D.: Content-learning correlations in spoken tutoring dialogs at word, turn and discourse levels

We study correlations between dialog content and learn-ing in a corpus of human-computer tutoring dialogs. Using an online encyclopedia, we first extract domain-specific concepts discussed in our dialogs. We then ex-tend previously studied shallow dialog metrics by in-corporating content at three levels of granularity (word, turn and discourse) and also by distinguishing between students ’ spoken and written contributions. In all ex-periments, our content metrics show strong correlations with learning, and outperform the corresponding shal-low baselines. Our word-level results show that al-though verbosity in student writings is highly associated with learning, verbosity in their spoken turns is not. On the other hand, we notice that content along with con-ciseness in spoken dialogs is strongly correlated with learning. At the turn-level, we find that effective tutor-ing dialogs have more content-rich turns, but not neces-sarily more or longer turns. Our discourse-level analysis computes the distribution of content across larger dialog units and shows high correlations when student contri-butions are rich but unevenly distributed across dialog segments