Online engagement prediction in child-robot interaction
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2018-08-20
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en
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Abstract
Robots are becoming increasingly popular in society. Within education, research is mainly
focused on tutoring robots. Currently robots are compared with other methods of tutoring,
but these robots are not yet adaptive, whereas adaptivity might make child-robot
interaction more natural, and improve learning. Robots that can adapt to the child's
knowledge and emotional state, could have a great e ect on learning. One of these
projects that aims to design a tutoring robot is L2TOR. Within L2TOR a Nao humanoid
robot is used for tutoring children a second language. Some steps have been taken in
making the robot more adaptive towards children based on the mistakes a child makes
during a tutoring task. Currently the child's engagement with the robot is not taken into
account. In this thesis a pipeline is suggested to account for the child's engagement with
the robot. This pipeline is based on three features: gaze, smiling and posture. These
features have been identi ed as important predictors for child-robot engagement, and can
be used to adapt a robot's behavior. An experiment for validating this pipeline is described.
Statistics for the individual features were collected, based on an L2TOR dataset
annotated with task engagement, and it was found that smiling and a combination of the
three features correlated signi cantly with engagement. Guidelines for future work are
provided, since it is expected that online measuring of engagement can work given that
enough (balanced) data is available.
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Faculteit der Sociale Wetenschappen