Artificial Intelligence
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Inspecting and Visualizing Distributed Bayesian Student Models
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Integration of a Complex Learning Object in a Web-Based Interactive Learning System
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Towards Predictive Modelling of Student Affect from Web-Based Interactions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
A contingency analysis of LEACTIVEMATH's learner model
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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This paper describes the design of the learner modelling component of the LeActiveMath system, which was conceived to integrate modelling of learners' competencies in a subject domain, motivational and affective dispositions and meta-cognition. This goal has been achieved by organising learner models as stacks, with the subject domain as ground layer and competency, motivation, affect and meta-cognition as upper layers. A concept map per layer defines each layer's elements and internal structure, and beliefs are associated to the applications of elements in upper-layers to elements in lower-layers. Beliefs are represented using belief functions and organised in a network constructed as the composition of all layers' concept maps, which is used for propagation of evidence.