• Information Systems Review
• /
• v.17 no.3
• /
• pp.77-94
• /
• 2015

As Internet has become a popular media for sharing information, users create and share tremendous volume of information including large amount of personal information in cyberspace. Sharing private information online can enhance strength of social relationship but it could also bring negative consequences like information privacy invasion. Although many companies and governments address the importance of information privacy online, there are countless cases of crimes and hackings relating personal information online world wide. Since there are some researches investigating the role of governments and organizations on online privacy domain but there is little research regarding users' privacy protection behaviors. This study investigates relationship between Internet users' information privacy protection behavior and environmental factors. Especially, this study focuses on users' behaviors regarding information privacy protection technology adoption. According to our research results, users' online privacy protective behaviors positively affected by governmental regulations expressed as an information privacy protection law. In addition, if user is allowed to use anonymity when he or she uses online services, they have more tendencies to adopt privacy protection technologies. The detailed research findings and contribution are discussed as well.

• Journal of Science Education
• /
• v.45 no.2
• /
• pp.156-171
• /
• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Journal of Navigation and Port Research
• /
• v.42 no.6
• /
• pp.539-545
• /
• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Navigation and Port Research
• /
• v.43 no.1
• /
• pp.72-78
• /
• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• 한국체육학회지인문사회과학편
• /
• v.56 no.2
• /
• pp.303-320
• /
• 2017

This paper seeks to better understand the focal issues and persons related to Rio Olympic soccer games through social data science and analytics. This study collected its data from online news articles and comments specific to KOR during the Olympic football games. In order to investigate the public interests for each game and target persons, this study performed the co-occurrence words analysis. Then after, the study applied the NodeXL software to perform its visualization of the results. Through this application and process, the study found several major issues during the Rio Olympic men's football game including the following: the match between KOR and PIJ, KOR player Heungmin Son, commentator Young-Pyo Lee, sportscaster Woo-Jong Jo. The study also showed the general public opinion expressed positive words towards the South Korean national football team during the Rio Olympics, though there existed negative words as well. Furthermore the study revealed positive attitude towards the commentators and casters. In conclusion, the way to increase the public's interest in big sporting events can be achieved by providing the following: contents that include various professional sports analysis, a capable domain expert with thorough preparation, a commentator and/or caster with artistic sense as well as well-spoken, explanatory power and so on. Multidisciplinary research combined with sports science, social science, information technology and media can contribute to a wide range of theoretical studies and practical developments within the sports industry.

• Journal of the Korean Society of Earth Science Education
• /
• v.13 no.3
• /
• pp.305-316
• /
• 2020

This study aimed to analyze the conception of an extra-solar planet perceived by university students. To conduct this, we developed an extra-solar planet education program and questionnaires which help to figure out changes between before and after the program, and then applied them to the targeted students. The results of the study are as follows. First, as to the conception of an extra-solar planet, participants understood it merely as a planet outside the solar system before they got training. However, they expanded it to the one revolving around a star that appears outside the solar system based on keywords after the training. Second, they gave brief responses regarding exploration strategies (e.g., observing the extra-solar planet by using the Doppler effect, dietary phenomenon, and gravitational lens) based on indirect experiences they encountered in the media. The responses indicated their lack of concept of the extra-solar planet exploration methods. However, their recognition of the extra-solar planet observation became concrete while students learned about the exploration of the extra-solar planet. Third, they were expanding the importance of the exoplanet observation simply beyond the discovery of extraterrestrial life to the creative process and research methods, including the solar system and the development of humanity. Fourth, they recognized that exoplanet education is necessary for curriculum as it will be able to bring about students' interest and curiosity as well as scientific knowledge if contents related to the extra-solar planet appear in the earth science curriculum.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.247-259
• /
• 2021

This study aimed to look for educational ways to prepare for the future society for education and people of talent who will lead the post-COVID-19 era. To this end, the factors necessary for the type of future talent in the post-COVID-19 era were identified by analyzing Big data. Based on the deducted factors composing the type of talent in the post-COVID-19 era, policy direction according to the emergence of the post-COVID-19 era were deducted through the interviews with the group of experts and delphi survey, and on the basis of this, this study sought for"a plan for the educational change in line with cultivation of people of talent in the post-COVID-19 era. The results of this study are as follows. First, through the big data analytics and analysis of the interviews, convergence, ICT utilization ability, creativity, self-regulated competency and leadership were found to be the factors necessary for the type of talent in the post-COVID-19 era. Second, it considered the innovation of digital education system and the support for vulnerable classes as the issue for cultivation of people of talent in the post-COVID-19 era. Third, the most important policy with regard to the educational direction for cultivation of people of talent in the post-COVID-19 era was cultivation of convergence talents. Convergence is a very important variable in the post-COVID-19 era since it creates new values by connecting things that are separated from each other. Hopefully, this study will build a basis for competency development, education and training in preparation for the post-COVID-19 era.

• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.765-777
• /
• 2022

The purpose of this study is to present a digital transformation-based learning model that can be used in universities based on learning digital transformation in order f to be competitive in a rapidly changing environment. Literature review, case study, and focus group interview were conducted and the implications for the learning model from these are as follows. Universities that stand out in related fields are actively using learning analysis to implement dashboards, develop predictive models, and support adaptive learning based on big data, They also have actively introduced advanced edutech to classes. In addition, problems and difficulties faced by other universities and K University when implementing digital transformation were also confirmed. Based on these findings, a digital transformation-based learning model of K University was developed. This model consists of four dimensions: diagnosis, recommendation, learning, and success. It allows students to proceed with learning by diagnosing and recommending various learning processes necessary for individual success, and systematically managing learning outcomes. Finally, academic and practical implications about the research results were discussed.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.125-144
• /
• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• Journal of Internet of Things and Convergence
• /
• v.10 no.1
• /
• pp.69-75
• /
• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).