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Transcript of Scott Poole, UIUC; Noshir Contractor, Northwestern; Mark Hasegawa-Johnson, UIUC; Feniosky Pena-Mora,...
A Team Workbench for Scholarly Investigation
Scott Poole, UIUC; Noshir Contractor, Northwestern; Mark Hasegawa-Johnson, UIUC; Feniosky Pena-Mora,
Columbia; David Forsyth, UIUC; Kenton McHenry, UIUC; Dorothy Espelage, UIUC; Margaret Fleck, UIUC;
Alex Yahja, National Center for Supercomputing Apps
The Story behind Cultural Artifacts
ChallengesSocio-cultural consequences of group decisionsInability to collect, analyze, and manage
High resolution,High quality,High volume interaction network data
Effective computer-aided collaboration among ScholarsScientistsStudentsVolunteersStakeholders
Scientific ChallengesWe understand small teams co-located (1-6
persons) and we think we understand large aggregations of 1000s
We don’t understand large teams: 8-25, 25-70, 50-300, 350-500, 400-1000—the sweet spot of scholarly collaborations and conferencesCurrent studies are surveys and case studies, not
direct observation, the gold standardNo tech to study these even though we coalesce in
natural groups of size 2, 5, 15,…Spatial dispersion and movement make big
difference
Importance of the ProblemMany critical groups are of this size:
Design TeamsScholarly CollaborationsCultural StudiesLegislative BodiesDisaster Response TeamsArchaeology TeamsMedical TeamsMilitary Units
“Swarming” Disaster Response
Supported ByCyber-enabled Discovery and Innovation
(CDI) program, National Science FoundationTwo Million Dollars Grant
National Center for Supercomputing AppsOffice of the Vice Chancellor for Research,
University of Illinois
Year 2 of Five Year ProjectProject “GroupScope”
ApproachEnd-to-end system from data capture to analysis to user
and team engagementVideo cameras to capture video and audio, of
Study subjects such as children on playgroundScholars and researchers executing the study—in team
and individuallySynchronization of video and audio dataAnnotation of video and audioCoding of video and audioManagement of video and audio dataAnalysis of video and audio; scenario simulation and
machine learningCommunity involvement
Data Acquisition (cameras, Kinect, audio recorders, GPS, iPhones, iPads)
First-order Data (audios, photos, videos, sensor data)
Data Management (Medici content management, ELAN transcription)
Second-order Data (visual, audio and text annotations, coding and metadata)
Network analysis, Group identification, Interaction categorization
What-if Scenario Simulation and Machine Learning
Circle of Continuous Improvement
2D Face Tracking (Kalal TLD)
Depth-image “Kinect” Skeleton Tracking
Human Movement Recognition
Social Interaction Recognition
Community EngagementProfessors and graduate students as primary research
participantsStudents help annotate videos and audios of
study objects and artifactsresearch activities of professors and research assistants
Interested folks help transcribe, translate, and annotate videos and annotateMulti-lingual collaboration enabled
Scenario “what-if” analyses of interactions and eventsAnnotated videos will “live” across time and place
Insights, inspirations, and moments are recorded and not lost to time and place
In Closing“GroupScope” tool is designed to provide
Computer-assisted collaboration among human teams
Natural and native human and professional social-networking—synergistic human machine effort
Scholarly collaboration tool with native domain-specific design and interfaces
Natural collaboration spaceBy your consent, putting up video cameras to get
PNC 2017 networking?Will put up video cameras for NSF Radical
Innovation Summit 2013