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Fusing Wireless Sensor Data to Measure Small-Group Collaborative Processes in Real-Time. Gregory K. W. K. Chung, Girlie C. Delacruz, Linda F. de Vries, Cecile H. Phan, UCLA/CRESST Mani B. Srivastava Department of Electrical Engineering, UCLA Raul Alarcon, Seeds University Elementary School.

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fusing wireless sensor data to measure small group collaborative processes in real time

Fusing Wireless Sensor Data to Measure Small-Group Collaborative Processes in Real-Time

Gregory K. W. K. Chung, Girlie C. Delacruz, Linda F. de Vries, Cecile H. Phan, UCLA/CRESST

Mani B. SrivastavaDepartment of Electrical Engineering, UCLA

Raul Alarcon, Seeds University Elementary School

Annual Conference of the National Center for Research on Evaluation, Standards, and Student Testing (CRESST)

September 10-11, 2002

Los Angeles, CA

project background
Project Background
  • Project focus is on engineering issues
    • Wireless networks, embedded systems, sensors, location tracking, speech recognition, middleware, location services, data mining
  • Deploy in demonstration classroom for system validation
    • Engineering: Does the hardware and software work?
    • Education: Can we use the technology to assess small group collaboration?
ibadge
iBadge

1.75W x 2.75H x 0.5D inches

2.3 oz

research challenge
Research Challenge
  • Devise data fusion strategy to measure collaborative processes from simple behavioral data
    • Location (x,y)
    • Head orientation (degrees from true north)
    • Speech on/off
research issues
Research Issues
  • How can sensor-based data be transformed into measures useful for instructional and assessment purposes?
    • Feasibility
      • Can we develop a behavioral calculus for individual and group behavior in a classroom context?
      • Can the calculus be operationalized with sensor-based measures?
    • Validity issues
      • How accurate are sensor-based measures in detecting individual and group interaction patterns?
      • How well do sensor-based measures detect changes in individual and group interaction? Relate to external measures of performance and achievement?
behavioral calculus

Inferential (e.g., members are collaborating)

construct

behavioral primitive

Descriptive (e.g., facing speaker, speaking)

Location, orientation, speech on/off

Voltage

Behavioral Calculus

• • •

atomic-level measure

atomic-level measure

• • •

sensor data

sensor data

bayesian networks
Bayesian Networks
  • Model causal relations of phenomena
    • Graphical network representation
      • Nodes represent variables (observable, unobservable)
      • Links represent dependencies between variables
    • Conditional probabilities associated with each node
  • API available
    • Microsoft MSBNx API; Hugin API
    • Moment-to-moment updates to network
simplified bayesian network

Collaboration

Engagement

Interaction

Existence of Group

Member speaking?

Member facing speaker?

Orientation toward group centroid

Neighbors

Simplified Bayesian Network

Unobservable (12 nodes)

Observable (48 nodes)

pilot test
Pilot Test
  • Test approach to measure group processes
    • No real data yet -- badges in production
    • Simulate data from badges to exercise model under expected range of conditions
    • Create scenarios of group interaction -- define the processes that should be occurring
    • Carry out scenarios with mannequins and measure mannequin location and orientation (Claymation)
    • Compare probabilities yielded from Bayesian network to specifications of what should be occurring
pilot test11
Pilot Test
  • 6 Scenarios
    • 1 scenario - boundary conditions
    • 5 scenarios - small group vignettes
      • Intended to represent the range of small group behaviors (drawn from 10 hours of video across 2 weeks)
    • 169 snapshots
  • 3 raters judged match/no match between probabilities and our a priori specifications of what should be occurring
results
Results
  • Ratings
    • Four variables per snapshot, 169 snapshots
      • Overall collaboration, interaction, engagement, group
    • Collaboration, interaction, engagement - 82% match
    • Existence of group - 88% match
    • Overall collaboration, interaction - 0% match for one scenario
      • Model sensitive to misidentification of groups
next steps

Simulated Groups I

Preliminary Bayesian Model

Classroom Observation

Next Steps

Simulated Groups II

Classroom Trial

System Validation

  • Tool Interface
  • iBadge w/researchers going through same scenarios
  • False positives
  • False negatives
  • Relationship of sensor measures to performance measures and external measures