Generating Tutorial Feedback with Affect

Johanna D. Moore, Kaska Porayska-Pomsta, Sebastian Varges, and Claus Zinn

Studies aimed at understanding what makes human tutoring effective have noted that the type of indirect guidance that characterizes human tutorial dialogue is a key factor. In this paper, we describe an approach that brings together sociolingusitic research on the basis of linguistic choice with natural language generation technology to systematically produce tutorial feedback appropriate to the given situation.


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