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...
Human tutors detect and respond to student emotional states, but current machine tutors do not. Our preliminary machine learning experiments involving transcription, emotion annot...
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...
The relationship between emotions and learning was investigated by tracking the emotions that college students experienced while learning about computer literacy with AutoTutor. Au...
Arthur C. Graesser, Patrick Chipman, Brandon King,...
This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experime...
Ivon Arroyo, David G. Cooper, Winslow Burleson, Be...