Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Learning object retrieval in heterogeneous environments

Learning object retrieval in heterogeneous environments This paper presents a solution to the problem of the search and retrieval digital tagged content in heterogeneous learning object repositories through architecture for intelligent retrieval of educational content in heterogeneous environments (AIREH) framework. This architecture unifies the search and retrieval of objects, thus facilitating the personalised learning search process by filtering and properly classifying learning objects retrieved for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients. The use of federated databases techniques by using an organisation of agents allows those agents to work in a coordinated manner to solve a common problem, allowing the agents to adapt to the constantly changing environment (users, content repositories, etc.). Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organising and sorting search results in a meaningful way for educational content. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Engineering and Technology Inderscience Publishers

Loading next page...
 
/lp/inderscience-publishers/learning-object-retrieval-in-heterogeneous-environments-8NCy9XcrAS

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1476-1289
eISSN
1741-9212
DOI
10.1504/IJWET.2013.055707
Publisher site
See Article on Publisher Site

Abstract

This paper presents a solution to the problem of the search and retrieval digital tagged content in heterogeneous learning object repositories through architecture for intelligent retrieval of educational content in heterogeneous environments (AIREH) framework. This architecture unifies the search and retrieval of objects, thus facilitating the personalised learning search process by filtering and properly classifying learning objects retrieved for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients. The use of federated databases techniques by using an organisation of agents allows those agents to work in a coordinated manner to solve a common problem, allowing the agents to adapt to the constantly changing environment (users, content repositories, etc.). Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organising and sorting search results in a meaningful way for educational content.

Journal

International Journal of Web Engineering and TechnologyInderscience Publishers

Published: Jan 1, 2013

There are no references for this article.