Dr. Theodora Chaspari of Texas A&M University

Diverse teaming creates opportunities for approaching
problems from different perspectives inspiring innovative
Micro-behaviors, which are brief, often unconscious
ideas. Little research has focused on the interpersonal
factors that affect diverse team functioning, with most
emphasizing factors such as team composition and training.
expressions, words, and tone of voice that occur during
team interactions, are rarely explored as factors that
could impact the team dynamics. Due to their subtlety,
micro-behaviors can be done subconsciously or in a fleeting
manner that can go unnoticed, nonetheless, such behaviors
play an important role in overall team dynamics rendering
the detection of these behaviors crucial to forming
intervention procedures geared toward ensuring successful
team functioning. This presentation will discuss the
effectiveness of linguistic and acoustic features extracted
at the conversation level between interlocutors in
automatically detecting micro-behaviors via machine
learning. It will further present various ways to integrate
contextual information about the occurring task and the
underlying sentiment of the conversation in the microbehavior
detection system. Results will be presented in two different
settings. The first setting uses longitudinal data collected
from teams during a 30-day space simulation in the Human
Exploration Research Analog (HERA) of the U.S. National
Aeronautics and Space Administration (NASA). The second
setting will discuss preliminary findings from team
interactions of first- and second-year undergraduate
students in Science Technology Engineering and Mathematics
(STEM) who were asked to solve programming tasks.

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