Cognitive modeling and intelligent tutoring
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
High-Level Student Modeling with Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Predicting correctness of problem solving in ITS with a temporal collaborative filtering approach
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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This article applies machine-learning techniques to student modeling, presenting a method for discovering high-level student behaviors from a very large set of low-level traces corresponding to problem-solving actions in a learning environment. The system encodes basic actions into sets of domain-dependent attribute-value patterns. Then, a domain-independent hierarchical clustering identifies high-level abilities, yielding natural-language diagnoses for teachers. The method can be applied to individual students or to entire groups, such as a class. The system was applied to the actions of thousands of students in the domain of algebraic transformations. This article is part of a special issue on intelligent educational systems.