We examine the utility of speech and lexical features for predicting student emotions in computerhuman spoken tutoring dialogues. We first annotate student turns for negative, neu...
We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. W...
This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) s...
Lisa N. Michaud, Kathleen F. McCoy, Christopher A....
Understanding answers to open-ended explanation questions is important in intelligent tutoring systems. Existing systems use natural language techniques in essay analysis, but rev...
Myroslava Dzikovska, Gwendolyn E. Campbell, Charle...
We have developed and evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to learners’...
Sidney K. D'Mello, Blair Lehman, Jeremiah Sullins,...