Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
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
The research on the technique of online experimental about SQLTutor
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Interaction analysis for adaptive user interfaces
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
A proposal for student modeling based on ontologies and diagnosis rules
Expert Systems with Applications: An International Journal
A reference model for adaptive visualization systems
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
Expert Systems with Applications: An International Journal
Supporting the review of student proposal drafts in information technologies
Proceedings of the 13th annual conference on Information technology education
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We have developed an intelligent tutoring system coupled with a virtual laboratory, which constitute a semi-open learning environment. This environment provides the student with the opportunity to learn through exploration within a virtual laboratory, while achieving the expected learning objectives. The key element of this environment is a novel representation for the student model based on probabilistic relational models. This student model has several advantages: flexibility, user adaptability, high modularity and facilities for model construction for different scenarios. The model keeps track of the students' knowledge at different levels of granularity, combining the performance and exploration behavior in several experiments, to decide the best way to guide the student in following experiments, and to recategorize the students based on the results. We have implemented a tutor for a virtual robotics laboratory, and evaluated the system with an initial group of 20 students. The results show that students who explore the virtual environment with the help of the tutor have a better academic skills, and also that the predictions of the student model are generally accurate.