Congratulations to Mihai on passing his comprehensive exam today!
His reading lists, writeups, and presentation can be found
online.
We examine correlations between dialogue behaviors and learning in tutoring, using two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. To formalize the notion of dialogue behavior, we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain. A unigram analysis of our annotated data shows that student learning is correlated both with the dialogue acts of the tutor and with the dialogue acts of the student. A bigram analysis of our data shows that learning is also correlated with joint patterns of tutor and student dialogue acts. Our results show that while the use of dialogue act n-grams is a promising method for examining correlations between dialogue behavior and learning, specific findings can differ in human versus computer tutoring, with the latter better motivating adaptive strategies for implementation. In addition, we also show that although many of our students experience problems with speech recognition, such problems do not negatively correlate with student learning.