• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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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.

• The Journal of Korean Institute for Practical Engineering Education
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• v.5 no.1
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• pp.28-33
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• 2013

In this paper, we proposed a smart learning model that reflects the environment of information and communication that rapidly changing with the advent of smart devices. There are varieties of screen size and performance of smart devices, but they can be classified smart phones, smart pads, personal computer(PC) and Smart TV. In this paper, we look for appropriate services model with method of interaction and educational content as per each device type, then present the development direction of smart learning that reflects the information communication environment and future smart learning service model according to the characteristics of each device.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.99-109
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• 2011

Ubiquitous computing technology makes an impact on the appearance of u-learning and presents an advanced direction of futuristic school education. In ubiquitous learning environments, various embedded computational devices will be pervasive and interoperable across the network for supporting the learning, so users may utilize these devices anytime anywhere. An important next step for ubiquitous learning is the introduction of context-aware learning service that employing knowledge and reasoning to understand the local context and share this information in support of intelligent learning services. However, the existing studies on design and application of ontology context model to support context-aware service in actual school environments are incomplete state. This paper, therefore, suggests a scheme of constructing ubiquitous learning space for existing school network by introducing USN to support context-aware ubiquitous learning services. This paper, also, designs an ontology based context model for ubiquitous school environments which describes context information through OWL. To determine the suitability of proposed ubiquitous learning space and ontology context model, we implement some of context-aware learning services in the ubiquitous learning environments.

• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.3
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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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• v.22 no.3
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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.

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.05a
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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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.

• KSCI Review
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• v.14 no.1
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• pp.213-219
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• 2006

We need the customized e-Learning service according to not only developing the wireless mobile and hardware technology, also developing the multimedia process skills. Especially, the beginner who start to learn the first programming course must be provided the personalized learning. The beginner require the repeated practices to obtain the programming skills, also they reveal the different learning effects following the learner capability In this paper. we develop a new e-Learning contents which give the individual service for learner and show the simulation which is program execution to maximize the learning effects.

• The Journal of Information Systems
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• v.14 no.2
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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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.77-82
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• 2022

The research study was conducted for development of the advanced image analysis service system based on deep learning. CNN(Convolutional Neural Network) is built in this system to extract learning data collected from Google and Instagram. The service gets a place image of Jeju as an input and provides relevant location information of it based on its own learning data. Accuracy improvement plans are applied throughout this study. In conclusion, the implemented system shows about 79.2 of prediction accuracy. When the system has plenty of learning data, it is expected to predict various places more accurately.