Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning

scholarly journals The Model for Learning Objects Design Based on Semantic Technologies

2017 ◽  
Vol 12 (2) ◽  
pp. 227
Author(s):  
Daina Gudoniene ◽  
Rytis Maskeliunas ◽  
Danguole Rutkauskiene
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Learning Environments ◽  
Information And Communication Technologies ◽  
Learning Objects ◽  
Semantic Web Services ◽  
Innovative Learning ◽  
Modern Learning ◽  
E Learning ◽  
Integration Of Learning

The paper presents a comparison of state of the art methods and techniques on implementation of learning objects (LO) in the field of information and communication technologies (ICT) using semantic web services for e-learning. The web can serve as a perfect technological environment for individualized learning which is often based on interactive learning objects. This allows learners to be uniquely identified, content to be specifically personalized, and, as a result, a learner’s progress can be monitored, supported, and assessed. While a range of technological solutions for the development of integrated e-learning environments already exists, the most appropriate solutions require further improvement on implementation of novel learning objects, unification of standardization and integration of learning environments based on semantic web services (SWS) that are still in the early stages of development. This paper introduces a proprietary architectural model for distributed e-learning environments based on semantic web services (SWS), enabling the implementation of a successive learning process by developing innovative learning objects based on modern learning methods. A successful technical implementation of our approach in the environment of Kaunas University of Technology is further detailed and evaluated.

Intelligence in Web Technology

2013 ◽  
pp. 739-757
Author(s):  
Sourav Maitra ◽  
A. C. Mondal
Keyword(s):  
Semantic Web ◽  
Science Research ◽  
Research Area ◽  
Web Technology ◽  
Clear Understanding ◽  
Web Technologies ◽  
Research Areas ◽  
Computer Science Research ◽  
E Learning ◽  
New Research

End users also start days with Internet. This has become the scenario. One of the most burgeoning needs of computer science research is research on web technologies and intelligence, as that has become one of the most emerging nowadays. A big area of other research areas like e-marketing, e-learning, e-governance, searching technologies, et cetera will be highly benefited if intelligence can be added to the Web. The objective of this chapter is to create a clear understanding of Web technology research and highlight the ways to implement Semantic Web. The chapter also discusses the tools and technologies that can be applied to develop Semantic Web. This new research area needs enough care as sometimes data are open. Thus, software engineering issues are also a focus.

Web 3.0 and E-Learning

2015 ◽  
pp. 101-123 ◽  
Author(s):  
Amit Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Learning Goals ◽  
Learning Behavior ◽  
Defining Moment ◽  
Web 3.0 ◽  
E Learning ◽  
Future Of Education ◽  
The Web

The annals of the Web have been a defining moment in the evolution of education and e-Learning. The evolution of Web 1.0 almost three decades ago has been a precursor to Web 3.0 that has reshaped education and learning today. The evolution to Web 3.0 has been synonymous with “Semantic Web” or “Artificial Intelligence” (AI). AI makes it possible to deliver custom content to the learners based on their learning behavior and preferences. As a result of these developments, the learners have been empowered and have at their disposal a range of Web tools and technology powered by AI to pursue and accomplish their learning goals. This chapter traces the evolution and impact of Web 3.0 and AI on e-Learning and its role in empowering the learner and transforming the future of education and learning. This chapter will be of interest to educators and learners in exploring techniques that improve the quality of education and learning outcomes.

E-Learning and the Semantic Web

2015 ◽  
pp. 66-100 ◽  
Author(s):  
Raadila Bibi Mahmud Hajee Ahmud-Boodoo
Keyword(s):  
Higher Education ◽  
Semantic Web ◽  
Success Factors ◽  
Comprehensive Analysis ◽  
Learning Systems ◽  
Education Sector ◽  
Learning Models ◽  
Semantic Web Technology ◽  
Critical Success ◽  
E Learning

A number of 3.0 e-learning systems have been proposed in the literature to capture the numerous benefits that the Semantic Web has to offer to the higher education sector. These 3.0 e-learning systems identify some essential Semantic Web characteristics that are either discussed as stand-alone factors or tend to revolve around the complexities of the Semantic Web technology and its implementation, often disregarding users' needs. Conversely, a comprehensive analysis of e-learning models for higher education in the literature revealed several Critical Success Factors (CSFs) that are relevant to the Semantic Web but often overlooked in 3.0 e-learning models. Consequently, this chapter provides an overview of the CSFs of e-learning relevant to 3.0 e-learning systems as well as an overview of the main Semantic Web characteristics for e-learning to define a new and combined set of 3.0 e-learning characteristics that will holistically represent 3.0 e-learning systems capturing the needs and expectations of users. The new initial 3.0 e-learning model proposed is evaluated within the higher education sector in Mauritius.

Web 3.0 and E-Learning

2016 ◽  
pp. 41-62
Author(s):  
Amit Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Learning Goals ◽  
Learning Behavior ◽  
Defining Moment ◽  
Web 3.0 ◽  
E Learning ◽  
Future Of Education ◽  
The Web

The annals of the Web have been a defining moment in the evolution of education and e-Learning. The evolution of Web 1.0 almost three decades ago has been a precursor to Web 3.0 that has reshaped education and learning today. The evolution to Web 3.0 has been synonymous with “Semantic Web” or “Artificial Intelligence” (AI). AI makes it possible to deliver custom content to the learners based on their learning behavior and preferences. As a result of these developments, the learners have been empowered and have at their disposal a range of Web tools and technology powered by AI to pursue and accomplish their learning goals. This chapter traces the evolution and impact of Web 3.0 and AI on e-Learning and its role in empowering the learner and transforming the future of education and learning. This chapter will be of interest to educators and learners in exploring techniques that improve the quality of education and learning outcomes.

Collaborative E-Learning Using Semantic Course Blog

2009 ◽  
pp. 463-472
Author(s):  
Lai-Chen Lu ◽  
Ching-Long Yeh
Keyword(s):  
Semantic Web ◽  
Learning Technology ◽  
Learning Efficiency ◽  
Learning Framework ◽  
E Learning

Collaborative e-learning delivers many enhancements to e-learning technology; it enables students to collaborate with each other and improves their learning efficiency. Semantic blog combines semantic Web and blog technology that users can import, export, view, navigate, and query the blog. We developed a semantic course blog for collaborative e-learning. Using our semantic course blog, instructors can import the lecture course. Students can team up for projects, ask questions, mutually discuss problems, take the comments, support answers, and query the blog information. This semantic course blog provided a platform for collaborative e-learning framework. In this chapter, we described some collaborative e-learning and semantic blog technology, and then we introduced functions, implementation and how collaborative e-learning appears in semantic course blog.

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