• Korean Journal of Culture and Social Issue
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• v.29 no.1
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• pp.147-170
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• 2023

This study explored keywords and key topics by collecting posts related to 'self-Iinjury' and 'suicide' through Twitter. The study subjects were selected as posts containing related hashtags related to self-injury and suicide from October 29, 2019 to November 30, 2020. Text mining based on collected posts resulted in a total of 11 key topics: -6 related to 'self-Iinjury' and 5 related to 'suicide'. The main message in the topic is as follows. First, looking at the main messages contained in the topic, they honestly expressed self-harm and suicide experiences that are difficult to express offline online, and used SNS as a channelpath for requesting help requests. Second, there were common and discriminatory characteristics in posts related to 'self-Iinjury' and 'suicide'. Although topics related to 'self-Iinjury' mainly revealed emotional control and interpersonal functions of self-harm, messages related to 'suicide' showed more clearly messages about suicide prevention addressing and social problems. These results are meaningful in that they can understand the opinions of people who have experienced self-harm and suicide accidents and the public voice on self-harm and suicide-related issues could be better understood, and that this study seeks for effective self-harm and suicide prevention and intervention measures for self-harm and suicide issues.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.27-39
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• 2023

This study aimed to develop SSTPBL (Student-led Simulation with Team and Problem-Based Learning), whichcombines TBL and PBL with a student-led method to strengthen knowledge application, nursing diagnosis ability, and collaboration ability among the core competencies of nurses. Then, SSTPBL was applied to nursing students, and the results were assessed. The data was collected from September 15, 2022, to December 21, 2022, with structured questionnaires and focus group interviews with 51 fourth-year nursing students at a university in A City. The collected data were analyzed using SPSS version 25.0 and topic analysis. As a results, it was effective in simulation experience satisfaction(t = 3.51, p < .01), vSim experience satisfaction(t = 3.50, p < .01), preparation as a prospective nurse(t = 3.73, p < .01), learning self-efficacy(t = 3.87, p < .01), collaborative self-efficacy (t = 4.30, p < .01), problem-solving ability(t = 5.26, p < .01), educational satisfaction(t = 3.54, p < .01), digital health equity(t = 2.18, p < .05). Through the qualitative data's topic analysis, six main topics were derived. The main topics were 'similar to clinical practice', 'difficulty in immersion', 'learning through others', 'learning through self-reflection', 'improving confidence through new experiences' and 'new teaching methods'. Based on the results of this study, it is expected that SSTPBL can be used in various ways as a new training method for prospective nurses in the face of growing clinical practice restrictions after the pandemic.

• Communications of Mathematical Education
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• v.38 no.1
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• pp.27-48
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• 2024

In order to prepare for changes in future society, cross-curricular learning is emphasized, and the need to link cross-curricular learning topics and subjects is increasing. However, there are few studies on how to deal with cross-curricular learning in mathematics education. This study analyzed the contents and methods of cross-curricular learning topics in subject-specific curriculum and mathematics textbooks. As a result of the study, the curriculum can be categorized into four types according to the variety of cross-curricular learning topics applied and the presence or absence of a main cross-curricular learning topic, and the mathematics curriculum belongs to the type where some cross-curricular learning topics are dealt with passively and there is no main topic. On the other hand, the analysis of 10 math textbooks for grades 5 and 6 according to the 2015 revised curriculum showed that, unlike the curriculum, various cross-curricular learning topics were applied in the textbooks, mainly environment and sustainable development education, safety and health education, career education, character education, and economic and financial education. In addition, in mathematics textbooks, cross-curricular learning topics appeared in various types such as materials, questions, explanations, illustrations, and in many cases, they appeared mainly as materials or illustrations. Based on these findings, implications were explored and suggested on how to integrate and apply cross-curricular learning topics in mathematics.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of the Korean Chemical Society
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• v.55 no.3
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• pp.400-404
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• 2011

Safflower is called as the 'beneficial flower' because 'it helps human health', and it was introduced as red flower in Tonguibogam due to the red color of floral leaf. From old times, it has been used for the material of cloth and rouge. Recently, polyphenol compound, the main ingredient of safflower, known as anti-aging and anti-oxidizing material in the healthy food industry becomes the emerging hot topic. This study aims to confirm by DDT (Disk Diffusion Test) assay, MTT assay, and NF-${\kappa}$B Luciferase activity inhibition assay in vitro that polyphenol compound, which is the main ingredient of safflower, has the anti-microbial efficacy to inhibit the growth of acne germs that make troubles for the teenagers or middle aged. Also it aims to evaluate its clinical efficacy on the acne skin, utilizing the facial cleansing cosmetic form of soap sample. This study can contribute to take a major step forward to the development of cosmetic soap for acne in the cosmeceutical industry.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.557-566
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• 2008

NGN(Next Generation Network) is a communication network which can make use of broadband and QoS-enabled transport technologies. One of the main service issues over NGN is a multimedia service, such as IPTV, using a multicast method. And overlay multicast technology is one of the promising solutions instead of traditional multicast technology which has a few problems, and supports flexibility and scalability for multicast services. Also, the main controversial topic in NGN and overlay multicast is QoS. In the present paper, we designed an agent in each receiver's network, and a manager which is in a source network and which manages the whole multicast network. Both of them are communicating with each other and applying resource policies to their multicast network. This mechanism enables overlay multicast to support QoS, focusing on RACF(Resource and Admission Control Functions) in NGN QoS architecture.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.443-448
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• 2020

The purpose of research is identifying visual factors affecting purchase of convenient store packed-meals through mobile applications. Previous studies have not covered mobile applications on purchase of convenient store packed-meals and this inspired the research topic. Document analysis and online survey are mainly implemented and 4 visual factors; typography, product image, main color, brand logo have been set as a research variables. It is revealed that consumers recognize product image prior to the rest and their purchase intentions are most significantly affected by product image. In conclusion, the product image should encourage consumers to have expectation on packed-meal and need to deliver credibility at the same time. Hence, the application should be designed to solely spotlight product image to lead consumer's concentration on it. The research can be further expanded by including non-visual factors as its variables or increasing scale of survey samples.

• Journal of the Korean earth science society
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• v.38 no.5
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• pp.378-392
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• 2017

Teacher quality is a topic of international concern, as it impacts student learning and teacher preparation. This study compared the undergraduate secondary science teacher preparation programs from two universities in Korea with those of Oregon, USA. We examined the programs' structural curricular coherence, conceptual curricular coherence, and curricular balance. Structural curricular coherence was determined by examining the overarching goals of the institutions' programs, the organization of the programs of study in terms of meeting those goals, and outside bodies of evidence. All universities were in structural coherence for various reasons. Conceptual curricular coherence was determined by examining students' perceptions of the connection between their preparation and their clinical practice. In case of Korea, most students from both universities were not satisfied with their practical preparation. In the US, the students from both institutions felt well prepared to transition to inservice teaching. To determine curricular balance, we examined the institutions' preparation programs looking at the credit hours taken in the four main areas of the teacher knowledge base: GPK (General Pedagogical Knowledge), SMK (Subject Matter Knowledge), PCK (Pedagogical Content Knowledge), and CK (Contextual Knowledge). The total credit hours taken in each category was very similar by country but the application and field component in the USA was far greater than those of Korea where the focus was heavily on SMK and PCK. The main reason for these may be the nations' licensing and employment processes.

• Journal of the Korean School Mathematics Society
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• v.16 no.2
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• pp.319-335
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• 2013

Chinese mathematical curriculum is divided 4 areas(number and algebra, space and figure, statistics and probability, practice and synthetic application). The purpose of this paper is to analyze the contents of the practice and synthetic application in yanbian elementary textbook. For this, 12-textbook which was published in yeonbeon a publishing company is analyze by topic, mathematical process, area of content and mathematical activity. mathematical process The following results have been drawn from this study. First, contextual backgrounds of practice are restricted in classroom. The contents of synthetic application are limited in connection of mathematical areas. Mathematical problem solving is a main in mathematical process, whereas reasoning activity is a few. Mathematical experience activity is a main in mathematical process, whereas synthetic activity is a few. We can use the suggestions of this paper for development of textbook and the contents of mathematical process.