Conceptual clustering of structured objects: a goal-oriented approach
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Concept formation over problem-solving experience
Concept formation knowledge and experience in unsupervised learning
Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning
Machine Learning - Special issue on multistrategy learning
Acquiring and Combining Overlapping Concepts
Machine Learning - Special issue on computational models of human learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Toward the automatic discovery of misconceptions
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Multistrategy Discovery and Detection of Novice Programmer Errors
Machine Learning - Special issue on multistrategy learning
User Modeling and User-Adapted Interaction
Toward the automatic discovery of misconceptions
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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Most conceptual clustering systems rely solely on data to form concepts without supervision; the few that exploit causalities in the background knowledge do so only after the completion of a similarity-based learning phase. In this paper, we describe a multistrategy misconception discovery system, MMD, that utilizes data and theory in a more tightly coupled way. The integration of similarity- and causality-based learning in MMD is shown to be essential for the automatic construction of accurate and meaningful misconceptions that account for errors in novice behavior.