Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Adaptation of Problem Presentation and Feedback in an Intelligent Mathematics Tutor
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Two-Phase Updating of Student Models Based on Dynamic Belief Networks
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Student Modeling from Conversational Test Data: A Bayesian Approach Without Priors
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Simultaneous Evaluation of Multiple Topics in SIETTE
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
CLARISSE: A Machine Learning Tool to Initialize Student Models
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
Information-based item selection with blocking strategy based on a Bayesian network
EDUCATION'10 Proceedings of the 7th WSEAS international conference on Engineering education
Information-based item selection with blocking strategy based on a Bayesian network
WSEAS Transactions on Information Science and Applications
Bayesian student models based on item to item knowledge structures
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
A scalable solution for adaptive problem sequencing and its evaluation
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Introducing prerequisite relations in a multi-layered bayesian student model
UM'05 Proceedings of the 10th international conference on User Modeling
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In this paper we present an integrated theoretical approach for student modelling based on an Adaptive Bayesian Network. A mathematical formalization of the Adaptive Bayesian Network is provided, and new question selection criteria presented. Using this theoretical framework, a tool to assist in the diagnosis process has been implemented. This tool allows the definition of Bayesian Adaptive Tests in an easy way: the only specifications required are a curriculum-based structured domain (together with a set of weights) and a set of questions about the domain (the item pool), which will be internally converted into a Bayesian Network. In this way, we intend to make available this theoretically sound technology to educators, minimizing the knowledge engineering effort required.