• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.5
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• pp.239-252
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• 2008

To facilitate the processes of e-learning resource description, discovery and reuse, e-learning objects should be appropriately described and classified using standard metadata that need to be published in a registry to reduce duplication of effort and enhance semantic interoperability. This paper describes how standard ebXML registries can be used for semantic grid computing for annotating, storing, discovering and retrieving e-learning object metadata. For semantic annotation of e-learning objects, IEEE Learning Object Metadata (LOM) is adopted as the metadata ontology. In order to support the e-learning metadata ontology in interoperable ebXML registries, a mapping scheme between LOM and ebXML Registry Information Model (RIM) is proposed. The usefulness of sharing e-learning object metadata is demonstrated by prototyping a semantic registry based on the scheme.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.87-94
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• 2006

Internet makes it possible to access anytime, anywhere learning and so many LMS(Learning Management Systems) serve web based learning. But LMS has not flexible and qualified metadata to offer customired teaming. So we need extensible and flexible techniques which make if possible to define and share advanced teaming metadata. This paper presents an approach for implementing advanced learning metadata in LMS using RDF and the Semantic Web language. So we will first sketch the learning scenario in Semantic Web environment and structure of metadata management. Next we suggest two types of RDF authoring tool and search RDF documents. Advanced metadata management techniques enables the organization of learning materials around small pieces of semantically annotated learning objects. With these metadata learner can customize learning courses, improve retrieval performances.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.211-217
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• 2011

Recent IT (information technology) environmental changes, such as emerging social network services or increasing user participation in multimedia environment, have made it necessary for e-learning systems to undergo changes in various ways. Metadata is an agreement for interoperability between different systems. The standardization of metadata for e-learning system has been driven by some domestic and international organizations, but applying diverse environmental changes into the design of e-learning metadata is in dire need. In this paper, we present a methodology for the analysis and design of modeling e-learning metadata and elicit the design requirements, on the basis of the metadata standard KEM 3.0, about the elements that are expected to be needed in future e-learning systems. Based on the requirements from the analysis, we present the three-layer model for classifying the requirements by the importance of metadata elements per Kana Model. An intelligent e-learning system is to be developed based on the proposed modeling design, which we hope to influence the development of an international standard in the future.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.249-259
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• 2006

E-learning objects should be appropriately described and classified using standard metadata for facilitating the processes of e-learning resource description, discovery and reuse. These metadata need to be published in a registry to reduce duplication of effort and enhance semantic interoperability. This paper describes how standard ebXML registries can be used for annotating, storing, discovering and retrieving e-learning object metadata. For semantic annotation of e-learning objects, IEEE LOM is adopted as the metadata ontology. In order to support the e-learning metadata ontology in interoperable ebXML registries, a mapping scheme between LOM and ebXML information model is proposed. The usefulness of standard ebXML registries for sharing e-learning metadata is demonstrated by prototyping an e-learning registry called ebRR4LOM based on the scheme.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.147-162
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• 2009

In this study, our purpose was to develop the metadata elements for archiving e-learning content being generated by universities. In order to achieve this goal, we first examined the current status of e-learning content providing services both domestically and overseas and then compared each standard for metadata for e-learning content built for educational purposes. We found that KEM (Korea Education Metadata) 3.0, a server being provided by KOCW (Korea Open CourseWare), does not currently accommodate the metadata elements for archiving. In this study, we extended and added the scope of metadata elements for archiving based on KEM 3.0. We also tried to build up metadata for archiving the e-learning content provided based on KEM 3.0+. As a result of this study, a basis for archiving elLearning content is expected to be founded.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.79-81
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• 2005

최근 SCORM, Dublin Core등의 국제 표준 메타데이터와 함께, 세계 사실 표준이라 할 수 있는 IMS와 IEEE/LTSC의 LOM이 e-Learning의 특성을 반영한 메타데이터로서 현재 국$\cdot$내외적으로 많은 e-Learning 업체 및 기관에서 활용되고 있다(5). 그러나 LOM에서 정의한 메타데이터는 멀티플랫폼 환경을 고려하지 않고 있고, 제작 및 유통되고 있는 대부분의 e-Learning 콘텐트는 멀티미디어 특성에 대한 메타데이터 요소가 부족한 실정이다. 따라서 , 본 논문에서는 멀티플랫폼 환경에서 e-Learning학습을 지원하기 위해, 메타데이터 및 e-Learning 업체의 Requirement를 조사,분석하고 e-Learning 국제 표준 메타데이터와 플랫폼의 디바이스 특성을 반영하여, 기본적인 PC(Personal Computer) 환경을 포함한 모바일 기기 환경과 디지털TV 환경을 고려한 멀티플랫폼 e-Learning 메타데이터(Multi-platform e-Learning Metadata)를 제안하였다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.189-201
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• 2011

In this paper, we design an ontology which provides interoperability by integrating typical metadata specifications and defines concepts and semantic relations between concepts that are used to describe metadata for learning objects in university courses. And we organize a hierarchical structured e-Learning system for efficient retrieval of learning objects on many local storages that use different specifications to describe metadata and propose a query processing method based on inferences. The proposed e-Learning system can provide more accurate and satisfactory retrieval service by using the designed ontology because both learning objects that be directly connected to user queries and deduced learning objects that be semantically connected to them are retrieved.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.269-278
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• 2003

The popularity of the web based education has come the need for variety learning methods and for business to exploit the web not only for interoperability but also standardization. This way of standardization has come to researched for environments, contents and practical uses in ISO. The IEEE has special]y established five technical classes for LTSA which provide advanced e-learning environments. Feedback functions would not be supported and specified in standardization for Query Answer on LTSA. In this paper, we describe the query and answer model which we have developed on layer three of LTSA. We develop the redefined model for transforming data flow oriented into object or component based model. We have developed the Query Answer Metadata (QAM) based on Learning Object Metadata (LOM). We design and showed thing a prototyping implementation the Query Answer Learning Tool (QALT). We have used the QALT to address the problem of efficiency of web based education. We also used it to develop the related tools with quality and productivity.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.