Multistrategy Discovery and Detection of Novice Programmer Errors
Machine Learning - Special issue on multistrategy learning
Multi Level Knowledge in Modeling Qualitative PhysicsLearning
Machine Learning - Special issue on multistrategy learning
User Modeling and User-Adapted Interaction
On Application of Rough Data Mining Methods to Automatic Construction of Student Models
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
A Domain Theory Extension of a Student Modeling System for Pascal Programming
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Developing process models as summaries of HCI action sequences
Human-Computer Interaction
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
A novel application of theory refinement to student modeling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A data-driven technique for misconception elicitation
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
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By its very nature, artificial intelligence is concerned with investigating topics that are ill-defined and ill-understood. This paper describes two approaches to expanding a good but incomplete theory of a domain. The first uses the domain theory as far as possible and fills in specific gaps in the reasoning process, generalizing the suggested missing steps and adding them to the domain theory. The second takes existing operators of the domain theory and applies perturbations to form new plausible operators for the theory. The specific domain to which these techniques have been applied is high-school algebra problems. The domain theory is represented as operators corresponding to algebraic manipulations, and the problem of expanding the domain theory becomes one of discovering new algebraic operators. The general framework used is one of generate and test—generating new operators for the domain and using tests to filter out unreasonable ones. The paper compares two algorithms, INFER and MALGEN, examining their performance on actual data collected in two Scottish schools and concluding with a critical discussion of the two methods.