Improving Student Model for Individualized Learning
Computer-based educational environments, like Intelligent Tutoring Systems (ITSs), have been used to enhance human learning. These environments aim at improving student achievement by providing individualized instructions. It has been recognized that individualized learning is more effective than the conventional learning. Student models which are used to capture student knowledge underlie the individualized learning. In recent decades, various competing student models have been proposed. However, some diagnostic information in student behaviors is usually ignored by these models. Furthermore, to individualize learning paths, student models should capture prerequisite structures of fine-grained skills. However, acquiring skill structures requires much knowledge engineering effort. We improve student models for individualized learning with respect to the two aspects. On one hand, in order to improve the diagnostic ability of a student model, we introduce the diagnostic feature—student error patterns. To deal with the noise in student performance data, we extend a sound probabilistic model to incorporate erroneous responses. The results show that the diagnostic feature improves the prediction accuracy of student models. On the other hand, we target on discovering prerequisite structures of skills from student performance data. It is a challenging task, since student knowledge of a skill is a latent variable. We propose a two-phase method to discover skill structure from noisy observations. Our method is validated on simulated data and real data. In addition, we verify that prerequisite structures of skills can improve the accuracy of a student model.
Defence : 09/29/2015 - 13h30 - Site Jussieu, 25-26/105 Jury members : M. Serge GARLATTI, Télécom Bretagne, [Rapporteur]
Mme. Nathalie GUIN, Université Lyon 1, [Rapporteuse]
Mme. Vanda LUENGO, UPMC
Mme. Naïma El-Kechaï, Pharma Biot'Expert
M. Jean-Marc LABAT, UPMC
M. Pierre-Henri Wuillemin, UPMC