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Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools
July/August 2007 (vol. 22 no. 4)
pp. 22-30
Vivien Robinet, TIMC-IMAG Laboratory
Gilles Bisson, TIMC-IMAG Laboratory
Mirta B. Gordon, TIMC-IMAG Laboratory
Benoît Lemaire, TIMC-IMAG Laboratory
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.
Index Terms:
computer-assisted instruction, machine learning, education, data mining
Citation:
Vivien Robinet, Gilles Bisson, Mirta B. Gordon, Benoît Lemaire, "Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools," IEEE Intelligent Systems, vol. 22, no. 4, pp. 22-30, July-Aug. 2007, doi:10.1109/MIS.2007.73
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