Author:
Benlamri Rachid,Zhang Xiaoyun
Abstract
AbstractAs mobile technologies become widespread, new challenges are facing the research community to develop lightweight learning services adapted to the learner’s profile, context, and task at hand. This paper attempts to solve some of these challenges by proposing a knowledge-driven recommender for mobile learning on the Semantic Web. The contribution of this work is an approach for context integration and aggregation using an upper ontology space and a unified reasoning mechanism to adapt the learning sequence and the learning content based on the learner’s activity, background, used technology, and surrounding environment. Whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The proposed system has been implemented and tested on various mobile devices. The experimental results show many learning scenarios to demonstrate the usefulness of the system in practice.
Publisher
Springer Science and Business Media LLC
Reference61 articles.
1. Teo CB, Gay RKL: A knowledge-driven model to personalized e-Learning. ACM J Educ Resour Comput 2006, 6(1):39–53.
2. Davies J, Lytras M, Sheth A: Semantic web based knowledge management. IEEE Internet Comput 2007, 10: 14–16.
3. Zouaq A, Nkambou R: A Survey of Domain Ontology Engineering: Methods and Tools. In Advances in Intelligent Tutoring Systems. Springer-Verlag, Berlin Heidelberg; 2010:103–119.
4. Wu J: An Ontology-Based Context Awareness Approach in Autonomous Mobile Learning. In Proc. of Int. Conference on e-Business and e-Government ICEE2010, Guangzhou, China; 2010.
5. Henze N, Dolog P, Nejdl W: Reasoning and ontologies for personalized e-Learning in the semantic web. Educ Technol Soc 2004, 7(4):82–97.
Cited by
33 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献