Socializing the intelligent tutor: bringing empathy to computer tutors
Learning Issues for Intelligent Tutoring Systems
Affective computing
Pedagogical agent research at CARTE
AI Magazine
Creating Interactive Virtual Humans: Some Assembly Required
IEEE Intelligent 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
Affective interactions: the computer in the affective loop
Proceedings of the 10th international conference on Intelligent user interfaces
Utterance classification in AutoTutor
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
International Journal of Artificial Intelligence in Education
Motivation and Affect in Educational Software
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
AutoTutor: A simulation of a human tutor
Cognitive Systems Research
The impact of individual differences on learning with an educational game and a traditional ITS
International Journal of Learning Technology
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We investigated the possibility of detecting affect from natural language dialogue in an attempt to endow an intelligent tutoring system, AutoTutor, with the ability to incorporate the learner’s affect into its pedagogical strategies. Training and validation data were collected in a study in which college students completed a learning session with AutoTutor and subsequently affective states of the learner were identified by the learner, a peer, and two trained judges. We analyzed each of these 4 data sets with the judges’ affect decisions, along with several dialogue features that were mined from AutoTutor’s log files. Multiple regression analyses confirmed that dialogue features could significantly predict particular affective states (boredom, confusion, flow, and frustration). A variety of standard classifiers were applied to the dialogue features in order to assess the accuracy of discriminating between the individual affective states compared with the baseline state of neutral.