Investigation of Learning Object Metadata and application to a search engine for K-12 schools in Japan

2002 ◽  
Author(s):  
M. Okamoto ◽  
M. Shinohara ◽  
Y. Okui ◽  
S. Terashima ◽  
M. Hashimoto
Keyword(s):  
Search Engine ◽  
Learning Object ◽  
Learning Object Metadata ◽  
K 12

Adaptation of Descriptive Metadata for Managing Educational Resources in the GREDOS Repository

2018 ◽  
pp. 2063-2085
Author(s):  
Erla M. Morales Morgado ◽  
Rosalynn A. Campos Ortuño ◽  
Ling Ling Yang ◽  
Tránsito Ferreras-Fernández
Keyword(s):  
Learning Styles ◽  
Mobile Apps ◽  
Learning Objects ◽  
Educational Resources ◽  
Learning Object ◽  
Institutional Repository ◽  
Dublin Core ◽  
Digital Repositories ◽  
Learning Object Metadata ◽  
Metadata Mapping

In this chapter the authors describe a Project entitled “Divulgación de Recursos Educativos Digitales (DIRED)” (Divulgation of Digital Educational Resources) addressed to promoting specific educational resources and mobile apps for educational proposals in order to manage them through the institutional repository of the Salamanca University (GREDOS). The authors present a proposal for describing learning objects based on pedagogical information, digital competences and learning styles. The authors also suggest educational information for classifying useful mobile apps. To achieve their suitable access and recovery, the authors focus on the use of Learning Object specific metadata in digital repositories such as LOM (Learning Object Metadata). The authors study the metadata mapping necessary to adapt from LOM to Qualified Dublin Core, because this is the standard used in the GREDOS repository built with a DSpace platform. Finally, the authors present their implementation of Learning Object Description in the GREDOS repository.

Enhancing Digital Repositories with Learning Object Metadata

2010 ◽  
pp. 246-263
Author(s):  
Andreas D. Alexopoulos ◽  
Georgia D. Solomou ◽  
Dimitrios A. Koutsomitropoulos ◽  
Theodore Papatheodorou
Keyword(s):  
Learning Object ◽  
Dublin Core ◽  
Semantic Level ◽  
Digital Repositories ◽  
Learning Object Metadata ◽  
Ontological Model ◽  
Basic Characteristics ◽  
Educational Applications ◽  
Metadata Mapping ◽  
Research Resources

In this chapter the authors present the basic characteristics about some existing educational metadata schemata and application profiles. They focus on the widely adopted IEEE LOM standard and give a brief analysis of its structure. Having in mind the utilization of educational metadata schemata by digital repositories preserving educational and research resources, they concentrate on a considerably popular system for this reason, DSpace. The authors want to show how the IEEE LOM metadata set can be incorporated in the default DSpace’s qualified Dublin Core metadata schema, introducing enhancements to the existing University of Patras live installation. For this reason, they document a potential LOM to Dublin Core metadata mapping and reveal possible gains from such an attempt. Further, they propose an ontological model for the repository’s metadata that takes also into account the educational characteristics of resources. In this way, they show how a semantic level of interoperability between educational applications can be achieved.

Using Learning Objects in K-12 Education

2007 ◽  
pp. 245-260
Author(s):  
Karen L. Rasmussen
Keyword(s):  
Learning Environments ◽  
Teaching And Learning ◽  
Learning Objects ◽  
Learning Object ◽  
Science Standards ◽  
Instructional Resources ◽  
Support Tool ◽  
Teachers And Students ◽  
Object Technology ◽  
K 12

Reusable Learning Object technology offers K-12 teachers and students the opportunity to access resources that can be used and reused in classroom teaching and learning environments. A support tool for teachers, QuickScience™, was developed to help teachers and students improve performance in science standards; resources in QuickScience™ are built upon RLO technologies. Six types of RLOs, including five types of instructional resources aligned to Bloom’s taxonomy, are used by teachers to help students improve their performance in science. QuickScience™ offers teachers a model for improving performance, including steps of diagnose, plan, teach, and assess.

Teaching Frameworks for Context-Rich Instruction

2007 ◽  
pp. 119-139 ◽  
Author(s):  
Kevin Oliver
Keyword(s):  
Learning Objects ◽  
Learning Object ◽  
Educational Strategies ◽  
Educational Levels ◽  
Content Development ◽  
Related Development ◽  
Rich Instruction ◽  
Design Objects ◽  
K 12 ◽  
Further Development

This chapter proposes a category of tools called design objects that can be used by instructors to integrate existing content sources, including but not limited to learning objects, within teaching frameworks that engage learners with content in meaningful ways. Emphasis is on tools to support the K-12 instructor, although related issues are applicable across educational levels. Examples of teaching-oriented design objects are provided along with related development systems, however it is argued the former represent more viable options for teachers given limitations in the learning object economy, conceptualizations of teachers regarding objects, complexity in packaging objects, and classroom control issues. The possibility of design objects and development systems working in tandem is discussed, with development systems prescribing effective educational strategies for novice teachers and design objects supporting more personalized content development. Various sources for new design objects are suggested to encourage further development and research.

scholarly journals A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

2019 ◽  
Vol 14 (06) ◽  
pp. 27 ◽  
Author(s):  
Kamal El Guemmat ◽  
Sara Ouahabi
Keyword(s):  
Search Engines ◽  
Learning Objects ◽  
Learning Object ◽  
Automatic Indexing ◽  
New Approach ◽  
Learning Object Metadata ◽  
Depth Extraction ◽  
Semantic Distances ◽  
Linguistic Structures

Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.

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