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

Vivien Robinet (1, 2) , Gilles Bisson (3) , Mirta Gordon (3) , Benoît Lemaire (3)

1 Leibniz - IMAG - Laboratoire Leibniz
2 TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
3 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]

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.

Mots clés: Computer-assisted instruction; Machine learning; Education; Mining methods and algorithms

Domaines: Machine Learning [stat.ML]; Environnements Informatiques pour l'Apprentissage Humain; Education; Apprentissage [cs.LG]; Informatique

Vivien Robinet, Gilles Bisson, Mirta Gordon, Benoît Lemaire. Induction of High-level Behaviors from Problem-solving Traces using Machine Learning Tools. IEEE Intelligent Systems, 2007, 22 (4), pp.22. ⟨hal-00370553⟩