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
Neural correlates of symbolic number comparison in developmental dyscalculia
Journal of Cognitive Neuroscience
Modeling engagement dynamics in spelling learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
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
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In this paper, we explore the possibility of a general framework for modelling engagement dynamics in software tutoring, focusing on the cases of developmental dyslexia and developmental dyscalculia. This project aims at capturing the similar engagement state patterns for the two learning disabilities. We start by presenting a model of engagement dynamics in spelling learning, which relates input behaviour to learning and explains the dynamics of engagement states. Predictive power of extracted features is increased by incorporating domain knowledge in the pre-processing. The introduced model enables the prediction of focused and receptive states, and of forgetting. In the second part, we extend the model to a more general framework, which takes into account the similarities and dissimilarities of the two studied cases. Finally, we define desirable properties of a general engagement dynamics model, while analysing the reusability of the introduced spelling model.