• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2009년도 춘계학술대회
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• pp.281-294
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• 2009

Recently the information system introduction and the application which use the computer are increasing. The efficient operation of information system helps corporations to improve productivity, customer service and corporate competitive power. SaaS(Software as a Service), which is the developed type of outsourcing in the area of information technology, is to use standardized and packaged application by hosting from the outside of enterprise. SaaS is not developed yet as much as people's interest about that in the initial stage, but its related technology and service capacity are accumulated after repeated trial and error, and it's ready to activate the industry. In the area of information system, studies on the measurement of service quality were followed. But there is little study on service quality measurement in the field of SaaS(Software as a Service). The purpose of this study was to divides the SaaS with e-SERVQUAL and it consider the relationship with the perceived recognition and the usage intention. According to this, variables of traditional research were rejected because SaaS is not developed yet in Korea. But information had a strong effect on perceived recognition. Therefore, business related with in SaaS must have provided a correct information about various applications.

• 한국실천공학교육학회논문지
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• 제5권1호
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• pp.28-33
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• 2013

본 논문에서는 스마트기기의 출현으로 급변하는 정보통신환경을 반영한 스마트러닝 모델을 제시한다. 스마트 기기들은 화면크기와 성능이 다양하지만 크게 스마트폰, 스마트 패드, 개인용 컴퓨터(PC), 스마트 TV로 구분할 수 있다. 본 논문에서는 각 기기 유형별로 서비스에 적합한 교육 콘텐츠와 상호작용 방법들에 대하여 살펴보고, 각 기기 특성에 맞는 스마트러닝 서비스 모델과 향후 정보통신환경을 반영한 스마트 러닝의 발전 방향을 제시하였다.

• 한국컴퓨터정보학회논문지
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• 제16권6호
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• pp.99-109
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• 2011

유비쿼터스 컴퓨팅 기술은 유비쿼터스 학습 즉 u-러닝의 출현을 이끌며 미래 학교 교육의 개선 방향으로 제시되고 있다. 유비쿼터스 학습 환경에서는 우선 사용자들이 언제 어디서나 학습을 위해 다양한 내장형 컴퓨터들을 활용할 수 있도록 이러한 기기들을 도처에 편재시키고 네트워크를 통한 상호작용을 지원하는 것이다. 다음 단계는 지능적인 학습 서비스를 지원하는 것으로, 상황 정보를 이해하고 이를 공유하도록 상황 지식과 상황 추론이 가능한 상황 인식 서비스를 도입하는 것이다. 그러나 아직까지 실제 학교를 대상으로 상황 인식 서비스를 지원하기 위한 온톨로지 상황 모델 설계 및 적용에 대한 연구결과는 미비한 상태이다, 이에 본 논문은 상황 인식 기반의 유비쿼터스 학습 서비스를 지원하기 위해, 기존 LAN 환경에서 이루어지는 학교 네트워크를 대상으로 USN을 활용한 유비쿼터스 학습 공간을 제안하고, OWL을 사용해 유비쿼터스학교 환경에대한온톨로지기반의상황모델을설계한다. 또한제안한유비쿼터스학습공간과온톨로지상황모델이 유비쿼터스 학습 환경에 적합함을 확인하기 위해 유비쿼터스 환경에서의 상황 인식 학습 서비스들을 구현한다.

• 한국의상디자인학회지
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• 제19권3호
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• pp.89-97
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• 2017

The purpose of this study is to analyze the difference and influence of educational institutions and majors on education service quality, learning satisfaction, and educational institution loyalty, and to provide information on an Academic Credit Bank System appropriate for the two types of education institutions and learners. Looking at the difference in education service quality, learning satisfaction and educational institution loyalty, Lifelong Education Organizations of Universities showed positive awareness compared to Job Technical colleges. Especially, differences in awareness regarding quality of education facilities were largest, followed by educational institution loyalty and learning satisfaction. Looking at the difference in awareness according to educational institution and majors, learners at Lifelong Education Organizations of Universities had positive awareness compared to Job Technical colleges in terms of quality of facilities, learning satisfaction and educational institution loyalty. No difference was found according to major. Looking at the effect of awareness of education service quality on learning satisfaction and educational institution loyalty, factors of education service quality affected learning satisfaction in the order of education>administration>facilities for Lifelong Education Organizations of Universities, and the effect on educational institution loyalty was found in the order of administration> education with no effect shown by facilities. For learners at Job Technical colleges, factors of education service quality affected learning satisfaction in the order of administration>education>facilities. Influence on loyalty to educational institution was found in the order of administration>facilities>education.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.77-88
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• 2022

In response to the significant COVID-19 outbreak, countries have enforced the use of E-learning systems as an alternative to traditional learning; to contain the virus and minimize the infection rate while maintaining the continuity of the learning experience. However, the effective adoption of E-learning systems requires a well-understanding of critical factors, especially in times of crisis. In this regard, this study intends to assess the success of the E-learning system adoption by Higher Education Institutions (HEIs) during the crisis of COVID-19 by utilizing the Information Systems Success (ISS) model. This study's adopted model consists of nine interdependent dimensions, namely: Technical System Quality, Information Quality, Service Quality, Learner Quality, Perceived Satisfaction, Perceived Usefulness, System Use, Intention to Use, and System Success. An electronic survey was distributed among higher education students from different universities in Saudi Arabia to explore each model's dimension. Structural Equation Modeling (SEM) has been applied via SmartPLS software to test the causal relationships between dimensions. This study's main results revealed that students' Service Quality, Learner Quality, and the Intention to Use by students are essential drives for E-learning System Use during the Covid-19 pandemic. Meanwhile, the Intention to Use the system is significantly influenced by Perceived Satisfaction and Perceived Usefulness dimensions. Further, Perceived Satisfaction, Perceived Usefulness, and System Use are interdependent, and all three have a significant positive impact on E-learning System Success.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2005년도 춘계학술대회 발표 논문집
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• pp.409-424
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-Learning, Distance Education, Virtual University. This IT-based learning paradigm shift is certainly a new opportunity or a threat to our universities. The Local University Community e-Learning Centers that support the demand of e-Learning for their community are recommended. Tn order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has S or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Learning centers.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.61-63
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• 2021

Machine learning model gained the popularity in recent years as multi-national companies have incorporated machine learning in their services. Such service is called machine learning as a service (MLaSS). Such services are provided to users based on charge-per-query which triggers the motivations for adversaries to steal the trained victim model to reduce the cost of using the service. Therefore, it is important for companies that provide MLaSS to protect their intellectual property (IP) against adversaries. It has been arms race between the attack and defence in a context of the privacy of machine learning models. In this paper, we provide a comprehensive study of recent development in protecting privacy of machine learning models.

• 한국컴퓨터정보학회지
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• 제14권1호
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• pp.213-219
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• 2006

무선 이동통신 기술과 하드웨어 기술 등의 발전과 멀티미디어 처리기술의 발전에 따라 개인 맞춤형 e-Learning서비스가 필요하다. 특히 처음 프로그래밍을 접하는 학습자에게 개인 맞춤형 e-러닝 콘텐츠는 반드시 필요하다. 즉 프로그램 개발 능력을 학습하기 위해 많은 반복적인 실습이 요구되며, 학습자 개인에 따라 다양한 학습 효과를 보여주기 때문이다. 따라서 본 논문에서는 프로그래밍을 처음 배우는 학습자에게 적합한 개인 맞춤형 서비스를 가능하게 하면서, 프로그래밍이 수행되는 과정을 시뮬레이션 형태로 보여주어 학습 효과를 극대화할 수 있는 e-러닝 콘텐츠를 개발하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제14권2호
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• pp.75-99
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-learning, Distance Education, Virtual University, This IT-based teaming paradigm shift is certainly a new opportunity or a threat to our universities. To overcome this problem the universities should think e-Learning as strategic weapon, such as many firms created competitive weapons from the information systems at the 1980s. So, e-Learning system can be a SIS(Strategic Information System) which supports university's future education strategies. To build a e-Learning system, not only many H/W and S/W resources but also expert personnels are required. An organization such as local university who is week at financial status can't himself plan the system. The Local University Community e-Learning Centers that support the demand of e-learning for their community are recommended. In order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has 5 or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. We also tried to find the difference for each factor by the characteristics of responsor. The data showed that there was litter difference between the groups in evaluating the significance of strategic roles of e-learning centers. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Loaming centers.

• 한국컴퓨터정보학회논문지
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• 제27권1호
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• pp.77-82
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• 2022

본 연구는 장소 이미지를 수집하고 학습하여 사용자가 관심이 있어 하는 이미지의 장소를 예측하여 알려주는 서비스 개발을 목적으로 한다. 이미지 학습을 위한 이미지 데이터들은 크롤링 부분을 통해 수집되도록 설계되었다. 이미지 수집 이후 수집된 이미지들은 장소별로 라벨링 되어 CNN의 다양한 층을 통하여 학습된다. 각 층을 거칠 때마다 입력받은 학습 데이터는 최적화하여 특징 맵과의 비교를 반복하며 특정 장소 이미지의 특징 정보를 뽑아낸다. 충분한 학습 데이터가 쌓이면 다양한 장소 이미지들에 대해 예측이 가능하다. 학습 결과 모델의 정확도는 79.2로 높은 학습 정확도를 보였다.