Discovery Mechanism for Learning Semantic Web Service

Web Services ◽  
2019 ◽  
pp. 575-596
Author(s):  
Chaker Ben Mahmoud ◽  
Ikbel Azaiez ◽  
Fathia Bettahar ◽  
Faiez Gargouri
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Learning Style ◽  
Learning Objects ◽  
Learning Object ◽  
Traditional Learning ◽  
Semantic Web Service ◽  
Learning Intention ◽  
Learning Path ◽  
E Learning

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.

Discovery Mechanism for Learning Semantic Web Service

2016 ◽  
Vol 12 (1) ◽  
pp. 23-43 ◽  
Author(s):  
Chaker Ben Mahmoud ◽  
Ikbel Azaiez ◽  
Fathia Bettahar ◽  
Faiez Gargouri
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Learning Style ◽  
Learning Objects ◽  
Learning Object ◽  
Traditional Learning ◽  
Semantic Web Service ◽  
Learning Intention ◽  
Learning Path ◽  
E Learning

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.

A Semantic Web Service Architecture for Learing Object Repositories

2011 ◽  
pp. 141-158 ◽  
Author(s):  
José-Manuel Lopez-Cobo ◽  
Sinuhé Arroyo ◽  
Miguel-Angel Sicilia ◽  
Salvador Sanchez
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Learning Objects ◽  
Learning Object ◽  
Learning Technology ◽  
Computational Semantics ◽  
Semantic Web Service ◽  
Service Architecture ◽  
Develop Technology ◽  
Definition Of

The evolution of learning technology standards has resulted in a degree of interoperability across systems that enable the interchange of learning contents and activities. Nonetheless, learning resource metadata does not provide formal computational semantics, which hampers the possibilities to develop technology that automates tasks like learning object selection and negotiation. In this paper, the provision of computational semantics to metadata is addressed from the perspective of the concept of Semantic Web service. An architecture based on the specifications of the WSMO project is described, including the definition of an ontology for learning object metadata, and issues of mediation, all under the perspective of the learning object repository as the central entity in learning object reuse scenarios. The resulting framework serves as a foundation for advanced implementations that consider formal metadata semantics as a mechanism for the automation of tasks related to the interchange of learning objects.

M-Learning through Semantically Annotated Learning Objects and Ontologies

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Reshmy Krishnan
Keyword(s):  
Semantic Web ◽  
Learning Process ◽  
Rapid Development ◽  
Learning Objects ◽  
Learning System ◽  
Learning Object ◽  
Mobile Technologies ◽  
Rich Medium ◽  
Object Structure ◽  
E Learning

Number of mobile subscriptions has increased tremendously due to rapid development of mobile technologies. The performance and accessibility of the e-learning process can be enhanced through mobile devices which is called m-learning. M-learning makes learning resources available anywhere and anytime, provide strong search capabilities, and offers easy interaction features to the learners. M-learning also points the opportunity for interoperability than existing e-learning system. The integration of semantic web in m-learning can improve the efficiency of searching for learning objects and reduce the time and cost of learning process. Semantic web can be integrated with the help of ontologies and learning objects in semantic web. They offer rich medium to assist m-learning via semantic annotated learning objects and shared repositories. Two types of ontologies, such as learning object content ontology and learning object structure ontology are used in this system. These ontologies facilitate the reuse, sharing and retrieval of relevant learning objects which are the backbone of m-learning.

Ontologies in Intelligent Learning Systems

2012 ◽  
pp. 31-48
Author(s):  
Boryana Deliyska ◽  
Peter Manoilov
Keyword(s):  
Semantic Web ◽  
Learning Process ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning Object ◽  
Structural Learning ◽  
Principal Role ◽  
Web Resources ◽  
Human Tutor ◽  
E Learning

The intelligent learning systems provide a direct customized instruction to the learners without intervention of human tutor on the base of Semantic Web resources. The principal role ontologies play in these systems is as an instrument for modeling learning process, learner, learning objects, and resources. In the chapter, a variety of relationships and conceptualizations of ontologies used in the intelligent learning systems are investigated. The utilization of domain and application ontologies in learning object building and knowledge acquisition is represented. The conceptualization of domain ontologies in e-learning is presented by the upper levels of its taxonomies. Moreover, a method and an algorithm intended for generation of application ontologies of structural learning objects (curriculum, syllabus, topic plan, etc.) are developed. Examples of curriculum and syllabus application ontologies are given. Further these application ontologies are used for structural learning object generation.

Building Interoperable Learning Objects Using Reduced Learning Object Metadata

2005 ◽  
Vol 2 (3) ◽  
pp. 299-313
Author(s):  
Mostafa S. Saleh
Keyword(s):  
Semantic Web ◽  
Learning Community ◽  
Learning Objects ◽  
Web Technology ◽  
Learning Object ◽  
Learning Standards ◽  
Semantic Web Technology ◽  
Learning Object Metadata ◽  
E Learning ◽  
Learning Object Repositories

The new e-learning generation depends on Semantic Web technology to produce learning objects. As the production of these components is very costly, they should be produced and registered once, and reused and adapted in the same context or in other contexts as often as possible. To produce those components, developers should use learning standards to describe these objects in order to support interoperability. IEEE Learning Object Metadata (LOM) is the most dominant standard for describing learning objects, in which 76 different elements are used to describe the different aspects of e-learning. Nonetheless, it will still be time consuming to build these learning objects. This paper introduces a model for building Global Interoperable Learning Objects (GILO) for the e-learning community. This is achieved by using a reduced set of the LOM elements, and giving a unique global ID to the learning object. This will enable software agents to query these learning object repositories, to automatically deliver the required material to the e-learning consumer.

scholarly journals The Integrated Environment for Learning Objects Design and Storing in Semantic Web

2018 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Valentina Dagiene ◽  
Daina Gudoniene ◽  
Reda Bartkute
Keyword(s):  
Semantic Web ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning Object ◽  
Learning Methods ◽  
Integrated Learning Systems ◽  
Integrated Environment ◽  
E Learning ◽  
Learning Object Repositories ◽  
Effective Design

There is a variety of tools and environments for Learning Objects (LOs) design and delivery as well as learning object repositories (LOR) but the researchers could not find a repository that includes both functions: creation and storing of LOs. A number of different integrated learning systems are suggested for users that demonstrate the variety of e-learning methods and semantic capabilities. LO repository oer.ndma.lt/lor, that we are going to present, is very friendly and interoperable to use and assure LO design, search in semantic web, adaptation of the re-used objects and storing. There are no more existing LO repositories with the functionality presented by researchers. Transformation of closed education into open one without existence of well-structured, multifunctional and integrated environment becomes problematic. Authors will present an integrated environment for the LO design, search in semantic web, adaptation and storing of newly designed or re-designed LO. Measures will support the transformation of closed education into open and will assure effective design, re-usability and adaptation of LO in the integrated environment.

Multi-Agent Paradigm for Semantic Web Service in E-learning System

2013 ◽  
Vol 5 (4) ◽  
pp. 20-43 ◽  
Author(s):  
Sami Ahmed Mohammed Al-Radaei ◽  
R.B. Mishra
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Learning System ◽  
Traditional Learning ◽  
Learning Materials ◽  
Agent Based ◽  
Composition Methods ◽  
Proposed Model ◽  
E Learning ◽  
Multi Agent

E-learning has become an alternative solution for the traditional learning. There is a need to manage the learning materials in e-learning environments in order to deliver it to learners according to their requirements. Semantic Web Services (SWS) aim at developing a machine understandable and common conceptual framework which share and accumulate concepts from different web service resources to meet a particular objective in question. Different SWS composition methods have been developed for different purposes and objectives. In this paper we have developed an Agent-based SWS composition method using two sets of agents i.e. Service Requester Agent (SRA) and Service Provider Agent (SPA) to represent the user's side and the solution side respectively for the problem of a course composition in e-learning systems. The SRA corresponds to requirements of different ebook/chapter and the SPA corresponds to books containing the relevant and required chapters in courseware. The course composition is primarily based on the important and relevant prime keywords in a courseware. Learning materials and other actors are described semantically in form of ontologies. Also, we present the use of reasoning rule to infer different relations between Agents, ebook/chapter and other actors in the proposed model.

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