  Hedges play an important role in the management of conversational
interaction. In peer tutoring, they are notably used by tutors in dyads (pairs
of interlocutors) experiencing low rapport to tone down the impact of
instructions and negative feedback. Pursuing the objective of building a
tutoring agent that manages rapport with students in order to improve learning,
we used a multimodal peer-tutoring dataset to construct a computational
framework for identifying hedges. We compared approaches relying on pre-trained
resources with others that integrate insights from the social science
literature. Our best performance involved a hybrid approach that outperforms
the existing baseline while being easier to interpret. We employ a model
explainability tool to explore the features that characterize hedges in
peer-tutoring conversations, and we identify some novel features, and the
benefits of such a hybrid model approach.
