Title: Using a hybrid AI approach for exercise difficulty level adaptation

Authors: Constantinos Koutsojannis, Grigorios Beligiannis, Ioannis Hatzilygeroudis, Constantinos Papavlasopoulos, Jim Prentzas

Addresses: Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Hellas (Greece). ' Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Hellas (Greece). ' Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Hellas (Greece). ' Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Hellas (Greece). ' Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, Hellas (Greece)

Abstract: An intelligent and adaptive web-based education system is presented. The system uses a hybrid AI approach, a combination of an expert systems approach and a genetic algorithm approach, to determine the difficulty levels of the provided exercises. The genetic algorithm is used to extract some kind of rules from the data acquired from the interactions of the students. Those rules are used to modify expert rules provided by the Tutor. In this way, feedback from the students is taken into account for determination of the difficulty levels of the questions/exercises. Experimental results show the validity of the method.

Keywords: intelligent web-based education; intelligent e-learning; exercise adaptation; expert systems; genetic algorithms; GAs; hybrid intelligent systems; HISs; online learning; artificial intelligence.

DOI: 10.1504/IJCEELL.2007.015042

International Journal of Continuing Engineering Education and Life-Long Learning, 2007 Vol.17 No.4/5, pp.256 - 272

Published online: 06 Sep 2007 *

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