Cognitive modeling and intelligent tutoring
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Evolving spelling exercises to suit individual student needs
Applied Soft Computing
Technical Section: A multimedia framework for effective language training
Computers and Graphics
A Phoneme-Based Student Model for Adaptive Spelling Training
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
An online algorithm for hierarchical phoneme classification
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Modeling engagement dynamics in spelling learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Modelling and optimizing the process of learning mathematics
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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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We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification (local) and prediction of further performance (global). The inference algorithm has been employed in a student model for spelling with a detailed set of letter and phoneme based mal-rules. The local and global information about the student allows for appropriate remediation actions to adapt to their needs. The error classification, student model prediction and the efficacy of the adapted remediation actions have been validated on the data of two large-scale user studies. The enhancement of the spelling training based on the novel student model resulted a significant increase in the student learning performance.