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

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

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

• The Journal of Korean Association of Computer Education
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• v.22 no.5
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• pp.51-65
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• 2019

This study aims to provide educational implications for more strategic online software education by the types of online learning according to learners' self-regulated learning characteristics in the online software education environment and examining the characteristics of each type. For this, variables related to self-regulated learning characteristic were extracted from the log data of 809 students participating in the online software learning program of K University, and then analyzed using hierarchical cluster analysis. Based on hierarchical cluster analysis learner clusters according to the characteristics of self-regulated learning were derived and the differences between learners' learning characteristics and learning results according to cluster types were examined. As a result, the types of self-regulated learning of online software learners were classified as 'high level self-regulated learning type (group 1)', 'medium level self-regulated learning type (group 2)', and 'low level self-regulated learning type (group 3)'. The achievement level was found to be highest in 'high-level self-regulated learning type (group 1)' and 'low-level self-regulated learning type (group 3)' was the lowest. Based on these results, the implications for effective online software education were suggested.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 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
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• v.56 no.2
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• pp.303-320
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• 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
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• v.13 no.3
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• pp.305-316
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• 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
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• v.12 no.3
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• pp.247-259
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• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.