Semantic web and distance education
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The document discusses the potential benefits of semantic web technologies for distance learning and education. It describes how the semantic web aims to make web content machine-understandable to improve search, integration and reduce errors. Key technologies include semantic markup, metadata, ontologies and ontology languages. Semantic web technologies could help address challenges in online learning environments like communication, collaboration and personalized services. The document recommends universities invest in semantic technologies to gain benefits for distance education.
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Semantic Technologies in Learning Environments
Invited talk delivered in the scope of an open online course: Introduction to Learning and Knowledge Analytics
Details about the course, and the recorded presentation can be found at
http://www.learninganalytics.net/?page_id=71
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Web 3.0 is an evolving extension of the current web environme bnt. Information in web 3.0 can be collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning experience to the students. Web 3.0 is 3D, media centric and semantic. Web based learning has been on
high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the queries by the students. Completely Interactive learner’s query determine the customization of the intelligent tutor. This paper analyses the Web 3.0 learning environment attributes. A Maximum spanning
tree model for the personalized web based collaborative learning is designed.
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Web 3.0 is an evolving extension of the current web environme bnt. Information in web 3.0 can be
collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning
experience to the students. Web 3.0 is 3D, media centric and semantic. Web based learning has been on
high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the
queries by the students. Completely Interactive learner’s query determine the customization of the
intelligent tutor. This paper analyses the Web 3.0 learning environment attributes. A Maximum spanning
tree model for the personalized web based collaborative learning is designed.
Developing a Collaborative Multimedia mLearning Environment
This document presents a framework for designing a collaborative multimedia mobile learning (mLearning) environment based on JXTA infrastructure and SVG-RDF multimedia learning objects. It discusses developing learning objects using SVG and RDF to represent multimedia in a way that can be used on mobile devices. It also discusses using JXTA and JXTA4JMS APIs to provide a peer-to-peer networking infrastructure where students can collaborate using their mobile devices.
Miller motte college digital library
The document outlines Miller-Motte College's plans to develop a digital library to provide students and faculty with anytime, anywhere access to learning materials. The digital library will bridge gaps between class time and independent study by making faculty-curated materials available online. The college aims to seamlessly integrate print and digital collections while increasing students' research skills. Key challenges include copyright compliance and developing an easy-to-use interface tailored for students and faculty. Plans also include ongoing evaluation of the digital library's impact and usability.
Semantic Web Technologies for the Integration of Learning Tools and Context-a...
Presentation of the ISWC 2009 paper:
One of the main software engineers’ competencies, solving software problems, is most effectively acquired through an active examination of learning resources and work on real-world examples in small development teams. This obviously indicates a need for an integration of several existing learning tools and systems in a common collaborative learning environment, as well as advanced educational services that provide students with right in time advice about learning resources and possible collaboration partners. In this paper, we present how we developed and applied a common ontological foundation for the integration of different existing learning tools and systems in a common learning environment called DEPTHS (Design Patterns Teaching Help System). In addition, we present a set of educational services that leverages semantic rich representation of learning resources and students’ interaction data to recommend resource relevant for students’ current learning context.
http://data.semanticweb.org/papers/iswc/2009/in-use/paper170.pdf
http://dx.doi.org/10.1007/978-3-642-04930-9_54
D04 06 2438
This document discusses applying semantic web technologies to enhance the services of e-learning systems. It proposes developing a semantic learning management system (S-LMS) based on technologies like XML, RDF, OWL and SPARQL to automate and accurately search for information on e-learning systems like Moodle. The S-LMS would add semantic capabilities to allow students to search for learning resources based on semantics and provide personalized, customized content tailored to individual needs. It presents applying ontologies and metadata to Moodle in order to define domains and describe learning content in a way that improves search, interoperability and reusability of educational resources.
Semantic Wiki
1. The study examines how semantic wikis can be used by application service providers (ASPs) as a knowledge management system for managing software artifacts. Semantic wikis offer capabilities like advanced search and collaboration that traditional systems lack.
2. A literature review found that semantic wikis can effectively support knowledge sharing of software artifacts through features like concept-based search and rich content navigation. They also allow information to be shared across boundaries.
3. Evaluating requirements for documentation found semantic wikis can address issues traditional wikis cannot, like understanding architectures. Recommendations include ASPs researching and training on semantic wikis to better manage software artifacts.
Semantic Technologies in Learning Analytics
My presentation at the pre-conference workshop of the 1st International Conference on Learning Analytics and Knoweldge
https://tekri.athabascau.ca/analytics/
Applying Semantic Web Technologies to Services of e-learning System
Applying Semantic Web Technologies to Services of e-learning SystemInternational Journal of Engineering Inventions www.ijeijournal.com
Although of the semantic web technologies utilization in the learning development field is a new research area, some authors have already proposed their idea of how an effective that operate. Specifically, from analysis of the literature in the field, we have identified three different types of existing applications that actually employ these technologies to support learning. These applications aim at: Enhancing the learning objects reusability by linking them to an ontological description of the domain, or, more generally, describe relevant dimension of the learning process in an ontology, then; providing a comprehensive authoring system to retrieve and organize web material into a learning course, and constructing advanced strategies to present annotated resources to the user, in the form of browsing facilities, narrative generation and final rendering of a course. On difference with the approaches cited above, here we propose an approach that is modeled on narrative studies and on their transposition in the digital world. In the rest of the paper, we present the theoretical basis that inspires this approach, and show some examples that are guiding our implementation and testing of these ideas within e-learning. By emerging the idea of the ontologies are recognized as the most important component in achieving semantic interoperability of e-learning resources. The benefits of their use have already been recognized in the learning technology community. In order to better define different aspects of ontology applications in e-learning, researchers have given several classifications of ontologies. We refer to a general one given in that differentiates between three dimensions ontologies can describe: content, context, and structure. Most of the present research has been dedicated to the first group of ontologies. A well-known example of such an ontology is based on the ACM Computer Classification System (ACM CCS) and defined by Resource Description Framework Schema (RDFS). It’s used in the MOODLE to classify learning objects with a goal to improve searching. The chapter will cover the terms of the semantic web and e-learning systems design and management in e-learning (MOODLE) and some of studies depend on e-learning and semantic web, thus the tools will be used in this paper, and lastly we shall discuss the expected contribution. The special attention will be putted on the above topics.Future Learning Landscape Introduction
Introductory presentation given at Future Learning Landscape Workshop held at EC-TEL 2009. Presents some introductory elements about the state of research in pervasive learning, Web 2.0/Social Software and Semantic Web/Linked Data before discussing convergence
FUNCTIONAL SEMANTICS AWARE BROKER BASED ARCHITECTURE FOR E-LEARNING WEB SERVICES
E-learning enables the learner to gain diverse knowledge anytime, anywhere and on any device. Learning
resources (objects) and resource providers play a very important role in e-learning applications/systems.
The increasing demand for interoperability in existing heterogeneous e-learning systems to support
accessibility and reusability is the most challenging research issue. Web services and SOA enables
interoperability between heterogeneous applications over the Web. To adopt Web services technology
towards the reusability and aggregation of e-learning services, the conceptual Web services architecture
and its building blocks need to be augmented. In this paper, a well formed functional semantics approach
is proposed to describe e-learning Web services providing variety of learning objects/resources. The
paper presents an extendible functional knowledge to map the learner’s or provider’s versions of service
descriptions into a standard form called Abstract Description. The authors propose a broker based elearning Web service architecture which facilitates effective e-learning service publishing and discovery
mechanisms. The paper explores a scheme to extend the WSDL 2.0 document in order to incorporate
functional semantics of e-learning Web services and their operations. The paper presents an e-learning
service knowledge called Learning Operation Tree (LOT) for the quick e-learning service discovery. The
experimentation shows that, the proposed broker based architecture for e-learning Web services
facilitates effective discovery with moderate performance in terms of recall and response.
Ontology-Oriented Inference-Based Learning Content Management System
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
Ontology-Oriented Inference-Based Learning Content Management System
The world is witnessing the electronic revolution in many fields of life such as health, education,
government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings
with it rapid change and greater opportunities to increase learning ability in colleges and schools. The
fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are
full of open source and commercial products, however LCMS systems in general inherit the drawbacks of
information system such as weakness in user expected information retrieval and semantic modelling and
searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the
Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the
constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an
organized fashion. This enables construction of a user-specific course, by semantic querying for topics of
interest. We present the development of an Ontology-oriented Inference-based Learning Content
Management System OILCMS, its architecture, conception and strengths.
ONTOLOGY-ORIENTED INFERENCE-BASED LEARNING CONTENT MANAGEMENT SYSTEM
The world is witnessing the electronic revolution in many fields of life such as health, education, government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings with it rapid change and greater opportunities to increase learning ability in colleges and schools. The fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are full of open source and commercial products, however LCMS systems in general inherit the drawbacks of information system such as weakness in user expected information retrieval and semantic modelling and searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an organized fashion. This enables construction of a user-specific course, by semantic querying for topics of interest. We present the development of an Ontology-oriented Inference-based Learning Content Management System OILCMS, its architecture, conception and strengths.
e-Framework Tools
The document discusses the e-Framework for Education and Research, which is an initiative to develop an open standards-based technical framework to support education and research. It aims to define a set of reusable services that can be combined to support different functions. The benefits of a service-oriented architecture are outlined, as well as some existing tools that could potentially be incorporated into or extended by the e-Framework. Challenges with adopting the framework approach are also mentioned.
Future Environments Gilliot Garlatti Pahst2010 2
This document discusses future learning environments that converge pervasive and contextual computing, global social media, and semantic web technologies to enhance technology-based learning. It envisions an open, cloud-based learning environment that adapts to student needs through interoperability at the semantic level using common vocabularies and ontologies. This environment would support new situated learning activities and organizations that provide new opportunities for informal learning.
Improve information retrieval and e learning using
The Web-based education and E-Learning has become a very important branch of new educational technology. E-learning and Web-based courses offer advantages for learners by making access to resources and learning objects very fast, just-in-time and relevance, at any time or place. Web based Learning Management Systems should focus on how to satisfy the e-learners needs and it may advise a learner with most suitable resources and learning objects. But Because of many limitations using web 2.0 for creating E-learning management system, now-a-days we use Web 3.0 which is known as Semantic web. It is a platform to represent E-learning management system that recovers the limitations of Web 2.0.In this paper we present “improve information retrieval and e-learning using mobile agent based on semantic web technology”. This paper focuses on design and implementation of knowledge-based industrial reusable, interactive, web-based training activities at the sea ports and logistics sector and use e-learning system and semantic web to deliver the learning objects to learners in an interactive, adaptive and flexible manner. We use semantic web and mobile agent to improve Library and courses Search. The architecture presented in this paper is considered an adaptation model that converts from syntactic search to semantic search. We apply the training at Damietta port in Egypt as a real-world case study. we present one of possible applications of mobile agent technology based on semantic web to management of Web Services, this model improve the information retrieval and E-learning system.
Semantic Technologies in Learning Environments -Promises and Challenges-
The document discusses the promises and challenges of semantic technologies in learning environments. It describes how semantics can improve search, reusability, and provide richer metadata for learning objects. However, challenges remain in ontology development, integrating different approaches, motivating contribution from students, and addressing usability and privacy concerns in personalized learning systems.
Semantic Technologies in Learning Environments -Promises and Challenges-
The document discusses the promises and challenges of semantic technologies in learning environments. It describes how semantics can improve search, reusability, and provide richer metadata for learning objects. However, challenges remain in ontology development, integrating different approaches, motivating contribution from students, and addressing usability and privacy concerns in personalized learning systems.
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Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...
Developing a Collaborative Multimedia mLearning Environment
Developing a Collaborative Multimedia mLearning Environment
Semantic Web Technologies for the Integration of Learning Tools and Context-a...
Semantic Web Technologies for the Integration of Learning Tools and Context-a...
Applying Semantic Web Technologies to Services of e-learning System
Applying Semantic Web Technologies to Services of e-learning System
FUNCTIONAL SEMANTICS AWARE BROKER BASED ARCHITECTURE FOR E-LEARNING WEB SERVICES
FUNCTIONAL SEMANTICS AWARE BROKER BASED ARCHITECTURE FOR E-LEARNING WEB SERVICES
Ontology-Oriented Inference-Based Learning Content Management System
Ontology-Oriented Inference-Based Learning Content Management System
Ontology-Oriented Inference-Based Learning Content Management System
Ontology-Oriented Inference-Based Learning Content Management System
ONTOLOGY-ORIENTED INFERENCE-BASED LEARNING CONTENT MANAGEMENT SYSTEM
ONTOLOGY-ORIENTED INFERENCE-BASED LEARNING CONTENT MANAGEMENT SYSTEM
Improve information retrieval and e learning using
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Semantic Technologies in Learning Environments -Promises and Challenges-
Semantic Technologies in Learning Environments -Promises and Challenges-
Semantic Technologies in Learning Environments -Promises and Challenges-
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This Dissertation explores the particular circumstances of Mirzapur, a region located in the
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Semantic web and distance education
- 1. The Contribution Of Semantic Web Technologies To Distance Learning And Education ZOU International Conference 16-17 June 2011 Dydimus Zengenene Erasmus Mundus Scholar International Master in Digital Library Learning (DILL) University College of Oslo, University of Tallinn, University of Parma
- 7. Why do we need it To reduce time, costs and errors that are incurred in the search, manipulation and utilization of web based resources.
- 8. What is it? An improved version of the current web, which can provide better services at lower costs by way of giving machines the power to understand the meaning of web documents.
- 12. Technologies of the semantic web