Affective computing
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
SENTIMENT ASSESSMENT OF TEXT BY ANALYZING LINGUISTIC FEATURES AND CONTEXTUAL VALENCE ASSIGNMENT
Applied Artificial Intelligence
Emotions and Learning with AutoTutor
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
International Journal of Human-Computer Studies
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
Affective Artificial Intelligence in Education: From Detection to Adaptation
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
Talk like an electrician: student dialogue mimicking behavior in an intelligent tutoring system
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Encouraging students to study more: adapting feedback to personality and affective state
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Predicting student knowledge level from domain-independent function and content words
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Towards IMACA: intelligent multimodal affective conversational agent
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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We explored the possibility of predicting learners' affective states (boredom, flow/engagement, confusion, and frustration) by monitoring variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. Multiple measures of cohesion (e.g., pronouns, connectives, semantic overlap, causal cohesion, coreference) were automatically computed using the Coh-Metrix facility for analyzing discourse and language characteristics of text. Cohesion measures in multiple regression models predicted the proportional occurrence of each affective state, yielding medium to large effect sizes. The incidence of negations, pronoun referential cohesion, causal cohesion, and co-reference cohesion were the most diagnostic predictors of the affective states. We discuss the generalizability of our findings to other domains and tutoring systems, as well as the possibility of constructing real-time, cohesion-based affect detectors.