• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.148-155
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• 2014

The purpose of this research was to analyze the effects of smart learning. By using meta analysis method, twenty MA and Ph.D degree papers published from 2006 to 2013 were analyzed and 104 effect sizes were calculated. Followings were the results of the research: (a) Smart learning turned out to be more statistically effective comparing to traditional education. The total mean effect size was .886 and the value of U3 was 66.53%. (b) All effect size of sub dependent variables(ie, academic achievement, learning satisfaction, learning attitude) were also effective by adapting smart learning. (c) The moderated variables likes learner characteristics, learning content, and interaction had high effect sizes. Operation system variable had a low effect size but it was not significant.

• Journal of The Korean Association of Information Education
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• v.18 no.1
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• pp.25-33
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• 2014

Due to the recent development of educational robot hardwares, processing speed and scalability have been greatly improved. Thus, the robot hardwares that are compatible with temperature sensor for MBL and gyro sensor made a data logging possible. Students can conduct an experiment on scientific research and prediction, collecting and data analysis with robots that can process data logging. Therefore this research constructed and adopted science project class that introduced a Smart r-Learning that utilizes Class SNS and smartphone. As a result of applying a data logging smart r-Learning to elementary school 5th graders, it has shown that the students' logical thinking ability four of the six areas have been improved in t-test.

• Journal of the Korean Society of Radiology
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• v.11 no.4
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• pp.279-287
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• 2017

The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 74 in 2classes were surveyed at the end of semester. Compared to use smartphones one group and not use smartphones one group for study in a class. The research data was analyzed using SPSS also self directed learning, self learning efficacy, learning satisfaction analyzed t-test, general character was analyzed two group(one : Used smart learning other : not Used smart learning) ${\chi}^2-test$. First, Used smart learning group is more higher than not Used smart learning group in a self learning efficacy, self directed learning, learning satisfaction. Second, during the smart learning classes a students appeared a positive response. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods.

• Journal of Science Education
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• v.39 no.3
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• pp.321-332
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• 2015

This study was intended to determine how developed Smart learning teaching-learning program on the unit of Solar system and Star affected on science-related attitude, science learning interest and academic achievement. The unit of Solar system and Star was selected among 5th grade science curriculum contents to design smart learning teaching-learning program. Smart learning instruction program utilized a various contents of smart equipment and made learners to do problem solving through their interaction and cooperation. The results of this study were as follows: First, smart learning instruction improved the science-related attitude and the science learning interest and the academic achievement of the experimental group students significantly. Sencond, the survey and the individual face-to-face data shows the positive effects of smart learning instruction. Especially, the satisfaction was high on the attitudes and interests in the classroom and the students regarded the classroom activities as interesting games by using the smart devices. On the basic of the conclusions, this work suggested the direction of the future studies, such as necessity of developments and researches on Smart learning teaching-learning program about other units or other subjects, such as measures of the increasing the intrinsic interest on science rather than Smart learning elicit simple interest and attitude.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.1
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• pp.128-136
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• 2017

The purpose of this research is to analyze the effects of smart learning in both general education and fisheries & marine education through meta-analysis. To find the effects size, we had collected 112 studies from graduation theses and journal articles. Followings are the results of the research: (a) Smart learning turns out to be more statistically effective comparing to traditional education. The total effect size of smart learning is .768 and the value of U3 is 61.50%. (b) There is no significant difference between general education and fisheries & marine education in the view of effect size. (c) There is a significant difference in subjects, type of publication, and size of members in experimental group. High school student group has the most effect size of smart learning.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.125-132
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• 2012

In recent years, a variety of learning content construction utilizing the smart phone is coming. In this paper, we investigate on overall trends and movements in e-learning performance at University. And system developed a e-learning platform consisting of smart phone portal, learning management system(LMS), and learning content management system(LCMS). Throughout the experiment, each of the components of the e-learning were implemented. LMS was implemented more efficiently using a user profile evaluation system for qualification.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.370-378
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• 2012

This paper proposes a method of constructing of hybrid web-based smart learning system to operable in a variety of mobile devices. To do this, the proposed system is developed a learning system with standardized and enhanced functions. In the proposed method, API specifications based on the standard functionality of smart learning system are created. And then, by building the API provider on a legacy system an organic linkage between the legacy system and the smart learning system is guaranteed. A standard API method is applied to data integration between the PC-based learning system and the smart learning system. The smart learning system interacts with legacy learning systems though Json/XML data forms via the https protocol. As a result, the legacy system using the proposed method dose not require major modifications and changes for a smart learning service.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.625-632
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• 2014

Due to the development of information and communication technology smart devices and apps, SNS, mirroring communication is made with such a smart and education to reflect the change of emphasis on the recent variety of collaborative social interaction are emerging. In this study, smart training and LT cooperative learning model developed in conjunction with the 'Smart-based cooperative learning 'model applied in the third grade social studies class and Smart-based cooperative learning and cooperative learning common to kidney doctor communication skills of elementary school students the impact on the communication capacity compared respectively, were analyzed. As a result, the expression of the elementary school, listening and understanding, all the sub-areas of interaction, such as communication skills in social studies class kidneys were applied to Smart-based cooperative learning in elementary school than applying the general cooperative learning model. This is not said to improve the ability to interact with the Smart-based cooperative learning in speech and in writing and clearly express the thoughts and opinions of students and separates help you understand the meaning of the words and writings of other students for the purpose in social situations can.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• Educational Technology International
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• v.18 no.1
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• pp.27-47
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• 2017

Smart devices have a variety of affordances to foster meaningful learning in elementary school. For the design of smart learning environments, more research is needed to understand students' smart device usage and their perception of learning with smart devices. In order to capture smart device usage profiles among elementary school students in South Korea, this study carried out Latent Profile Analysis with three constructs: information search, communication, and study. Participants (n=253), who ranged from the fourth to the sixth grade students, were classified into three profiles of smart device usage: low-activity, communication, and high-activity groups. The smart device usage profiles varied depending on smartphone usage experience, and the profiles were significantly related with smart device addiction, not with smart device usage ability. Perceptions of smart education were also significantly associated with the profiles. The high-activity group showed more positive attitudes toward smart education than the others, but no significant difference was found in regard to negative attitudes. Based on the findings, this study discussed implications for the use of smart devices in elementary school.