  Speakers build rapport in the process of aligning conversational behaviors
with each other. Rapport engendered with a teachable agent while instructing
domain material has been shown to promote learning. Past work on lexical
alignment in the field of education suffers from limitations in both the
measures used to quantify alignment and the types of interactions in which
alignment with agents has been studied. In this paper, we apply alignment
measures based on a data-driven notion of shared expressions (possibly composed
of multiple words) and compare alignment in one-on-one human-robot (H-R)
interactions with the H-R portions of collaborative human-human-robot (H-H-R)
interactions. We find that students in the H-R setting align with a teachable
robot more than in the H-H-R setting and that the relationship between lexical
alignment and rapport is more complex than what is predicted by previous
theoretical and empirical work.
