Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
THEMIS: a nonmonotonic inductive student modeling system
Journal of Artificial Intelligence in Education
Uncertainty Management in Information Systems: From Needs to Solutions
Uncertainty Management in Information Systems: From Needs to Solutions
To Contradict is Human - Student Modeling of Inconsistency
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
An endorsement-based approach to student modeling for planner-controlled tutors
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Personis: A Server for User Models
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Personalised e-learning through an educational virtual reality game using Web services
Multimedia Tools and Applications
The Logic-ITA in the Classroom: A Medium Scale Experiment
International Journal of Artificial Intelligence in Education
Privacy-enhanced web personalization
The adaptive web
Tackling HCI challenges of creating personalised, pervasive learning ecosystems
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
User model interoperability: a survey
User Modeling and User-Adapted Interaction
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
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This paper describes the accretion representation for scrutable student modelling. Essentially, the representation maintains a times-tamped collection of the evidence about each component of the student model. This is interpreted by a resolver at the time that a teaching program needs to determine the value of parts of the model.The accretion representation treats external evidence as ground assumptions which are normally kept long term. By contrast, the student modelling system's internal inferences are handled quite differently. This approach supports long-term modelling of the learner's knowledge and other characteristics. It was used in large scale modelling and coaching experiments for knowledge of a text editor.An important concern for the representation is to support scrutability of the student model. This notion is explained in the paper and linked to the design of the accretion representation.