Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Elements of information theory
Elements of information theory
Attribute-mastery patterns from rule space as the basis for student models in algebra
International Journal of Human-Computer Studies
Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Machine Learning
Learning in graphical models
A tutorial on learning with Bayesian networks
Learning in graphical models
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
An Experience in Learning about Learning Composite Concepts
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Learned student models with item to item knowledge structures
User Modeling and User-Adapted Interaction
Learning Bayesian Networks
Cognitively Informed Systems: Utilizing Practical Approaches to Enrich Information Presentation and Transfer
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive testing for hierarchical student models
User Modeling and User-Adapted Interaction
Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effects Methods
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Responsibility and blame: a structural-model approach
Journal of Artificial Intelligence Research
Bayes nets in educational assessment: Where the numbers come from
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning students' learning patterns with support vector machines
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Introducing prerequisite relations in a multi-layered bayesian student model
UM'05 Proceedings of the 10th international conference on User Modeling
A bayes net toolkit for student modeling in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Learning how students learn with bayes nets
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
From neural networks to the brain: autonomous mental development
IEEE Computational Intelligence Magazine
Performance comparison of item-to-item skills models with the IRT single latent trait model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Composite concepts result from the integration of multiple basic concepts by students to form highlevel knowledge, so information about how students learn composite concepts can be used by instructors to facilitate students' learning, and the ways in which computational techniques can assist the study of the integration process are therefore intriguing for learning, cognition, and computer scientists. We provide an exploration of this problem using heuristic methods, search methods, and machine-learning techniques, while employing Bayesian networks as the language for representing the student models. Given experts' expectation about students and simulated students' responses to test items that were designed for the concepts, we try to find the Bayesian-network structure that best represents how students learn the composite concept of interest. The experiments were conducted with only simulated students. The accuracy achieved by the proposed classification methods spread over a wide range, depending on the quality of collected input evidence. We discuss the experimental procedures, compare the experimental results observed in certain experiments, provide two ways to analyse the influences of Q-matrices on the experimental results, and we hope that this simulation-based experience may contribute to the endeavours in mapping the human learning process.