User models: theory, method, and practice
International Journal of Man-Machine Studies
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
Machine learning in user modeling
Machine Learning and Its Applications
Logic-Based Representation and Reasoning for User Modeling Shell Systems
User Modeling and User-Adapted Interaction
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Some Problems and Proposals for Knowledge Representation
Some Problems and Proposals for Knowledge Representation
Reasoning about Uncertainty
Knowledge Representation and Reasoning
Knowledge Representation and Reasoning
A Bayesian approach for user modeling in dialogue systems
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Reasoning about knowledge and probability
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
Assessing Learner's Scientific Inquiry Skills Across Time: A Dynamic Bayesian Network Approach
UM '07 Proceedings of the 11th international conference on User Modeling
A case study of knowledge representation in UC
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evaluating the integration of fuzzy logic into the student model of a web-based learning environment
Expert Systems with Applications: An International Journal
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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The core of adaptive system is user model containing personal information such as knowledge, learning styles, goals which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognition... but they do not bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer user's knowledge from evidence collected during user's learning process.