Building rapport with a teachable agent enhances learning. In human-human interactions, speakers build rapport by aligning their conversational behaviors with others. However, the roles of lexical alignment (LA) in building rapport with computational agents are more complex. Computing LA is problematic for emerging multi-party scenarios because neither existing multi-party measures nor combinations of pair-wise measures are designed to model these roles. Thus, we extend an existing LA measure to better capture the dynamics of alignment in multi-party human-computer interactions by automatically extracting lexical patterns used by all speakers and characterizing the alignment behaviors of each (group of) speaker(s). Our new measure predicts rapport in a human-human-robot collaborative scenario better than existing ones and captures individual contributions to a group’s alignment.