An architecture of agent-based multi-layer interactive e-learning and e-testing platform

An architecture of agent-based multi-layer interactive e-learning and e-testing platform E-learning is the synthesis of multimedia and social media platforms powered by Internet and mobile technologies. Great popularity of e-learning is encouraging governments and educational institutions to adopt and develop e-learning cultures in societies in general and universities in particular. In traditional e-learning systems, all components (agents and services) are tightly coupled into a single system. In this study, we propose a new architecture for e-learning with two subsystems, namely, e-learning and e-testing. The motivation of the research is to improve the effectiveness of the learning process by extracting relevant features for elastic learning and testing process. We employ a multi-agent system because it contains five-layer architecture, including agents at various levels. We also propose a novel method for updating content through question and answer between e-learners and intelligent agents. To achieve optimization, we design a system that applies various technologies, which guarantee various dynamic features for e-learning systems, such as intelligence, distributed nature, adaptive attitude, interaction, accessibility, and security. Agent assisted e- learning enable the users to collect the quantifiable and sensible material; examine, and distribute customized knowledge from multiple e-learning sources. Intelligent agents, being program helper or assistants, are deployed at different levels of abstraction in this architecture to manage information overload and create environment for learners. Moreover, this proposed system is designed by keeping in view several characteristics specific to e-learning system such as interaction, personalization, adaptation, intelligence, interoperability, accessibility and security. The architecture is designed to support instructional design, to retrieve relevant learning materials, to process and analyses data to enable meaningful e-learning recommendations for instructors and learners by considering all issues that existing e-learning architectures don’t address. Most of the existing e-learning architectures don’t consider all the features in a single system so there is a need for a generic architecture that should support all the features to make the e-learning system more efficient. The outcome of this approach is to provide flexible and lightweight systems for e-learning environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

An architecture of agent-based multi-layer interactive e-learning and e-testing platform

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Publisher
Springer Netherlands
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-0121-9
Publisher site
See Article on Publisher Site

Abstract

E-learning is the synthesis of multimedia and social media platforms powered by Internet and mobile technologies. Great popularity of e-learning is encouraging governments and educational institutions to adopt and develop e-learning cultures in societies in general and universities in particular. In traditional e-learning systems, all components (agents and services) are tightly coupled into a single system. In this study, we propose a new architecture for e-learning with two subsystems, namely, e-learning and e-testing. The motivation of the research is to improve the effectiveness of the learning process by extracting relevant features for elastic learning and testing process. We employ a multi-agent system because it contains five-layer architecture, including agents at various levels. We also propose a novel method for updating content through question and answer between e-learners and intelligent agents. To achieve optimization, we design a system that applies various technologies, which guarantee various dynamic features for e-learning systems, such as intelligence, distributed nature, adaptive attitude, interaction, accessibility, and security. Agent assisted e- learning enable the users to collect the quantifiable and sensible material; examine, and distribute customized knowledge from multiple e-learning sources. Intelligent agents, being program helper or assistants, are deployed at different levels of abstraction in this architecture to manage information overload and create environment for learners. Moreover, this proposed system is designed by keeping in view several characteristics specific to e-learning system such as interaction, personalization, adaptation, intelligence, interoperability, accessibility and security. The architecture is designed to support instructional design, to retrieve relevant learning materials, to process and analyses data to enable meaningful e-learning recommendations for instructors and learners by considering all issues that existing e-learning architectures don’t address. Most of the existing e-learning architectures don’t consider all the features in a single system so there is a need for a generic architecture that should support all the features to make the e-learning system more efficient. The outcome of this approach is to provide flexible and lightweight systems for e-learning environments.

Journal

Quality & QuantitySpringer Journals

Published: Oct 31, 2014

References

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