Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Capability, potential and collaborative assistance
UM '99 Proceedings of the seventh international conference on User modeling
Broader Bandwidth in Student Modeling: What if ITS were "Eye"TS?
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
The Collaborative System with Situated Agents for Activating Observation Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Further Results from the Evaluation of an Intelligent Computer Tutor to Coach Self-Explanation
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Conceptual and Meta Learning During Coached Problem Solving
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Designing an adaptive learning module to teach software testing
Proceedings of the 37th SIGCSE technical symposium on Computer science education
"How do you know that I don't understand?" A look at the future of intelligent tutoring systems
Computers in Human Behavior
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
Designs for explaining intelligent agents
International Journal of Human-Computer Studies
Generating tailored examples to support learning via self-explanation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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We present an adaptive interface designed to provide tailored support for the understanding of written instructional material. The interface relies on a user model based on a Bayesian network, that assesses users' understanding as users read the instructional material and try to understand it by generating explanations to themselves. The user model's assessment is used by the interface to generate tailored scaffolding of further user's explanations that can improve the user's comprehension. After illustrating how the Bayesian user model assesses understanding from the user's explanations and from latency data on the user's attention, we discuss initial results on the effectiveness of the interface's adaptive interventions.