The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Towards developing general models of usability with PARADISE
Natural Language Engineering
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Affective Transitions in Narrative-Centered Learning Environments
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
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Detection and analysis of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Using virtual tour behavior to build dialogue models for training review
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
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We present a method of evaluating the immediate performance impact of user state misclassifications in spoken dialogue systems. We illustrate the method with a tutoring system that adapts to student uncertainty over and above correctness. First we define a ranking of user states representing local performance. Second, we compare user state trajectories when the first state is accurately classified versus misclassified. Trajectories are quantified using a previously proposed metric representing the likelihood of transitioning from one user state to another. Comparison of the two sets of trajectories shows whether user state misclassifications change the likelihood of subsequent higher or lower ranked states, relative to accurate classification. Our tutoring system results illustrate the case where user state misclassification increases the likelihood of negative performance trajectories as compared to accurate classification.