ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Technical Section: A multimedia framework for effective language training
Computers and Graphics
Optimized expected information gain for nonlinear dynamical systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Predicting Learner Answers Correctness through Brainwaves Assesment and Emotional Dimensions
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Ranking feature sets for emotion models used in classroom based intelligent tutoring systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Poisson-based inference for perturbation models in adaptive spelling training
International Journal of Artificial Intelligence in Education
Modelling and optimizing the process of learning mathematics
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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In this paper, we introduce a model of engagement dynamics in spelling learning. The model relates input behavior to learning, and explains the dynamics of engagement states. By systematically incorporating domain knowledge in the preprocessing of the extracted input behavior, the predictive power of the features is significantly increased. The model structure is the dynamic Bayesian network inferred from student input data: an extensive dataset with more than 150 000 complete inputs recorded through a training software for spelling. By quantitatively relating input behavior and learning, our model enables a prediction of focused and receptive states, as well as of forgetting.