A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning
Item
Title
A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning
Abstract/Description
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’ cognitive states, the affect-sensitive tutor is responsive to their affective states as well. This affective tutor automatically detects learners’ boredom, confusion, and frustration by monitoring conversational cues, gross body language, and facial features. The sensed affective states guide the tutor’s responses in a manner that helps students regulate their negative emotions. The tutor also synthesizes affect via the verbal content of its responses and the facial expressions and speech of an embodied pedagogical agent. An experiment comparing the affect-sensitive and non-affective tutors indicated that the affective tutor improved learning for low-domain knowledge students, particularly at deeper levels of comprehension. We conclude by discussing the conditions upon which affect-sensitivity is effective, and the conditions when it is not.
Author/creator
Date
In publication
Editor
Pages
245-254
Publisher
Springer
Resource type
Background/Context
Medium
Print
Background/context type
Conceptual
Open access/free-text available
Yes
Peer reviewed
Yes
ISBN
978-3-642-13388-6
Citation
D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., & Graesser, A. (2010). A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent Tutoring Systems (pp. 245–254). Springer. https://doi.org/10.1007/978-3-642-13388-6_29
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