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A Personalized Learning System Based on Semantic Web Technologies

A Personalized Learning System Based on Semantic Web Technologies

Funda Dag, Kadir Erkan
Copyright: © 2011 |Pages: 27
ISBN13: 9781616920081|ISBN10: 1616920084|ISBN13 Softcover: 9781616923747|EISBN13: 9781616920098
DOI: 10.4018/978-1-61692-008-1.ch013
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MLA

Dag, Funda, and Kadir Erkan. "A Personalized Learning System Based on Semantic Web Technologies." Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation and Evaluation, edited by Slavomir Stankov, et al., IGI Global, 2011, pp. 258-284. https://doi.org/10.4018/978-1-61692-008-1.ch013

APA

Dag, F. & Erkan, K. (2011). A Personalized Learning System Based on Semantic Web Technologies. In S. Stankov, V. Glavinic, & M. Rosic (Eds.), Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation and Evaluation (pp. 258-284). IGI Global. https://doi.org/10.4018/978-1-61692-008-1.ch013

Chicago

Dag, Funda, and Kadir Erkan. "A Personalized Learning System Based on Semantic Web Technologies." In Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation and Evaluation, edited by Slavomir Stankov, Vlado Glavinic, and Marko Rosic, 258-284. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-008-1.ch013

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Abstract

In this study, a personalized learning system framework that is KOULearn (Kocaeli University Learning System) has been proposed. It is based on learning object and produces a personalized learning content according to learning style and cognitive knowledge level of an individual. The proposed framework is situated in advanced learning technologies according to educational perspective and is a semantic web application according to development technologies. The proposed framework is aimed to form a personalized learning environment. Three models are included in this system: domain model, user model and adaptation model, respectively. Components of the system have been realized by ontology based knowledge modeling approach. Execution of the personalized learning content structure and learning environment of the system, related to electric education, has been examined by resonance learning module which is constituted considering personalization requirements of the system.

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