• 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 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.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.267-279
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• 2011

A variety of Web Services are being existences based on the development of the Internet. Especially, e-Learning services in the Universities make the temporal and spatial constraints overcome, and e-Learning services have gained great popularity to the students who use because those provide various convenience and usefulness. e-Learning studies have been actively performed based on the spread of e-Learning in the various industries. A number of studies suggest the diffusion plan of e-Learning applying the Technology Acceptance Model studies. Those studies focused on the ease of use and usefulness of e-Learning. The explanation about educational contents perspectives, which is the key factor in e-Learning, is very weak. Therefore, this study suggested the strategy for spreading the e-Learning adoption through in terms of e-Learning educational contents and trust perspectives. This research results would provide the strategic implications to boost the e-Learning adoption in the various universities in terms of e-Learning educational contents and trust perspectives.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.739-748
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• 2016

This study quantitatively measured the impact of blended learning with smart devices for learning satisfaction. It is targeted in specialized domestic company with IT Service which build smart learning systems and utilize for employee training. Specifically, it empirically analyzed that learning attitude(Self-efficacy, Self-innovativeness, Perceived usefulness, Perceived ease of use) with smart devices affect acceptance of smart learning and offline face-to-face learning satisfaction. As a result, the learning attitude of the smart learning gave a positive effect on the acceptance of the smart learning and then acceptance of the smart learning gave a positive effect on offline face-to-face learning satisfaction. Additionally learning the attitude of the smart learning even gave a positive impact, as well as the acceptance of smart learning experience in offline training. It imply that this variables of smart-learning attitude affect the self-directed learning and positive learning experience.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.169-175
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• 2015

The purpose of this study is to investigate the structural relationships among teaching presence, cognitive presence, social presence, service quality, learning flow, and learners' satisfaction in mobile learning. Survey data collected by 255 learners who completed mobile-supported courses offered by an online university in South Korea were analyzed using structural equation modeling. The results suggest that cognitive presence, social presence, and service quality have direct effects on learning flow, and that cognitive presence, service quality, and learning flow have direct effects on learners' satisfaction.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1505-1506
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• 2011

소셜 서비스의 확산에 따라 이러닝 분야에서도 소셜러닝이 확산되고 있다. 소셜러닝이 기존 교육과 구별되는 가장 큰 특징은 콘텐츠의 생산과 소비 방법으로, 네트워크를 통해 가치를 전달하고, 다른 사람으로부터 배운다는 것이다. 따라서 소셜미디어 콘텐츠와 소셜네트워크 활동 콘텐츠를 학습객체화하여 함께 이용할 수 있어야 한다고 본다. 이를 위해 본 논문에서는 소셜미디어 콘텐츠를 학습객체화 할 수 있도록 콘텐츠 생성모델 확장 방안을 제안하고자 한다. 소셜자원기반 콘텐츠 생성모델은, 학습객체 정의와 메타데이터 생성모델로 구성된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.568-571
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• 2020

최근 빅데이터 기술이 다양한 산업과 접목되고 있다. 그 중 고객 이탈 방지가 최우선인 통신사들 또한 예외가 아닐 수 없다. 이에 본 논문은 통신사 데이터에 머신러닝 알고리즘을 접목. 이탈 예측과 데이터 추이를 분석하고, 이를 시각화 하여 일목요연하게 표출하는 과정을 제공함으로서 통신사의 고객 유치 정책을 위한 토대를 마련할 것이다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.347-350
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• 2019

인공지능 관련 기술의 발달로 다양한 분야에서 인공지능 활용에 대한 관심이 고조되고 있으며 전문영역에서도 기계학습 기법을 활용한 연구들이 활발하게 이루어지고 있다. 특허청에서는 분야별 전문지식을 가진 분류담당자가 출원되는 모든 특허에 국제특허분류코드(이하 IPC) 부여 작업을 수행하고 있다. IPC 분류와 같은 전문적인 업무영역에서 딥러닝을 활용한 자동 IPC 분류 서비스를 제공하기 위해서는 기계학습을 이용하는 분류 모델에 분야별 전문지식을 직관적으로 반영하는 것이 필요하다. 이를 위해 본 연구에서는 딥러닝 기반의 IPC 분류 모델과 전문지식이 반영된 분류별 어휘사전을 활용한 규칙기반 분류 모델을 병행하여 특허문서의 IPC분류를 자동으로 추천하는 방법을 제안한다.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.157-164
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• 2019

This paper investigates applicable methods of using machine learning(ML) in design process that is currently at infant stage and discuss how designers can use machine learning in UX design process. This research is differentiated from design method for machine learning-based products or services. For this purpose, this paper conducted literature reviews and case investigation and discussed three categories of design method of combination with such as 1) UX design centered ML, 2) ML system centered UX, and 3) UX-ML matchmaking. With this investigation, the workshop was conducted with specifically applicable methods of 2) and 3) for designers. Throughout the workshop, this paper analyzed each method' process with pros and cons in details. Throughout the process, this paper suggests precise methods of applying ML into UX design process.