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
Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems
International Journal of Artificial Intelligence in Education
Adapting to Student Uncertainty Improves Tutoring Dialogues
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Using Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains Modalities
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Metacognition and learning in spoken dialogue computer tutoring
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Estimating a user’s internal state before the first input utterance
Advances in Human-Computer Interaction
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We hypothesize that monitoring the accuracy of the "feeling of another's knowing" (FOAK) is a useful predictor of tutorial dialogue system performance. We test this hypothesis in the context of a wizarded spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our corpus, which has been annotated with respect to student correctness and uncertainty. We then discuss the derivation of FOAK measures from these annotations, for use in building predictive performance models. Our results show that monitoring the accuracy of FOAK is indeed predictive of student learning, both in isolation and in conjunction with other predictors.