• Informatization Policy
• /
• v.24 no.4
• /
• pp.79-92
• /
• 2017

As city functions develop more complex and advanced, interests in smart cities are also increasing. Smart cities refer to the cities effectively solving urban problems such as traffic, safety, welfare, and living issues by utilizing ICT. Recently, many countries are attempting to introduce big data, Internet of Things, and artificial intelligence into smart cities, but they have not yet developed into comprehensive urban services. In this paper, we review the current status of domestic and overseas smart cities and suggest ways to solve issues of data sharing and service compatibility. To this end, we propose a "Deep Learning City Framework" that incorporates the deep learning technology into smart city services, and propose a new smart city strategy that safely shares spatial and temporal data in cities and converges learning data of various cities.

• Journal of Internet Computing and Services
• /
• v.15 no.1
• /
• pp.143-156
• /
• 2014

The smart education has the goal of enhancing the capability of learners in the 21st century and especially address the improvement of the problem solving capability. This smart education based on the growth of smart devices and the effect of dramatical spread requires the ability of problem solving using the smart technology in accordance with time change. As the problem solving learning is a model used mainly for improving the capability of problem solving, this study develops the problem solving learning model focusing on the teaching-learning activity using the smart devices and also applies this model to the school field. As a result, the favorable response that using the smart devices is effective to the problem solving can be obtained. This study can contribute to achieve the goal of the smart education, and later can be effective to the successful smart education in the school field.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.5
• /
• pp.93-105
• /
• 2011

This study was performed to figure out the effects of perceived usefulness and ease of use in Technology Acceptance Model(TAM) affecting acceptance attitude and intention to use in smart learning. In addition, the study recognized the need for differentiation of learning time by analyzing the difference of effects influencing acceptance attitude of perceived usefulness and ease of use during learning time, which is at the beginning, midterm, and at the end of the term. As the results of the study, it showed that there were differences between the factors, the learning time of which was considered, affecting acceptance attitude and intention to use. Furthermore, in order to improve the effectiveness of building a smart campus, which is currently under the construction, the study argued that universities need to consider the learning relevance and subjective norm as important factors in perceived usefulness of smart learning. Finally, the need for the design of various smart learning types became accepted considering learning time.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1081-1086
• /
• 2016

While smart learning have been introduced for more learning effect, this study is to understand instructor's smart learning acceptance using technology acceptance model(TAM). This study developed the extended TAM model, including external pressure for smart learning and smart self efficacy for smart devices as study variables and attempted to examine the research model through the empirical analysis. The research model has the 7 variables including smart self-efficacy and external pressure. For the empirical study, the survey was conducted for the one month, March, 2016, and the total 143 data among the collected 167 responses were used for the empirical analysis. As the result of the analysis through the structural equation model, the 9 paths among the total 10 paths show the significant relationships between the variables. Through using the result of this study, it is to provide suggestions for the improvement of smart learning environments.

• East Asian mathematical journal
• /
• v.33 no.4
• /
• pp.413-429
• /
• 2017

This study is a basic study for developing a mathematical learning application program that can be used in smart devices for adaptive learning. We selected 20 mathematical learning applications including middle school contents and analyzed learning types. And we analyzed the contents and the learning process. As a result, most learning types of mathematics learning applications were problem-centered. Contents analysis results showed that the most applications have achievement goals. The factors that induce interest in learning were lacking and feedback was not provided sufficiently. Analysis of the learning process showed that most of the math learning applications were classified according to their purpose and characteristics.

• Journal of The Korean Association of Information Education
• /
• v.21 no.2
• /
• pp.183-190
• /
• 2017

This study is for analyzing the effect of smart blended instructional strategy in history class. The effectiveness of this strategy is set as students' academic achievement and learning interest. This study is applied to elementary school students who participated in history class by classifying the experiment group with smart blended instructional strategy and the comparison group with traditional teaching method. As a result of the t-test, the smart blended instructional strategy positively affected the students' academic achievement and learning interest. Through the qualitative analysis, the students' understanding of the class is improved and the concentration is also improved.

• The Journal of the Korea Contents Association
• /
• v.13 no.10
• /
• pp.584-593
• /
• 2013

The purpose of this research is to analyze students' perception of smart learning focusing on its definitions, roles and values in distance higher education. In the online survey, 1,950 students of 'A' open university were participated. The results show that the students viewed the smart learning to be more 'absorbing', 'interactive' and 'collaborative' than the existing e-learning, as it compiles their experiences into learning. However, the respondents' perceptions of smart learning varied among different age groups: more students in their 40s and 50s responded that smart learning was 'customized', 'humanlike', 'interactive', 'comfortable', 'stable', 'familiar', 'unstressful', and 'practical' than students in their 20s and 30s, and they tend to view the main feature of smart learning to be the compilation of learner experiences.

• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.383-390
• /
• 2014

In recent years, the convergence of IT and IT sightseeing tour has emerged as a fusion of academic disciplines in the future. Convergence study of social data analysis, raising the heat. Social Network Services (SNS) being utilized in many areas of marketing and to apply the case study is also increasing. This study is based u-smart tourist information systems for mobile learning content design. This is the pattern of things in the template library for things to increase the effectiveness of the learning content to mobile learning content to be converted to a. Design of mobile learning content using u-smart things smart phone app (App) and XMI to go through the design process of utilizing the heat. Future through the design process by implementing a mobile learning content to meet information quality tourist information content to create mobile learning content and learning things that can be content to live it up advantage.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1072-1075
• /
• 2005

This paper presents that TD method is applied to the human adaptive devices for smart home with context awareness (or recognition) technique. For smart home, the very important problem is how the appliances (or devices) can adapt to user. Since there are many humans to manage home appliances (or devices), managing the appliances automatically is difficult. Moreover, making the users be satisfied by the automatically managed devices is much more difficult. In order to do so, we can use several methods, fuzzy controller, neural network, reinforcement learning, etc. Though the some methods could be used, in this case (in dynamic environment), reinforcement learning is appropriate. Among some reinforcement learning methods, we select the Temporal Difference learning method as a core algorithm for adapting the devices to user. Since this paper assumes the environment is a smart home, we simply explained about the context awareness. Also, we treated with the TD method briefly and implement an example by VC++. Thereafter, we dealt with how the devices can be applied to this problem.

• Journal of Digital Convergence
• /
• v.13 no.1
• /
• pp.487-493
• /
• 2015

This paper derives the strategic implications of smart learning as a sophisticated alternative to e-learning through the convergence approach of e-learning and Bigdata based on the practices of developed countries. To this, this paper derives e-Learning status and challenges issues in Korea, and then, analyzes the convergence case of e-learning and data science in major foreign advanced companies and universities. In addition, this study conducts an awareness survey on Bigdata applied for employees of e-learning companies, and then derives a strategic alternative to the Bigdata convergence-based smart learning effectiveness in the industry with the analysis of the survey data.