Authors: Soufiane Boulehouache; Ramdane Maamri; Zaidi Sahnoun
Addresses: Faculty of Sciences, Department of Computer Science, Université 20 Août 1955-Skikda, B.P.26 route El-Hadaiek Skikda, 21000, Algeria ' LIRE Laboratory, University of Constantine 2 – Abdelhamid Mehri, Nouvelle Ville Ali Mendjeli, BP: 67A, Constantine, Algeria ' LIRE Laboratory, University of Constantine 2 – Abdelhamid Mehri, Nouvelle Ville Ali Mendjeli, BP: 67A, Constantine, Algeria
Abstract: Adaptive human learning systems (AHLSs) are important tools to personalise learning. However, the used domain representation formalisms lack the needed precision and flexibility those make the domains efficiently adaptable and intensively reusable. To address this issue, we propose a component based knowledge domain for an AHLS that aims to improve the adapting efficiency and provides intensive reuse of the pre-built (sub) knowledge domains. To show the feasibility and the benefits of the proposed AHLS, a prototype that experiments the explanation variants method is implemented. So, unlike the other, our AHLS achieves the adapted learning by (re)selecting and sequencing the appropriate linear combination of the component variants explaining the corresponding concepts. Also, as a more challenging task, to get a compromised solution of the conflicting learning goals and to reduce the substantial overhead, the adapting is formulated as a multi-objective component variants selection problem and it is implemented using Genetic Algorithms.
Keywords: AHLSs; adaptive human learning systems; knowledge domains; explanation variants; knowledge reuse; fractal component model; genetic algorithms; adaptive learning; personalised learning; personalisation.
International Journal of Knowledge and Learning, 2015 Vol.10 No.4, pp.336 - 363
Accepted: 13 Aug 2015
Published online: 05 Jul 2016 *