• 한국디지털정책학회:학술대회논문집
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• 2005.06a
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• pp.591-604
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• 2005

이러닝의 중요성과문화산업에의 파급효과 및 장래성에 대하여는 이미 많은 문건이나 발표로 알려져 있다. 이렇게 여러 분야에 중요한 효과를 가져가기 위해서는 교육의 양적 성장 뿐 만 아니라 효율적인 교육과 이에 대한 효과에 대해 관심을 기울여야 한다. 이미 미국이나 유럽을 중심으로 각종 이러닝 관련 연구를 통해 ROE(교육투자회임) 연구와 더불어 품질인증(QA : Quality Assurance)에 대한 중요성이 부각되고 있다. 이러한 움직임은 이러닝을 위한 교육자원의 상호운용 또는 활용이라는 측면에서 강조되어 온 이러닝 표준화와 연계되어 그 움직임이 활발히 진행되고 있다. 이러닝 품질표준화의 논의는 단순히 교육자원의 상호운용과 재사용이라는 측면에서 제시되어 온 메타데이터 관리 차원의 SCORM과 같은 기준 외에 교육자원의 생성으로부터 교육시스템 및 교육과정 운영에 이르기까지 그 영역이 광범위한 것에 주목할 필요가 있으며, 국가와 문화적으로도 다양한 환경을 고려해야만 할 것이다. 본고에서는 이러닝 품질보증 표준화의 정의와 범위 그리고 표준안을 만들기 위한 수행절차 및 적용방법 등을 살펴본 후에 외국의 개발 현황과 국내의 개발 현황을 비교함으로써 우리가 가져야 할 미래지향적 표준전략을 제시하고자 한다.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.661-667
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• 2010

A lot of existence e-learning are connected SCORM and LMS. And u-learning was researching as one of the new trend. But there are few research paper to connect the existing SCORM and LMS. In this paper, we proposed u-learning framework with connect the SCORM and LMS. And we used the mobile equipment transform module and learning object reconstruction module to apply each different characteristics of mobile equipment. Especially, information of the mobile equipment was stored and managed using the meta-data of the equipment.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.441-452
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• 2022

Training using metacognition in a learning environment is one of the topics that have been continuously studied since the 1990s. Metacognition can be broadly divided into declarative metacognitive knowledge and procedural metacognitive knowledge (metacognitive skills). Accordingly, metacognitive training has also been studied focusing on one of the two metacognitive knowledge. The purpose of this study was to examine the role of metacognitive skills training in the context of mathematical problem solving. Specifically, the learner performed the prediction of problem difficulty, estimation of problem solving time, and prediction of accuracy in the context of a test in which problems of various difficulty levels were mixed within a set, and this was repeated 5 times over a total of 5 weeks. As a result of the analysis, we found that there was a significant difference in all three predictive indicators after training than before training, and we revealed that training can help learners in problem-solving strategies. In addition, we analyzed whether there was a difference between the experiment group and control group in the degree of test anxiety and math achievement. As a result, we found that learners in the experiment group showed less emotional and relationship anxiety at 5 weeks. This effect through metacognitive skill training is expected to help learners improve learning strategies needed for test situations.

• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.129-140
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• 2022

Our society is undergoing rapid changes due to COVID-19, and in particular, online learning using digital technology is being tried in various forms in the educational field. A change has occurred. However, the limitations of distance learning, such as reduced learning immersion in non-face-to-face educational situations, lack of interaction between teachers and learners, and lower basic academic ability, are constantly being raised, and an appropriate educational strategy is needed to solve these problems. This study focused on the concept of 'Metaverse' based on the interaction between the virtual world and the real world, and tried to verify the effectiveness of educational activities based on it. In detail, we propose an educational framework for realizing flipped learning in the Metaverse Virtual Classroom, and a frame developed by measuring the learning immersion of a single group with a teaching/learning program developed based on this. The effectiveness of the work was verified. When the metaverse platform-based flip learning framework and education program proposed in this study were applied, it was confirmed that learners' immersion in learning was improved.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.13-24
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• 2021

This was a one-group pretest-protest pre-experimental study designed to find out the effects of writing programs on college students using flipped learning for training convergence talents with complex problem solving skills. Data were collected from 27 C university students in G-do, from September 1st to October 30th, 2020. Analysis was done using IBM SPSS 25.0 for frequency (percentage), average, standard deviation, and paired t-test. The study showed that self leadership was enhanced by an average of 0.45 points (p<.001), metacognition by 0.87 points (p<.001), goal orientation by 0,77 points (p<.001), and creativity by 0.51 points (p<.001), so was statistically significant. The study demonstrated that writing programs using filpped learning had the effect of improving complex problem solving skills. so the results are expected to be used as basic data for the development of educational programs for traning convergence talents with problem solving skills at university-level. Also, for current college students that can freely access the online environment, development of various e-learning classes that apply flipped learning is needed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.337-339
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• 2011

본 연구에서는 EPUB기반의e-Book 콘텐츠를 스마트러닝환경에서학습객체로 활용하고자한다. 이를 위해e-Book의 표준인 EPUB을 분석하여 SCORM의 콘텐츠 모델을 적용하여 확장 설계하였으며, 더블린코어와 LOM 메타데이터를 Collection Map을 이용하여 EPUB 기반의 학습객체메타데이터인ELOM을 설계하였다. EPUB기반의 학습객체가 LMS에서 추적관리가 가능하도록 SCORM2004의 CMI 데이터 모델을 바탕으로 학습객체 특성에 맞는 기본 데이터 모델을 정의하였다. 설계된 학습객체의 운용 가능성을 평가하기 위해 EPUP기반 오픈소스 콘텐츠인 모비딕의 bodymatter를 학습객체로 재구현한 후 ADL의 SCORM2004 $4^{th}$ Test Suite1.1.1을 이용하여 검증하였다. 본 연구에서 설계된 ELOM은 스마트 스크린으로 확장하여 적용할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.934-936
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• 2022

현재 사회적인(COVID-19) 영향으로 메타버스에 대한 수요가 급증하였지만, 메타버스 플랫폼 진입을 지원하는 XR(AR/VR) 장비의 높은 가격대와 전문성 요구로 폭넓은 수요층을 포괄하기 어려운 상황이다. 본 논문에서는 이러한 수요층의 어려움을 개선하고자 웹 캠이나 스마트폰 카메라로 생성된 개인의 사진 이미지를 StyleGAN 딥러닝 기술과 접목시켜 캐릭터를 생성해 Mediapipe를 활용하여 모션 측정 및 제어를 처리하는 서비스를 제안하여 메타버스 시장의 대중화에 기여하고자 한다.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.694-702
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• 2022

This paper is the UX-based technology strategy research which is a solution to how conversational AI can be ethically evolved in the Metaverse environment. Since conversational AI influences people's on-offline decision-making factors through interaction with people, the Metaverse AI ethics must be reflected. In the machine learning process of conversational AI, cultural codes along with user's personal experience data must be included and considered to reduce the error value of user experience. Through this, the super-personalized Metaverse service can evolve ethically with social values. With above hypothesis as a result of the study, a conceptual model of a forward-looking perspective was developed and proposed by adding user experience data to the machine learning (ML) process for context-based interactive AI in the Metaverse service environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.47-50
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• 2023

본 논문에서는 딥러닝 CNN 알고리즘을 이용하여 사람의 얼굴 이미지를 학습한 다음 나이와 성별을 예측하는 시스템을 제안한다. 이 시스템은 개개인 마다 각기 다른 외형적 특성을 고려하여 이를 분석한 다음 이에 맞는 헤어 스타일, 옷차림을 추천할 수 있다. 해당 기술을 활용하여 메타버스 아바타 생성에 사용자의 얼굴과 같은 신체적 특성을 고려할 수 있다. 향후에는 신체 전체를 이미지화하여 보다 더 다양한 정보를 인식할 수 있도록 연구를 진행할 것이다.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.