Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Teaching-Learning by Means of a Fuzzy-Causal User Model
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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We have developed a novel student model based on probabilistic relational models (PRMs). This model combines the advantages of Bayesian networks and object-oriented systems. It facilitates knowledge acquisition and makes it easier to apply the model for different domains. The model is oriented towards virtual laboratories, in which a student interacts by doing experiments in a simulated or remote environment. It represents 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 the next experiments. Based on this model, we have developed tutors for virtual laboratories in different domains. An evaluation of with a group of students, show a significant improvement in learning when a tutor based on the PRM model is incorporated to a virtual robotics lab.