Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Multimodal affect recognition in learning environments
Proceedings of the 13th annual ACM international conference on Multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Comparing Two Emotion Models for Deriving Affective States from Physiological Data
Affect and Emotion in Human-Computer 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
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
User Modeling and User-Adapted Interaction
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
New Perspectives on Affect and Learning Technologies
New Perspectives on Affect and Learning Technologies
The impact of system feedback on learners' affective and physiological states
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
Hybrid fusion approach for detecting affects from multichannel physiology
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Automatic natural expression recognition using head movement and skin color features
Proceedings of the International Working Conference on Advanced Visual Interfaces
Categorical vs. dimensional representations in multimodal affect detection during learning
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Combining classifiers in multimodal affect detection
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Knowledge Elicitation Methods for Affect Modelling in Education
International Journal of Artificial Intelligence in Education
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It is widely acknowledged that learners experience a variety of emotions while interacting with Intelligent Tutoring Systems (ITS), hence, detecting and responding to emotions might improve learning outcomes. This study uses machine learning techniques to detect learners' affective states from multichannel physiological signals (heart activity, respiration, facial muscle activity, and skin conductivity) during tutorial interactions with AutoTutor, an ITS with conversational dialogues. Learners were asked to self-report (both discrete emotions and degrees of valence/arousal) the affective states they experienced during their sessions with AutoTutor via a retrospective judgment protocol immediately after the tutorial sessions. In addition to mapping the discrete learning-centered emotions (e.g., confusion, frustration, etc) on a dimensional valence/arousal space, we developed and validated an automatic affect classifier using physiological signals. Results indicate that the classifier was moderately successful at detecting naturally occurring emotions during the AutoTutor sessions.