Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Bayesian Networks to Implement Adaptivity in Mobile Learning
SKG '06 Proceedings of the Second International Conference on Semantics, Knowledge, and Grid
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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An e-learning system must be capable of gathering and correctly evaluating a learner's profile and performance data in order to deliver individualised instruction to the learner. However, the learner's data can be incomplete, inaccurate and/or contradictory. They can also be correlated. This paper aims to alleviate these data problems by evaluating the data of each new learner probabilistically based on the data of earlier learners. Our probabilistic rule model allows our system to apply adaptation rules to examine learners' data at various stages of a learning activity, and determine the suitable actions to take to personalise the instruction. Adaptation rules are processed by a rule engine and a Bayesian model processor to achieve adaptive content search and selection, adaptive processing of learning objects, and continuous improvement on the accuracy of learner evaluation.