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» Adapting to Student Uncertainty Improves Tutoring Dialogues
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ACL
2004
13 years 7 months ago
Predicting Student Emotions in Computer-Human Tutoring Dialogues
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...
Diane J. Litman, Katherine Forbes-Riley
NAACL
2004
13 years 7 months ago
Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
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...
Katherine Forbes-Riley, Diane J. Litman
ASSETS
2000
ACM
13 years 10 months ago
An intelligent tutoring system for deaf learners of written English
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....
FLAIRS
2008
13 years 8 months ago
Diagnosing Natural Language Answers to Support Adaptive Tutoring
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...
ITS
2010
Springer
176views Multimedia» more  ITS 2010»
13 years 8 months ago
A Time for Emoting: When Affect-Sensitivity Is and Isn't Effective at Promoting Deep Learning
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,...