• Journal of Information Processing Systems
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• v.20 no.3
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.587-592
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• 2020

This paper analyzed the curriculum of computer and software-related majors educating the core IT-related skills needed for the 4th Industrial Revolution. The analysis was conducted on 158 majors classified as applied software, computer science and computer engineering according to the standard classification of university education units by the Standard Classification Committee of the Korean Council of University Education. The current status of introduction of curricular divided into the fields of Internet of Things(IoT) & mobile, cloud & big data, artificial intelligence(AI), and information security was analyzed among the contents of education in the relevant departments. According to the analysis, an average of 81.6% of the majors for each group of curricular organized related subjects into the curriculum. The Curriculum Response Index for the 4th industrial revolution(CRI4th) by major, calculated by weighting track operations by education sector, averaged 27.5 point out of 100 point. And the IoT & mobile sector had the highest score of 42.3 points.

• The Journal of the Korean dental association
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• v.57 no.11
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• pp.644-653
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• 2019

Since 2013, adults aged over 20 can receive national health insurance scaling once a year in South Korea. In this study, we analyzed the usage status of national health insurance care service for periodontal disease in 2010-2018 by using Healthcare big data of the Health Insurance Review and Assessment Service. The increase rate of the dental care users was very high at 7.8 and 11.2% in 2013 and 2014, respectively. These are higher than the increase rate of all medical institution users, which is between -1.7 and 3.7%. In 2017, the rate of dental use was 44.4%, which has increased more than 10% compared to 2012. Percent receiver of national health insurance scaling was 19.5% in 2017. The 20s had the highest rate of 23.2%. The rate decreased with age. Based on these results, it can be evaluated that the expansion of national health insurance coverage for scaling improves accessibility to dental care. A more long-term assessment of the effect of periodic dental examination and scaling on reducing the prevalence of periodontal disease is needed. National health insurance coverage should be extended to oral hygiene education and supportive periodontal therapy in order to prevent periodontal disease.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.292-307
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• 2024

This study aimed to develop an index to assess the level of revitalization in mountain villages by utilizing big data and to verify its applicability in such areas. To achieve this, four key indices related to mountain villages were developed to evaluate the degree of revitalization, namely, Settlement Index, Workplace Index, Learning Index, and Leisure Index. These indices enable users to compare the revitalization levels of different mountain villages by establishing living zones, assigning data weights, extracting comparative data, and generating results in both map and report formats. The revitalization index developed in this study was applied to five mountain villages (A, B, C, D, E) located in Jeollabuk-do. Results showed that Village C had the highest comprehensive score of 320 points, while Village E had the lowest score of 141 points. In the mountain village indices of Jeollabuk-do, the Workplace Index generally showed higher scores, whereas the Learning Index had relatively lower scores on average. The development of these indices provides a practical means to identify which areas should be prioritized for support to enhance revitalization in specific mountain villages and offers a clear comparison of the revitalization levels across different regions and individual villages. The mountain village revitalization index developed in this study is expected to serve as valuable foundational information for formulating mountain village revitalization policies.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Korean Journal of Health Education and Promotion
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• v.30 no.3
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• pp.59-73
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• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.326-337
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• 2020

This study examined the public interest in high school Korean language instruction and the universal design for learning (UDL) using the social big data analysis method. The observations from 10,339 search results led to the conclusion that public interest in UDL was significantly lower than that of high school Korean language instruction. The results of the Big Data Association analysis showed that 17.22% of the terms were found to be related to "curriculum." In addition, a survey was conducted on a total of 330 high school students to examine how their teachers apply UDL in the classroom. High school students perceived computers as the most frequently used technology tool in daily classes (38.79%). Teacher-led lectures (52.12%) were the most frequently observed method of instruction. Compared to the second-year and third-year students, the first-year students appreciated the usage of technology tools and various instruction mediums more frequently (ps<.05). Students were relatively more positive in their response to the query on the provision of multiple means of representation. Consequently, the lesson contents became easier to understand for students with the availability of various study methods and materials. The first-year students were generally more positive towards teachers' incorporation of UDL.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.853-863
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• 2022

In order to revitalize start-ups in the beauty field, this study attempted to derive characteristic patterns of changes in demand and differences in emotions and meaning for 'beauty start-ups' by dividing the period by year from 2019 to 2021 based on exploratory data analysis (EDA). Most of the search terms related to the keyword "beauty start-up" showed more interest in institutions or certificates that can learn beauty skills than professional start-up education, which still does not recognize the importance of start-up education, and as an alternative, it is necessary to develop customized start-up education programs for each major. We establish hypotheses through exploratory data analysis and verify hypotheses by combining traditional corroborative data analysis (CDA). There has never been an exploratory data analysis method for beauty startups, and rather than mentioning the need for formal start-up education, analyzing changes in interest in beauty startups and the requirements of prospective start-ups with exploratory data will help develop customized start-up programs.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.40-46
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• 2020

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.