• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.43-50
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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.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• Educational Technology International
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• v.17 no.2
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• pp.229-251
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• 2016

This study was designed to investigate the relationship between middle school students' smart media competency and online learning outcomes. The context of this study was on online inquiry-based mathematics and science learning environment, and four-hundred and fifty-five (455) 7^th to 9^th grade students in Korea participated in this research. To assess students' smart device competency, Smart Media Literacy Quotient (SMLQ) which consisted of 18 items regarding the operation ability of smart media and its application (14 items) and positive beliefs of smart media (4 items) was administered to each student. The findings of this study first showed that students' smart media literacy varied according to their characteristics such as gender, grade, class (subject), and socio-economic status. Female students' scores were higher in overall smart media literacy operation and learning ability of smart media than male students. 7^th grade middle school students represented lower smart media literacy scores, compared to 8^th and 9^th graders. Also, minority students were significantly lower in smart media literacy, operation and learning ability of smart media, and positive belief of smart media than the non-minority students. Second, overall smart media literacy and operation and learning ability of smart media varied among high, medium, and low score achievers in inquiry tasks. Low scored students in inquiry tasks were significantly lower in overall smart media literacy and operation and learning ability of smart media than medium scored students. Lastly, smart media literacy also varied by students' dropout/completion. Students who dropped out reported significantly lower scores in overall smart media literacy, operation and learning ability of smart media, and positive belief of smart media.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.79-88
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• 2011

This article is exploring on the concept and realization conditions of 'Smart Learning' including the problems about e-Learning, such as its low learning effectiveness and the weak profitability of its industry. We think, Smart Learning is an alternative solution for the continuous growth of e-Learning in the smart computing age. In this context, this paper will review the actual condition of e-Learning and the prior studies of Smart Learning, and study the concept and realization conditions of Smart Learning.

• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.309-318
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• 2012

Smart learning is a new trend in education following E-learning, U-Learning, and M-Learning. In June 2011, the Korean government announced the education policy on promoting smart learning, and presented the vision and the direction for the smart learning. However, it seems that the current government-directed education policy on smart learning has promoted the unconditional implementation of the policy without taking into consideration of a variety of factors, such as the reality of the classroom, educational environment, educators' competencies to use smart learning, and so on. The aims of this study are to examine the reality of the classroom and the educational environments for smart learning, and to take a survey on the elementary teachers' use of the smart learning. In addition, the study attempted to investigate the teachers' understanding of the various factors regarding the use of smart learning. On the basis of the results of the survey, the problems of implementing smart learning in the classroom were analyzed, and then some suggestions were made to pave the way for the more improved and systematic smart learning.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.156-165
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• 2014

The purpose of this study was to find research trends of smart learning. For this, we identified the research's characteristics such as the subject or keyword of research, method, data collection, and statistical analysis method. The 2,865 articles published from 1995 to 2013 were gathered from five Korean academic journals related to smart learning. Among them, research keyword, areas, research method, data collection method, and statistical analysis method were analyzed on 596 papers. The findings of this study were as follows: (a) Smart learning papers such keyword likes u-learning, m-learning, and smart-learning were emerging after 2006. Smart learning papers with ICT related topics were highly increased after 2000, but they were decreased after 2006. Smart learning papers with e-learning related keywords were steadily increased after 2000 through 2013. (b) The research field of deign had the highest portion in smart learning research, but managing had the lowest portion. (c) Development was mainly used as a research method. Both questionnaire and experiment were mainly used for collecting data methods. T-test and frequency analysis were mainly used as statistical analysis methods.

• Journal of Digital Convergence
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• v.9 no.3
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• pp.181-190
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• 2011

Smart Learning has been spreaded rapidly to the education and industry fields. Especially, Smart Learning of the university and Smart Campus are the focus of attention of the knowledge based age, because university education play a core role for lifetime education and has largest ripple effect. This article is exploring on the concept and realization conditions of 'Smart Campus'. We think, Smart Campus is an alternative solution for the continuous development of university education in the smart time. In this context, this paper will review the prior studies of Smart Learning, U-Learning and Smart Campus, and study the concept and realization conditions of 'Smart Campus'.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.51-60
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• 2022

This paper raised the need to examine how the online education environment triggered by COVID-19 and the smart learning environment can be established in consideration of the improvement of education and learning through learning analysis. Many studies are being conducted in Korea, and the Ministry of Education is continuously striving to build a smart school by promoting strategies for promoting smart education on the way to a talent powerhouse. Nevertheless, there is no unified definition of smart learning, and it can be seen as customized (individualized) learning using smart devices. However, most of the discussions on the construction of smart schools so far have limitations in that they are limited to physical spaces. Accordingly, the opinions of teachers and learners were not sufficiently reflected in the establishment of the facility. This study intends to study smart learning in various departments. In addition, the subjects students in charge of the co-researcher of this study were analyzed. The total number of subjects was 951, and 434 responded to this study survey. In addition, students were well accepting the online environment, and in the future, regardless of COVID-19, research will be presented to improve mutual communication between professors and students in smart learning.