Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Learning Students’ Learning Patterns with Support Vector Machines
verfasst von : Chao-Lin Liu
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Using Bayesian networks as the representation language for student modeling has become a common practice. Many computer-assisted learning systems rely exclusively on human experts to provide information for constructing the network structures, however. We explore the possibility of applying mutual information-based heuristics and support vector machines to learn how students learn composite concepts, based on students’ item responses to test items. The problem is challenging because it is well known that students’ performances in taking tests do not reflect their competences faithfully. Experimental results indicate that the difficulty of identifying the true learning patterns varies with the degree of uncertainty in the relationship between students’ performances in tests and their abilities in concepts. When the degree of uncertainty is moderate, it is possible to infer the unobservable learning patterns from students’ external performances with computational techniques.