supportive

• High levels of excitement and interest

• Favorably compared to a Sony Playstation

Electronic Visualization Laboratory University of Illinois at Chicago

Acknowledgements

• Funded by NSF award EIA 9720351 - Deep Learning and Visualization Technologies

• Whole team - Joe Alexander, Tom DeFanti, Mark Gillingham, Josh Hemmerich, Jyoti Jain, Andrew Johnson, Tom Moher, Mark Orr, Stellan Ohlsson, Carlos Orrego, Maria Roussos, and Mike Trolio

• With help from - Julieta Aguilera, Josephone Anstey, Jim Costigan, Greg Dawe, Tom Frisch, Steve Jones, Jason Leigh, Dave Pape, Sam Thongrong, and Fang Wang

Electronic Visualization Laboratory University of Illinois at Chicago

For More Information

www.evl.uic.edu/roundearthroundearth@evl.uic.edu