Tasha K. Hollingsed and Nigel G. Ward
ISCA Workshop on Speech and Language Technology in Education (SLaTE) 2007
A good tutoring system should be able to detect and respond to subtle changes in the affective state of the learner, as a way to motivate and encourage the student, thereby improving the learning outcomes. This responsiveness should also operate at the sub-second timescale, as with some human tutors. Modeling this ability is, however, a challenge. This paper presents a combined method for the discovery of the rules governing such real-time responsiveness. This method uses both machine-learning and perceptual techniques, both with and without reference to internal states. This method is illustrated with the problem of choosing supportive acknowledgments in memory-reinforcing quiz dialogs. A wizard-of-oz experiment showed that users prefer a tutorial system based on responsive rules to one that chooses acknowledgments at random.
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