• Human Ecology Research
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• 제60권4호
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Journal of Environmental Impact Assessment
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• 제6권2호
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• pp.165-180
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• 1997

Mitigation measures in EIS are the important factors in the effectiveness of EIS. This paper analyzed the content of mitigation measures described in the 30 EISs selected, using 7 analysis items in order to discover the degree of effectiveness of mitigation measures. 30 EISs used in this study were selected through variance maximization strategy, and the 7 analysis items were; 1) thoroughness of mitigation content, 2) quantification of mitigation content, 3) explicit description of mitigation effect, 4) likelihood of mitigation effect, 5) impacted area of mitigation effect, 6) time frame of mitigation effect, and 7) environmental impact of mitigation measures. The results showed that the effectiveness of mitigation measures in the analyzed EISs was relatively low both in the appropriateness and in the specificity. It was suggested that in order to improve the appropriateness of EIS as a decision making tool, the effect of mitigation measures, as well as the mitigation measures themselves, should be studied and described more thoroughly and specifically.

• 보존과학연구
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• 통권31호
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• pp.131-140
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• 2010

This study shows the comparison of chemical compositions of main component with other bronze bells after the research on the component analysis by non-destructive XRF analysis. There are shrinkage cavities caused by the shrinkage defect and pores with pollutants on Bronze Bell of Heungguksa Temple with gamma radiation images and 77.3% of copper, 8.4% of tin and 10.9% of lead were determined as the main components of it with XRF analysis The tin content of Brozen Bell of Heungguksa Temple is less than those (11~18%) of other bronze bells but the lead content of that is higher. The lead content of it shows 10.91% which is quite high while generally the lead contents of other bells were controlled lower than 2.1%. Buddhist bells have the different lead content according to the period. The lead content was low until Silla Dynasty and Unified Silla period but it has been getting higher since some point of Koryo Dynasty. It is assumed that expensive copper and tin were replaced with lead.

• Human Ecology Research
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• 제60권1호
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• pp.1-20
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• 2022

This paper presents a social network analysis of changes in Home Economics education content loaded on YouTube before and after the outbreak of COVID-19. From January 1, 2008 to June 30, 2021, a basic analysis was conducted of 761 Home Economics education videos loaded on YouTube, using NetMiner 4.3 to analyze important keywords and the centrality of video titles and full texts. Before COVID-19, there were 164 Home Economics education videos posted on YouTube, increasing significantly to 597 following the emergence of the pandemic. In both periods, there was more middle school content than high school content. The content in the child-family field was the most, and the main keywords were youth and family. Before COVID-19, a performance evaluation indicated that the proportion of student content was high, whereas after the outbreak of the disease, teacher content increased significantly due to the effect of distance learning. However, compared with video use, the self-expression and participation of users were lower in both periods. The centrality analysis indicated that in the title, 'family' exhibited a high degree of both centrality and eigenvector centrality over the entire period. Degree centrality of the video title was found to be high in the order of class, online, family, management, etc. after the outbreak of COVID-19, and the connection of keywords was strong overall. Eigenvector centrality indicated that career, search, life, and design were influential keywords before COVID-19, while class, youth, online, and development were influential keywords after COVID-19.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1528-1528
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• 2001

Ginseng cultivated in different country or growing condition has generally different components such as saponin and protein, and it relates to efficacy and action. Protein content assumes by nitrogen content in ginseng radix. Nitrogen content could be determined by chemical analysis such as kjeldahl or extraction methods. However, these methods require long analysis time and result environmental pollution and sample damage. In this work we investigated possibility of non-destructive determination of nitrogen content in ginseng radix using near-infrared spectroscopy. Ginseng radix, root of Panax ginseng C. A. Meyer, was studied. Total 120 samples were used in this study and it was consisted of 6 sample sets, 4, 5 and 6-year-old Korea ginseng and 7, 8 and 9-year-old China ginseng, respectively. Each sample set has 20 sample. Nigrogen content was measured by electronic analysis. NIR reflectance spectra were collected over the 1100 to 2500 nm spectral region with a InfraAlyzer 500C (Bran+Luebbe, Germany) equipped with a halogen lapmp and PbS detector and data were collected every 2 nm data point intervals. The calibration models were carried out by multiple linear regression (MLR) and partial least squares (PLS) analysis using IDAS and SESAME software. Result of electronic analysis, Korean ginseng were different mean value in nitrogen content of China ginseng. Ginseng tend to generally decrease the nitrogen content according as cultivation year is over 6 years. The MLR calibration model with 8 wavelengths using IDAS software accurately predicted nitrogen contents with correlation coefficient (R) and standard error of prediction of 0.985 and 0.855%, respectively. In case of SESAME software, the MLR calibration with 9 wavelength was selected the best calibration, R and SEP were 0.972 and 0.596%, respectively. The PLSR calibration model result in 0.969 of R and 0.630 of RMSEP. This study shows the NIR spectroscopy could be applied to determine the nitrogen content in ginseng radix with high accuracy.

• Journal of the Korean Society for information Management
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• 제31권4호
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• pp.49-68
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• 2014

The purpose of this study is to perform content analysis of research articles using the language network analysis method in Korea and catch the basic point of the language network analysis method. Six analytical categories are used for content analysis: types of language text, methods of keyword selection, methods of forming co-occurrence relation, methods of constructing network, network analytic tools and indexes. From the results of content analysis, this study found out various features as follows. The major types of language text are research articles and interview texts. The keywords were selected from words which are extracted from text content. To form co-occurrence relation between keywords, there use the co-occurrence count. The constructed networks are multiple-type networks rather than single-type ones. The network analytic tools such as NetMiner, UCINET/NetDraw, NodeXL, Pajek are used. The major analytic indexes are including density, centralities, sub-networks, etc. These features can be used to form the basis of the language network analysis method.

• Journal of Convergence for Information Technology
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• 제10권9호
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• pp.251-261
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• 2020

The purpose of this research is to examine the relationship between motivation, reliability, satisfaction, and sustainability of YouTube content and suggest the implications needed for content consumption or production of beauty workers in the future as K-beauty's interest is increasing due to the global Hallyu craze, and beauty industry workers are also watching YouTube hair care content and exchanging the latest trends. The analysis methods included frequency analysis, factor analysis, reliability analysis, correlation analysis, and multi-recovery analysis. As a result, the correlation between the motivation for content use, reliability, satisfaction, and persistence was shown, the motivation for content use was statistically significant (+) and the impact of content reliability on YouTube content satisfaction and sustainability (a significant statistical+).

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.326-331
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• 2023

Korea's cultural content industry can develop into another unique tourism industry. However, since other prior studies focus on the Japanese content industry, this study identifies modern industrial trends by combining the unique characteristics of Korean content, that is, cultural content tourism, and the analysis ability of big data. The current status and direction of the cultural content tourism industry were studied by utilizing the extensive information collection and in-depth analysis capabilities of big data, and as a result, it was confirmed that the trend of the cultural content industry is related to the business aspect of cultural content, not the pure content interest of cultural content. This shows that Korean cultural contents have a strong business aspect. As a limitation, when research design was conducted using social media big data, the age, gender, etc. of the subject analyzed with unique anonymity could not be known. The Korean cultural content industry is expected to be successful in terms of business.

• Journal of the Korea Convergence Society
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• 제13권2호
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• pp.303-312
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• 2022

In this study, the importance-performance analysis (IPA) of the content components of remote graduate students was conducted. To this end, an online survey of 221 remote graduate students at J University obtained the following results. First, the importance of content components by area was in the order of learning content, interaction, teaching-learning strategy, and evaluation, and the degree of execution was in the order of teaching-learning strategy, interaction, evaluation, and learning content. Second, it was found that there were significant differences between importance and performance in the four areas of content components: learning content, teaching-learning strategy, interaction, and evaluation. The importance-execution analysis (IPA) was conducted in two dimensions: region-specific and item-specific, and the results are as follows. The learning content was found to be the maintenance area, the teaching-learning strategy and interaction were the key improvement areas, and the evaluation area was the overinvestment area. The results of this study can be used as basic data to diagnose the present of remote graduate school content and to gauge what needs to be improved in the future based on it.

• Journal of Korea Multimedia Society
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• 제16권6호
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• pp.735-745
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• 2013

The emergence of Web 2.0 technologies, such as blogs and wiki, enable even naive users to easily create and share content on the Web using freely available content sharing tools. Wide availability of almost free data and promiscuous sharing of content through social networking platforms created a content borrowing phenomenon, where the same content appears (in many cases in the form of extensive quotations) in different outlets. An immediate side effect of this phenomenon is that identifying which content is re-used by whom is becoming a critical tool in social network analysis, including expert identification and analysis of information flow. Internet-scale reuse detection, however, poses extremely challenging scalability issues: considering the large size of user created data on the web, it is essential that the techniques developed for content-reuse detection should be fast and scalable. Thus, in this paper, we propose a $qSign_{lsh}$ algorithm, a mechanism for identifying multi-sentence content reuse among documents by efficiently combining sentence-level evidences. The experiment results show that $qSign_{lsh}$ significantly improves the reuse detection speed and provides high recall.