A model for reasoning about persistence and causation
Computational Intelligence
Student Modeling and Mastery Learning in a Computer-Based Proramming Tutor
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
Probabilistic Student Models: Bayesian Belief Networks and Knowledge Space Theory
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
A Belief Net Backbone for Student Modelling
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Using a Probabilistic Student Model to Control Problem Difficulty
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Adaptive Bayesian Networks for Multilevel Student Modelling
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Bayesian networks for student model engineering
Computers & Education
International Journal of Artificial Intelligence in Education
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
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When a belief network is used to represent a student model, we must have a theoretically-sound way to update this model. In ordinary belief networks, it is assumed that the properties of the external world, modelled by the network, do not change as we go about gathering evidence related to those properties. I present a general approach as to how student model updates should be made, based on the concept of a dynamic belief network, and then show this work relates to previous research in this area.