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
Informing the Detection of the Students' Motivational State: An Empirical Study
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
Experimental evaluation of polite interaction tactics for pedagogical agents
Proceedings of the 10th international conference on Intelligent user interfaces
Emotion detection in task-oriented spoken dialogues
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Gender-Specific Approaches to Developing Emotionally Intelligent Learning Companions
IEEE Intelligent Systems
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
Investigating Human Tutor Responses to Student Uncertainty for Adaptive System Development
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Affective Transitions in Narrative-Centered Learning Environments
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Spoken Versus Typed Human and Computer Dialogue Tutoring
International Journal of Artificial Intelligence in Education
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
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
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
Turn-taking cues in a human tutoring corpus
HLT-SS '11 Proceedings of the ACL 2011 Student Session
When does disengagement correlate with learning in spoken dialog computer tutoring?
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Multimodal analysis of the implicit affective channel in computer-mediated textual communication
Proceedings of the 14th ACM international conference on Multimodal interaction
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
Proceedings of the 2013 international conference on Intelligent user interfaces
Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures
International Journal of Distance Education Technologies
Using reflective text to improve qualitative physics tutoring
International Journal of Learning Technology
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 evaluate the performance of a spoken dialogue system that provides substantive dynamic responses to automatically detected user affective states. We then present a detailed system error analysis that reveals challenges for real-time affect detection and adaptation. This research is situated in the tutoring domain, where the user is a student and the spoken dialogue system is a tutor. Our adaptive system detects uncertainty in each student turn via a model that combines a machine learning approach with hedging phrase heuristics; the learned model uses acoustic-prosodic and lexical features extracted from the speech signal, as well as dialogue features. The adaptive system varies its content based on the automatic uncertainty and correctness labels for each turn. Our controlled experimental evaluation shows that the adaptive system yields higher global performance than two non-adaptive control systems, but the difference is only significant for a subset of students. Our system error analysis indicates that noisy affect labeling is a major performance bottleneck, yielding fewer than expected adaptations thus lower than expected performance. However, the percentage of received adaptation correlates with higher performance over all students. Moreover, when uncertainty is accurately recognized and adapted to, local performance is significantly improved.