Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Detecting the Learner's Motivational States in An Interactive Learning Environment
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
When do Students Interrupt Help? Effects of Time, Help Type, and Individual Differences
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
Recognizing, modeling, and responding to users' affective states
UM'05 Proceedings of the 10th international conference on User Modeling
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of 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
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: Motivationally intelligent systems deploy resources and tactics dynamically to maintain or increase the student's desire to learn and her willingness to expend effort in so doing. Three categories of diagnostic inputs and feedback reactions are outlined each with its associated meta-level. The meta-level includes the account which learners tell themselves, the system and others about what they know, how they feel, and the conditions under which they learn best.