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a team workbench for scholarly investigation

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

  • Socio-cultural consequences of group decisions
  • Inability to collect, analyze, and manage
    • High resolution,
    • High quality,
    • High volume interaction network data
  • Effective computer-aided collaboration among
    • Scholars
    • Scientists
    • Students
    • Volunteers
    • Stakeholders
scientific challenges
Scientific Challenges
  • We 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 conferences
    • Current studies are surveys and case studies, not direct observation, the gold standard
    • No 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 problem
Importance of the Problem
  • Many critical groups are of this size:
    • Design Teams
    • Scholarly Collaborations
    • Cultural Studies
    • Legislative Bodies
    • Disaster Response Teams
    • Archaeology Teams
    • Medical Teams
    • Military Units
supported by
Supported By
  • Cyber-enabled Discovery and Innovation (CDI) program, National Science Foundation
    • Two Million Dollars Grant
  • National Center for Supercomputing Apps
  • Office of the Vice Chancellor for Research, University of Illinois
  • Year 2 of Five Year Project
    • Project “GroupScope”
  • End-to-end system from data capture to analysis to user and team engagement
  • Video cameras to capture video and audio, of
    • Study subjects such as children on playground
    • Scholars and researchers executing the study—in team and individually
  • Synchronization of video and audio data
  • Annotation of video and audio
  • Coding of video and audio
  • Management of video and audio data
  • Analysis of video and audio; scenario simulation and machine learning
  • Community involvement
circle of continuous improvement
Circle of Continuous Improvement

Data Management (Medici content management, ELAN transcription)

Data Acquisition (cameras, Kinect, audio recorders, GPS, iPhones, iPads)

First-order Data (audios, photos, videos, sensor data)

Second-order Data (visual, audio and text annotations, coding and metadata)

Network analysis, Group identification, Interaction categorization

What-if Scenario Simulation and Machine Learning

community engagement
Community Engagement
  • Professors and graduate students as primary research participants
  • Students help annotate videos and audios of
    • study objects and artifacts
    • research activities of professors and research assistants
  • Interested folks help transcribe, translate, and annotate videos and annotate
    • Multi-lingual collaboration enabled
  • Scenario “what-if” analyses of interactions and events
  • Annotated videos will “live” across time and place
    • Insights, inspirations, and moments are recorded and not lost to time and place
in closing
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 space
  • By your consent, putting up video cameras to get PNC 2017 networking?
    • Will put up video cameras for NSF Radical Innovation Summit 2013