Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
Modeling Students' Emotions from Cognitive Appraisal in Educational Games
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
The Dynamics of Affective Transitions in Simulation Problem-Solving Environments
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Towards Emotionally-Intelligent Pedagogical Agents
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
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
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Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
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International Journal of Learning Technology
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Motivational Diagnosis in ITSs: Collaborative, Reflective Self-Report
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Scaffolding Motivation and Metacognition in Learning Programming
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
International Journal of Human-Computer Studies
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Recognizing emotion from postures: cross-cultural differences in user modeling
UM'05 Proceedings of the 10th international conference on User Modeling
“Yes!”: using tutor and sensor data to predict moments of delight during instructional activities
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
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A motivationally intelligent tutor should determine the motivational state of the learner and also determine what caused that state. Only if the causation is taken into account can an efficient pedagogic strategy be selected to find an effective way to maintain or improve the learner's motivation. Thus we argue that motivation is more constructively thought of as a process involving causation rather than simply as a state. We describe methods by which this causality might be determined and suggest a range of pedagogic tactics that might be deployed as part of an overall pedagogic strategy.