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