Sharing a single expert among multiple partners

Authors:
Jeffrey Wong;Lui Min Oh;Jiazhi Ou;Carolyn P. Rosé;Jie Yang;Susan R. Fussell
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;DSO National Laboratories, Singapore, Singapore;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2007

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Abstract

Expertise to assist people on complex tasks is often in short supply. One solution to this problem is to design systems that allow remote experts to help multiple people in simultaneously. As a first step towards building such a system, we studied experts' attention and communication as they assisted two novices at the same time in a co-located setting. We compared simultaneous instruction when the novices are being instructed to do the same task or different tasks. Using machine learning, we attempted to identify speech markers of upcoming attention shifts that could serve as input to a remote assistance system.