Extending Domain Theories: Two Case Studies in Student Modeling
Machine Learning
Education and technology: What do we know? And where is AI?
AI Communications
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Automatic student modeling and bug library construction using theory refinement
Automatic student modeling and bug library construction using theory refinement
Learning Logical Definitions from Relations
Machine Learning
Some challenges for intelligent tutoring systems
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Multi Level Knowledge in Modeling Qualitative PhysicsLearning
Machine Learning - Special issue on multistrategy learning
Adaptive Techniques for Universal Access
User Modeling and User-Adapted Interaction
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Extracting student models for intelligent tutoring systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Computer-aided tracing of children's physics learning: a teacher oriented view
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Theory refinement systems developed in machine learning automatically modify a knowledge base to render it consistent with a set of classified training examples. We illustrate a novel application of these techniques to the problem of constructing a student model for an intelligent tutoring system (ITS). Our approach is implemented in an ITS authoring system called ASSERT which uses theory refinement to introduce errors into an initially correct knowledge base so that it models incorrect student behavior. The efficacy of the approach has been demonstrated by evaluating a tutor developed with ASSERT with 75 students tested on a classification task covering concepts from an introductory course on the C++ programmm. g Ia nguage. The system produced reasonably accurate models and students who received feedback based on these models performed significantly better on a post test than students who received simple reteaching.