A component-based knowledge domain model for adaptive human learning systems Online publication date: Tue, 05-Jul-2016
by Soufiane Boulehouache; Ramdane Maamri; Zaidi Sahnoun
International Journal of Knowledge and Learning (IJKL), Vol. 10, No. 4, 2015
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge and Learning (IJKL):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com