Synerg-eye-zing: Decoding Nonlinear Gaze Dynamics Underlying Successful Collaborations in Co-located Teams

Author:

Reddy G. S. Rajshekar1ORCID,Eloy Lucca2ORCID,Dickler Rachel3ORCID,Reitman Jason G.4ORCID,Pugh Samuel L.4ORCID,Foltz Peter W.4ORCID,Gorman Jamie C.5ORCID,Harrison Julie L.6ORCID,Hirshfield Leanne7ORCID

Affiliation:

1. NSF AI Institute for Student-AI Teaming, University of Colorado Boulder, United States and Institute of Cognitive Science, University of Colorado Boulder, United States

2. Institute of Cognitive Science, University of Colorado Boulder, United States

3. NSF AI Institute for Student-AI Teaming, University of Colorado - Boulder, United States

4. NSF AI Institute for Student-AI Teaming, University of Colorado Boulder, United States

5. Human Systems Engineering, Arizona State University, United States

6. School of Psychology, Georgia Institute of Technology, United States

7. NSF AI Institute for Student-AI Teaming, University of Colorado Boulder, United States and Institute of Cognitive Science, University of Colorado, United States

Funder

National Science Foundation

Publisher

ACM

Reference69 articles.

1. Eye to Eye: Gaze Patterns Predict Remote Collaborative Problem Solving Behaviors in Triads

2. APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

3. One of a kind

4. Daniel Belenky , Michael Ringenberg , Jennifer Olsen , Vincent Aleven , and Nikol Rummel . 2014. Using Dual Eye-Tracking to Evaluate Students ’ Collaboration with an Intelligent Tutoring System for Elementary-Level Fractions.Grantee Submission ( 2014 ). Daniel Belenky, Michael Ringenberg, Jennifer Olsen, Vincent Aleven, and Nikol Rummel. 2014. Using Dual Eye-Tracking to Evaluate Students’ Collaboration with an Intelligent Tutoring System for Elementary-Level Fractions.Grantee Submission (2014).

5. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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