Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Student Modeling and Mastery Learning in a Computer-Based Proramming Tutor
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
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
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
Inspectable Bayesian student modelling servers in multi-agent tutoring systems
International Journal of Human-Computer Studies
Modeling individual and collaborative problem-solving in medical problem-based learning
User Modeling and User-Adapted Interaction
A multifactor approach to student model evaluation
User Modeling and User-Adapted Interaction
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
An Intelligent SQL Tutor on the Web
International Journal of Artificial Intelligence in Education
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Modelling Learning in an Educational Game
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
A computational framework for granularity and its application to educational diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Using similarity to infer meta-cognitive behaviors during analogical problem solving
UM'05 Proceedings of the 10th international conference on User Modeling
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Evaluating the integration of fuzzy logic into the student model of a web-based learning environment
Expert Systems with Applications: An International Journal
Using Bayesian networks to improve knowledge assessment
Computers & Education
Problem solving learning environments and assessment: A knowledge space theory approach
Computers & Education
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
A control system proposal for engineering education
Computers & Education
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Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.