• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.81-88
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• 2019

This article aims to identify the characters and theme shown in dance costume and utilize them from an educational perspective by constructing digital museum archiving, which can be systematically collected, classified and stored from dance costume. It deals with definition of digital museum archiving as theoretical background and examples of how to create digital museum archiving as research content. The role that archiving plays in digital museum and effectiveness have been demonstrated. Archive is a term used to indicate extensive material and its storage and referred to as an integrative model of display in the computer-generated space. When it comes to producing dance costume as a form of digital museum, the museum is to be made in the computer-generated area of dance costume. The museum shows each division of major, medium and minor classification. The major classification divides genre of dance performance into Korean dance, modern dance and ballet. The middle involves choreographers, costume designers. The minor categorization includes newspaper, interviews, performance pictures, and programs. Digital museum has the value of space utilization, creation, culture, utilization of multiple educational programs, offering of digital museum content, two-way communication, and program development of the new display form.

• The Korean Journal of Archival Studies
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• no.16
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• pp.147-200
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• 2007

Archives provide users with easier and more convenient access to and use of archival data through their Internet websites. Now, archival websites function not only as a tool of information provision or marketing, or as a gateway of Internet-based materials, but also as cyber space for all services of archives, providing users with information and knowledge and direct accessibility to the services in the archives. At present, Korean archives are proceeding with establishing websites or upgrading existing websites, and the use rates and reliability of web services by users are becoming higher. However, although there have been various studies for the evaluation of general websites, few are found with regard to the evaluation of archival websites. It is necessary for archival websites that provide information service to users of every stripe to make more efforts and have more interest in user-centered convenience, right to know, and information provision-centered service. Accordingly, needless to say, it is necessary to study evaluation criteria of websites so that high quality archival websites can be established. With this background, this study establishes evaluation criteria for archival websites, which are appropriate to their objectives and functions and directly evaluates archives, presenting ways to establish and redevelop archival websites. More detailed purposes are as follows: First, analyzes existing theories of evaluation through reviews on previous literature and elicits evaluation criteria for websites, which are appropriate to archives; Second, based on the elicited evaluation criteria for archival websites, examines the current state of domestic archival websites through analytic evaluation; and Third, presents ways to improve archival websites that may be helpful in establishing or improving them in the future. The expectancy effects of this study are as follows: First, it will be helpful when one wishes to identify the current state of archival websites and to improve or redevelop existing websites, or to develop online service through website; Second, it will function as a checklist when a developer who is to establish an archival website wishes to develop evaluation criteria; and Third, it may be used as an inspection tool when an archives contracts out the establishment of its website.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.51-60
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• 2014

In this study, ICT medical service provider's level of knowledge fire fighting safety and methods on coping with fires in the regions of Gwangju and Jeonam Province of Korea were investigated to determine the elements affecting such levels and provide basic information on the manuals for educating how to cope with the fire fighting safety in medical facilities. The data were analyzed using SPSS Win 14.0. The scores of level of knowledge fire fighting safety of ICT medical service provider's were 7.06(10 point scale), and the scores of level of recognizing how to cope with fire fighting safety were 6.61(11 point scale). level of recognizing how to cope with fire fighting safety were significantly different according to gender(t=4.12, p<.001), age(${\chi}^2$=17.24, p<.001), length of career(${\chi}^2$=22.76, p<.001), experience with fire fighting safety education(t=6.10, p<.001), level of subjective knowledge on fire fighting safety(${\chi}^2$=53.83, p<.001). In order to enhance the level of understanding of fire fighting safety and methods of coping by the ICT medical service providers it is found that: self-directed learning through avoiding the education just conveying knowledge by lecture tailored learning for individuals fire fighting education focused on experiencing actual work by developing various contents emphasizing cooperative learning deploying patients by classification systems using simulations and a study on the implementation of digital anti-fire monitoring system with multipoint communication protocol, a design and development of the smoke detection system using infra-red laser for fire detection in the wide space, video based fire detection algorithm using gaussian mixture mode developing an education manual for coping with fire fighting safety through multi learning approach at the medical facilities are required.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.292-300
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• 2016

The purpose of this study was to investigate the effects of case-based learning about infants and toddlers on healthcare department students, using a video in an emergency care environment. A total of 57 students from a healthcare department of D university in J city were enrolled. They were divided into two groups: The experimental group (n=29) and the control group (n=28). This study is pre-post designed with a non-equivalent control group. The experimental group received a 1-week education for a duration of 3 weeks (3 sessions in total) with 180 minutes per session. The control group received a traditional curriculum of lecture. Before and after the education, we measured the knowledge and skill confidence of emergency care toward infants and toddlers, the academic self-efficacy, and problem solving ability. Data collection and intervention were carried out from November to December of 2014. Data were analyzed with x2-test, paired t-test, unpaired t-test with SPSS version 20.0 Program. The experimental group showed a significantly higher improvement of skill confidence of emergency care toward infants and toddlers (P<001), as well as preferred task difficulty among sub-items of academic self-efficacy (p=.029), approach avoidance style (P=.001), and problem solving confidence (p=.040) among sub-items of problem solving ability on preference compared with the control group. In this study, a case-based learning was verified to be an effective teaching method to enhance professional competency of healthcare department students. The findings from this study suggest that a case-based learning using various educational contents should be developed, expanded, and carried out to promote better learning.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.640-654
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• 2021

Purpose: Normal education operation was difficult in the national disaster situation of Coronavirus Infection-19. Non-face-to-face education can be an alternative to face to face education, but it is not easy to provide the same level of education. In this study, the professor of disaster management field will identify problems that can occur in the overall operation and progress of non-face-to-face education and seek ways to improve non-face-to-face education. Method: Non-face-to-face real-time education was largely categorized into pre-class, in-class, post-class, and evaluation, and case studies were conducted through the professor's case studies. Result&Conclusion: The results of the survey are as follows: First, pre-class, it was worth considering providing a non-face-to-face educational place for professors, and the need for prior education on non-face-to-face educational equipment and systems was required. In addition, it seems necessary to make sure that education is operated smoothly by giving enough notice on classes and to make efforts to develop non-face-to-face education programs for practical class. Second, communication between professor and learner, and among learners can be an important factor in non-face-to-face mid classes. To this end, it is necessary to actively utilize debate-type classes to lead learners to participate in education and enhance the educational effect through constant interaction. Third, non-face-to-face post classes, policies on the protection of privacy due to video records should be prepared to protect the privacy of professors in advance, and copyright infringement on educational materials should also be considered. In addition, it is necessary to devise various methods for fair and objective evaluation. According to the results of the interview, in the contents, which are components of non-face-to-face education, non-face-to-face education requires detailed plans on the number of students, contents, and curriculum suitable for non-face-to-face education from the design of the education. In the system, it is necessary to give the professor enough time to fully learn and familiarize with the function of the program through pre-education on the program before the professor gives non-face-to-face classes, and to operate the helpdesk, which can thoroughly check the pre-examination before non-face-to-face education and quickly resolve the problem in case of a problem.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.