• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2684-2690
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• 2009

The existing mobile learning service is the method that provides data packets in real time. It has some problems of much data packet cost and delay to provide the mobile learning service. In this paper, to solve the problems, we suggest to implementation of the mobile learning system of the new method using serial communication.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.827-830
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• 2005

e-learning 교육은 오프라인 교육의 다양한 제한적 문제를 해결할 수 있는 대안으로 많은 발전을 이루어 오고 있다. e-learning 교육의 표준화 작업으로 앞으로 더 많은 발전을 가져올 것이고 ITS (Intelligent Tutoring System)의 구현을 앞당길 것이다. 그러나 모든 교육이 능동적으로 문제를 해결해 나갈 수 있는 능력을 키우는 것 이라는 교육학적 입장에서 본 논문은 학습자의 개별적 특성을 수용하는 개별화된 학습방향을 선택할 수 있는 Self-learning 서비스를 제안한다. 이 서비스는 교수설계자에 의해 지정된 시퀀싱을 학습자가 재정렬 할 수 있다. 이 시스템은 SCORM 기반의 LMS 에 추가되는 서비스이다.

• Educational Technology International
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• v.17 no.2
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• pp.203-228
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• 2016

This study was designed to determine the factors affecting learner satisfaction and examine the relationships of these factors in mobile learning linked to pre-existing e-learning in universities. In the structural model used, three mobile learning quality factors are the endogenous variables, namely, system quality (SYQ), information quality (INQ) and service quality (SEQ) perceived by students, and learner satisfaction (LS), whereas students' self-efficacy (SE) and experience (EX) in mobile learning are the exogenous variables. The subjects were 900 students who registered for mobile learning courses offered by a private university in Seoul, Korea. The results indicated that SE in mobile learning had positive effects on SYQ, INQ, and SEQ. Furthermore, SE influenced LS when analyzed without quality factors as parameters. Mobile learning EX directly affected INQ, but not SYQ or SEQ. EX likewise had a direct effect on LS when analyzed without quality factors as parameters. Meanwhile, both SYQ and INQ showed a positive effect on LS, but not SEQ. SE and EX affected LS indirectly when SYQ and INQ were used as parameters. This study addresses the importance of increasing SE, EX, SYQ, and INQ to increase LS in mobile learning in universities

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.903-906
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• 2015

Over the recent industry -wide virtual world and the real world, broadcasting and telecommunications, IT technology and traditional industries, such as the fusion research has been conducted in a variety of fields. And training in the field of education is changing the paradigm of creativity to break the intrusive training center. In addition, the quality of interactive educational content technology to foster self-directed future talent is a situation that is required. The market has already surpassed the smartphone PC, smart devices and e-learning technologies are appearing new service called 'smart learning' as a new form of convergence of the educational system. In this paper, based on the direct development of a content authoring applications and Web sites, and cloud environments to the students collect and analyze patterns. Utilizing this information, we studied the curation service plans that recommend the appropriate content to fit the tastes of the learner.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.883-890
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• 2008

It becomes enabled to provide variety of multimedia contents through mobile service with the development of high-speed 3rd generation mobile communication and handsets. MMS (Multimedia Messaging Service) can be displayed in the presentation format which is unified the various multimedia contents such as text, audio, image, video, etc. It is applicable as a new type of ubiquitous learning. In this study we propose to design a mobile learning system by providing profiles which meets the standard of CC/PP and by generating multimedia messages based on SMIL language through the adaptation steps according to the learning environment, the content type, and the device property of learners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1534-1538
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• 2020

Smart farming education service focuses on the dissemination of farming information that is the farming knowledge, farming skill, and farmer's experiences and knowhow, etc. This farming information is supposed from current activities, farming product and from the experience of farmer on the field. If the information is not available, or if available and not in a form that is amenable to being brought to the end producer then the process stalls at this point. The core component of the automation process for smart farming education service is the creation of a data store which will be a repository for the information of the smart farming education. The farming sector will benefit immensely from the implementation of farming data in farming contents repository which will serve as the knowledge base for the smart farming education service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.518-527
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• 2014

This study is to estimate the learning curve based on the consequences of reduced voice of customer from each telecommunication service products. We used Exponential Decay Model, which is the most popular among the learning curve models. We attempted to add how VOC changes in accordance with seasonal factors, human resource input, application of software, and the investment. The results of the empirical analysis of each service product as follows: First, as learning curve, customer complaints decreased. Second, human resource input, Network fault make increase or decrease customer complaints(VOC). Third, even though increasing the customer's quality of experience, VOC would not decrease due to service paradox.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.71-76
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• 2010

This research proposed the method for application of SCORM in U-learning system. That is, I proposed the API broker to connect between U-learning and API Instance of RTE that is existing SCORM based learning object interface environment. The API broker operated handling process using request port and response port between SCORM and U-learning server. For efficient operation in each service, this system has learning contents service buffer in API broker.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.