Responding to subtle, fleeting changes in the user's internal state
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Multimodal affect recognition in learning environments
Proceedings of the 13th annual ACM international conference on Multimedia
The politeness effect: Pedagogical agents and learning outcomes
International Journal of Human-Computer Studies
User Modeling and User-Adapted Interaction
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Diagnosing and acting on student affect: the tutor's perspective
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
The relative impact of student affect on performance models in a spoken dialogue tutoring system
User Modeling and User-Adapted Interaction
What Are You Feeling? Investigating Student Affective States During Expert Human Tutoring Sessions
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems
International Journal of Artificial Intelligence in Education
Tools for Authoring a Dialogue Agent that Participates in Learning Studies
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Image and Vision Computing
Discourse structure and performance analysis: beyond the correlation
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Models for multiparty engagement in open-world dialog
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Spoken tutorial dialogue and the feeling of another's knowing
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
When does disengagement correlate with learning in spoken dialog computer tutoring?
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Exploring user satisfaction in a tutorial dialogue system
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Adapting to multiple affective states in spoken dialogue
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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We present a model for detecting user disengagement during spoken dialogue interactions. Intrinsic evaluation of our model (i.e., with respect to a gold standard) yields results on par with prior work. However, since our goal is immediate implementation in a system that already detects and adapts to user uncertainty, we go further than prior work and present an extrinsic evaluation of our model (i.e., with respect to the real-world task). Correlation analyses show crucially that our automatic disengagement labels correlate with system performance in the same way as the gold standard (manual) labels, while regression analyses show that detecting user disengagement adds value over and above detecting only user uncertainty when modeling performance. Our results suggest that automatically detecting and adapting to user disengagement has the potential to significantly improve performance even in the presence of noise, when compared with only adapting to one affective state or ignoring affect entirely.