• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.81-88
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• 2006

With the introduction of the digital technology in the exhibition environment, the information of the exhibit has come to be transmitted through diverse media. The visitors desire has been increased from the simple viewing of the exhibition to the active participation in the exhibition viewing and the utilization of the exhibit information. Subsequently, the study on the service to effectively support the viewing experience and provide the information by utilizing the internet and mobile device for visitors in movement has become important in terms of the exhibition environment. Accordingly, in this study, the current condition in studying the service supporting the exhibition environment and the exhibition viewing, which are being changed into a digital network environment, was examined through the literature and case studies. In order to find out the viewing situation and viewing type of visitors, the visitors behaviors of viewing the exhibition were observed. By analyzing the contents observed, the viewing type and keyword were drawn in accordance with the visitors behaviors of viewing. On the basis of this, visitors needs and problems occurring in case of the exhibition viewing were found out via in-depth interview. The service factors of supporting the exhibition viewing were proposed on the basis of the factors by which visitors needs and problems could be solved via interface in the circumstance when visitors would move round the exhibition hall and view the exhibition. In terms of the service factors, the method to resolve was presented on the basis of the relationship between the exhibit and the space in case of selecting and viewing the exhibit. This was applied into the mobile PDA with the example of the exhibition environment in the national museum. Through the scenario of using, the usefulness of the service proposed and the relevant possibility of utilization were reviewed.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1055-1062
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• 2002

Recently, the lesson plan document that is imported in the educational area is not provided to the educational information systematically, and the teachers are not easy to compose the lessen plan documentation. So, it needs additional time and effort to develope the lesson plan documents. Because of increasing the distributing network. web-based lesson plan system is required to all of the education area. Therefore, we need to compose the lesson plan that is possible to obtain the various teacher's requirement by providing creation, retrival, and reusability of document using the standard XML on web. In this paper, we developed the system for creating the common DTD (Document Type Definition), providing the standard XML document through the common DTD over the lesson plan analysis. In this system, it provides the editor to compose the lesson plan and supports the searching function to improvement of reusability on the existing lesson plan. We design the searching functions such as the structure base, facet and keyword. The composed lesson plans are interoperated with Database. Consequently, we can share the information on web by composing the lesson plan using the XML and save the time and cost by directly writing the lesson plan on web. We can also provide the improved learning environment.

• Journal of Gifted/Talented Education
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• v.25 no.6
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• pp.881-904
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• 2015

The purpose of this paper is to explore the future direction of research on gifted education through a literature review of dissertations and research reports, as well as an analysis of the trends and milestones achieved related to gifted education. The period from 2003 to 2012, from which the data for this literature review was collected, marks the ten-year period proposed by the Gifted Education Development Comprehensive Plan II and I. Data was collected through a search of the keyword "gifted" on Academic Naver and on Korea Education and Research Information Service (KERIS). Results showed 1,696 articles from 182 academic journals, 138 doctoral dissertations, 1,470 masters' dissertations, and 798 research reports from 75 institutions. For analysis, each article was classified by target of study, kind of giftedness, subject of study, and methods used for the study. Results from this literature review demonstrated that from 2003 to 2012, the articles from the 182 academic journals and the doctoral and masters' dissertations used quantitative research to analyze elementary and middle school students gifted in math and science as well as the curriculum and programs of their study. This paper provides recommendations for future research on gifted education within the country.

• Journal of Oriental Neuropsychiatry
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• v.22 no.3
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• pp.101-113
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• 2011

Objectives : Trend of R&D of herbal medicine for dementia treatment was examined based on the quantitative information analysis for establishing the national strategy of research on dementia treatment with oriental medicine. Methods : Definition was made to clarify the technology for development of herbal medicine for dementia treatment. Based on the initial keyword provided by experts in the field, queries were compounded to conduct search in the search engines of WoS and DWPI. The raw data (papers or patents) extracted from the initial search were examined by expert-review before objects of analysis were determined. Then, the accumulated data was analyzed in terms of year, country and organization, which led to examination of the trend of R&D. And the research performance evaluation for dementia treatment technologies was also made in terms of country, organization and researcher based on the forward citation analysis. The international cooperation intensity was examined on the basis of analysis of network by researcher before analysis results were put together to select lead researchers. Results : According to the quantitative information analysis of 1,330 articles that were selected as analysis objects, the number of papers on natural products research for dementia treatment has increased by around 4.6 times in recent five years. This indicates that the intensive studies have been underway recently. It was found to be the US that had the highest level in research filed of herbal medicine for dementia treatment and the highest capacity of international cooperation for that purpose. On the contrary, Korea had the share of papers at 5.1%, the number of countries in cooperation research at 8, and the article quality index at 0.40, showing that the qualitative level was insufficient, compared to the quantitative outcome. In particular, Korea was found to have no intensity of international cooperation among researchers. In case of patent, the results of information analysis of 305 patents selected as analysis objects demonstrated that China had the highest share while Korea had the very low frequency of patent application quantitatively. Conclusions : In this study, the research to develop herbal medicine for dementia treatment has recently drawn much attention that has spread around the globe. Therefore, these results suggest establishing the strategy to develop technology for dementia treatment with oriental medicine in the future based on quantitative information analysis.

• Informatization Policy
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• v.25 no.4
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• pp.37-64
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• 2018

This study aims to forecast the future of the Korean society via a big data analysis. Based upon two sets of database - a collection of 46,000,000 news on 127 media in Naver Portal operated by Naver Corporation and a collection of 70,000 academic papers of social sciences registered in KCI (Korea Citation Index of National Research Foundation) between 2005-2017, 40 most frequently occurring keywords were selected. Next, their temporal variations were traced and compared in terms of number and pattern of frequencies. In addition, core issues of the future were identified through keyword network analysis. In the case of the media news database, such issues as economy, polity or technology turned out to be the top ranked ones. As to the academic paper database, however, top ranking issues are those of feeling, working or living. Referring to the system and life-world conceptual framework suggested by $J{\ddot{u}}rgen$ Habermas, public interest of the future inclines to the matter of 'system' while professional interest of the future leans to that of 'life-world.' Given the disparity of future interest, a 'mismatch paradigm' is proposed as an alternative to social forecasting, which can substitute the existing paradigms based on the ideas of deficiency or deprivation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.489-495
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• 2020

The amount of data is increasing through various IT devices in a hyper-connected society where the 4th revolution is progressing, and new value can be created by analyzing that data. This paper was collected total 1,526 articles from 2017 to 2019 in central magazines, economic magazines, regional associations, and major broadcasting companies with the keyword "(East Coast Tourism or East Coast Travel) and Gangwon-do" through Bigkinds. It was performed the topic modeling using LDA algorithm implemented in the R language to analyze the collected 1,526 articles. It was extracted keywords for each year from 2017 to 2019, and classified and compared keywords with high frequency for each year. It was setted the optimal number of topics to 8 using Log Likelihood and Perplexity, and then inferred 8 topics using the Gibbs Sampling method. The inferred topics were Gangneung and Beach, Goseong and Mt.Geumgang, KTX and Donghae-Bukbu line, weekend sea tour, Sokcho and Unification Observatory, Yangyang and Surfing, experience tour, and transportation network infra. The changes of articles on East coast tourism was was analyzed using the proportion of the inferred eight topics. As the result, the proportion of Unification Observatory and Mt. Geumgang showed no significant change, the proportion of KTX and experience tour increased, and the proportion of other topics decreased in 2018 compared to 2017. In 2019, the proportion of KTX and experience tour decreased, but the proportion of other topics showed no significant change.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.92-104
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• 2021

This study aims to analyze online news articles to identify social issues related to patient safety and compare the changes in these issues before and after the implementation of the Patient Safety Act. This study performed text mining through the R program, wherein 7,600 online news articles were collected from January 1, 2010, to March 5, 2020, and examined using keyword analysis, topic modeling, and word co-occurrence network analysis. A total of 2,609 keywords were categorized into 8 topics: "medical practice", "medical personnel", "infection and facilities", "comprehensive nursing service", "medicine and medical supplies", "system development and establishment for improvement", "Patient Safety Act" and "healthcare accreditation". The study revealed that keywords such as "patient safety awareness", "infection control" and "healthcare accreditation" appeared before the implementation of the Patient Safety Act. Meanwhile, keywords such as "patient safety culture". and "administration and injection" appeared after the act's implementation with improved ranking of importance pertaining to nursing-related terminology. Interest in patient safety has increased in the medical community as well as among the public. In particular, nursing plays an important role in improving patient safety. Therefore, the recognition of patient safety as a core competency of nursing and the persistent education of the public are vital and inevitable.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.29-43
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• 2021

Coinciding with the advent of COVID-19, online education is on the rise both domestically and globally, and has become an absolutely necessary and irreplaceable form of education. It is a very curious question what the perception of people about the suddenly growing form of education is, and how it has changed. This study investigated changes in consumers' perception of online education using big data. To this end, we divided the time into four stages: before COVID-19 (November to December 2019), after the triggering of COVID-19 (January to February 2020), right after the online classes started (March to April 2020), after experiencing some online education (May to June 2020). Then we conducted text mining, namely, keyword frequency analysis, network analysis, word cloud analysis, and sentiment analysis were performed. The implications derived as a result of the analysis can help education policy makers and educators working in the field to improve online education quality and establish its future directions.