  Social robots and interactive computer applications have the potential to
foster early language development in young children by acting as peer learning
companions. However, studies have found that children only trust robots which
behave in a natural and interpersonal manner. To help robots come across as
engaging and attentive peer learning companions, we develop models to predict
whether the listener will lose attention (Listener Disengagement Prediction,
LDP) and the extent to which a robot should generate backchanneling responses
(Backchanneling Extent Prediction, BEP) in the next few seconds. We pose LDP
and BEP as time series classification problems and conduct several experiments
to assess the impact of different time series characteristics and feature sets
on the predictive performance of our model. Using statistics & machine
learning, we also examine which socio-demographic factors influence the amount
of time children spend backchanneling and listening to their peers. To lend
interpretability to our models, we also analyzed critical features responsible
for their predictive performance. Our experiments revealed the utility of
multimodal features such as pupil dilation, blink rate, head movements, facial
action units which have never been used before. We also found that the dynamics
of time series features are rich predictors of listener disengagement and
backchanneling.
