An analysis of human tutors' actions in tutorial dialogues


Understanding effective human tutors' strategies is one approach to discovering effective tutorial strategies. These strategies are described in terms of actions that tutors take while interacting with learners. To this end, we analyze in this paper dialoguebased interactions between professional tutors and tutees. There are two challenges when exploring patterns in such dialoguebased tutorial interactions. First, we need to map utterances, by the tutor and by the tutee, into actions. To address this challenge, we rely on the language-as-action theory according to which when we say something we do something. A second challenge is detecting effective tutorial sessions using objective measurements of learning. To tackle this challenge we align tutorial conversations with pre- and post-measures of student mastery obtained from an intelligent tutoring system with which the students interacted before and after interacting with the human tutor. We present performance results of the automated tools that we developed to map tutor-tutee utterances onto dialogue acts and dialogue modes. We also report the most interesting emerging patterns in terms of tutor and tutees' actions. These patterns could inform our understanding of the tutoring process and the development of intelligent tutoring systems.

Publication Title

FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

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