Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
The language of emotion in short blog texts
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Coarse-grained detection of student frustration in an introductory programming course
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Responding to Learners' Cognitive-Affective States with Supportive and Shakeup Dialogues
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
Modeling User Affect from Causes and Effects
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Using emotion to gain rapport in a spoken dialog system
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Multimethod assessment of affective experience and expression during deep learning
International Journal of Learning Technology
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
Intelligent tutoring systems: new challenges and directions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
User Modeling and User-Adapted Interaction
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Evaluation of unsupervised emotion models to textual affect recognition
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
Designing affective computing learning companions with teachers as design partners.
Proceedings of the 3rd international workshop on Affective interaction in natural environments
PlayPhysics: an emotional games learning environment for teaching physics
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Polite web-based intelligent tutors: Can they improve learning in classrooms?
Computers & Education
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Modeling mental workload using EEG features for intelligent systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Automatic identification of affective states using student log data in ITS
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Investigating acoustic cues in automatic detection of learners' emotion from auto tutor
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
In the zone: towards detecting student zoning out using supervised machine learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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
Metacognition and learning in spoken dialogue computer tutoring
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
Tune in to your emotions: a robust personalized affective music player
User Modeling and User-Adapted Interaction
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Mental workload, engagement and emotions: an exploratory study for intelligent tutoring systems
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Categorical vs. dimensional representations in multimodal affect detection during learning
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
I feel you: towards affect-sensitive domotic spoken conversational agents
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
A Framework for Designing Computer Supported Learning Systems with Sensibility
International Journal of e-Collaboration
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Proceedings of the Third International Conference on Learning Analytics and Knowledge
AffectButton: A method for reliable and valid affective self-report
International Journal of Human-Computer Studies
Modelling human tutors' feedback to inform natural language interfaces for learning
International Journal of Human-Computer Studies
Exploiting sentiment analysis to track emotions in students' learning diaries
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
Virtual butler: what can we learn from adaptive user interfaces?
Your Virtual Butler
When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?
International Journal of Artificial Intelligence in Education - Best of AIED 2011
Inducing and Tracking Confusion with Contradictions during Complex Learning
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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We explored the reliability of detecting a learner's affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training data were collected in a learning session with AutoTutor, after which the affective states of the learner were rated by the learner, a peer, and two trained judges. Inter-rater reliability scores indicated that the classifications of the trained judges were more reliable than the novice judges. Seven data sets that temporally integrated the affective judgments with the dialogue features of each learner were constructed. The first four datasets corresponded to the judgments of the learner, a peer, and two trained judges, while the remaining three data sets combined judgments of two or more raters. Multiple regression analyses confirmed the hypothesis that dialogue features could significantly predict the affective states of boredom, confusion, flow, and frustration. Machine learning experiments indicated that standard classifiers were moderately successful in discriminating the affective states of boredom, confusion, flow, frustration, and neutral, yielding a peak accuracy of 42% with neutral (chance = 20%) and 54% without neutral (chance = 25%). Individual detections of boredom, confusion, flow, and frustration, when contrasted with neutral affect, had maximum accuracies of 69, 68, 71, and 78%, respectively (chance = 50%). The classifiers that operated on the emotion judgments of the trained judges and combined models outperformed those based on judgments of the novices (i.e., the self and peer). Follow-up classification analyses that assessed the degree to which machine-generated affect labels correlated with affect judgments provided by humans revealed that human-machine agreement was on par with novice judges (self and peer) but quantitatively lower than trained judges. We discuss the prospects of extending AutoTutor into an affect-sensing ITS.