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
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
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
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
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
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Proceedings of the 14th ACM international conference on Multimodal interaction
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We investigate whether four metacognitive metrics derived from student correctness and uncertainty values are predictive of student learning in a fully automated spoken dialogue computer tutoring corpus. We previously showed that these metrics predicted learning in a comparable wizarded corpus, where a human wizard performed the speech recognition and correctness and uncertainty annotation. Our results show that three of the four metacognitive metrics remain predictive of learning even in the presence of noise due to automatic speech recognition and automatic correctness and uncertainty annotation. We conclude that our results can be used to inform a future enhancement of our fully automated system to track and remediate student metacognition and thereby further improve learning.