  This paper explores the application of machine learning techniques to predict
where hedging occurs in peer-tutoring interactions. The study uses a
naturalistic face-to-face dataset annotated for natural language turns,
conversational strategies, tutoring strategies, and nonverbal behaviours. These
elements are processed into a vector representation of the previous turns,
which serves as input to several machine learning models. Results show that
embedding layers, that capture the semantic information of the previous turns,
significantly improves the model's performance. Additionally, the study
provides insights into the importance of various features, such as
interpersonal rapport and nonverbal behaviours, in predicting hedges by using
Shapley values for feature explanation. We discover that the eye gaze of both
the tutor and the tutee has a significant impact on hedge prediction. We
further validate this observation through a follow-up ablation study.
