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

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HAL-SHS : sciences de l'éducation

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Ce document est lié à :
info:eu-repo/semantics/altIdentifier/arxiv/0904.0776

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Centre pour la communication scientifique directe

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info:eu-repo/semantics/OpenAccess

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Computer-assisted instruction Machine learning Education Mining methods and algorithms

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Learning process Scholarship Learning

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Vivien Robinet et al., « Induction of High-level Behaviors from Problem-solving Traces using Machine Learning Tools », HAL-SHS : sciences de l'éducation, ID : 10670/1.4btty6

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This paper applies machine learning techniques to student modeling. It presents 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. Basic actions are encoded into sets of domain-dependent attribute-value patterns called cases. Then a domain-independent hierarchical clustering identifies what we call general attitudes, yielding automatic diagnosis expressed in natural language, addressed in principle to teachers. The method can be applied to individual students or to entire groups, like a class. We exhibit examples of this system applied to thousands of students' actions in the domain of algebraic transformations.

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