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This paper applies machine learning techniques to student modeling. Itpresents a method for discovering high-level student behaviors from a verylarge set of low-level traces corresponding to problem-solving actions in alearning environment. Basic actions are encoded into sets of domain-dependentattribute-value patterns called cases. Then a domain-independent hierarchicalclustering identifies what we call general attitudes, yielding automaticdiagnosis expressed in natural language, addressed in principle to teachers.The method can be applied to individual students or to entire groups, like aclass. We exhibit examples of this system applied to thousands of students'actions in the domain of algebraic transformations.

Induction of High-level Behaviors from Problem-solving Traces using Machine Learning Tools