• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.297-304
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• 2021

This study aims not only to access the visual thought-oriented approach that has been implemented in established art therapy and education but also to integrate language education and therapeutic approach to support the development of school-age children. Thus, text mining technique was applied to search for areas where different areas of language and art can be integrated. This research was conducted in accordance with the procedure of basic research, preliminary DB construction, text screening, DB pre-processing and confirmation, stop-words removing, text mining analysis and the deduction about the convergent areas. These results demonstrated that this study draws convergence areas related to regional, communication, and learning functions, areas related to problem solving and sensory organs, areas related to art and intelligence, areas related to information and communication, areas related to home and disability, topics, conceptualization, peer-related areas, integration, reorganization, attitudes. In conclusion, this study is meaningful in that it established a framework for designing an activity-centered convergence program of art and language in the future and attempted a holistic approach to support child development.

• Korean Journal of Childcare and Education
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• v.18 no.5
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• pp.79-99
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• 2022

Objective: This study was conducted to examine the significant factors affecting media device addiction using the data mining technique for large-scale data from the Panel Study on Korean Children Survey (PSKC). The PSKC data of this study were gathered from the elementary school students in their 10th survey (1,286 3rd grade students). Methods: The SPSS 21.0 program was used for data mining decision tree analysis, and the results are as follows. Results: First, the most important predictor of media device addiction was planning-organization which was among the sub-factors of executive function. Second, as a result of the decision tree analysis, the children with the highest probability of addiction to media devices were ones that had difficulties in planning and organizing, had mothers with a permissive parenting attitude felt difficulties in controlling behavior, and were alone at home for more than two hours a day without any adult supervision. Conclusion/Implications: The results of this study can help guide the direction of future research related to children's addiction to media devices by exploring and analyzing factors that significantly affect children's addiction to media devices.

• Journal of the Korean Dietetic Association
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• v.30 no.4
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• pp.227-239
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• 2024

This study aimed to analyze the factors influencing the adaptation of novice nutrition teachers through big data analysis and to propose strategies for enhancing this process. Data were collected from internet portals using the keywords 'novice nutrition teacher' and 'nutrition teacher' from May 25, 2021, to May 25, 2024. Text mining techniques, including frequency analysis, semantic network analysis, and CONvergence of iterated CORrelations (CONCOR) analysis, were employed. Key terms such as 'teacher', 'nutrition', 'career', 'school', and 'school meals' exhibited high frequency and centrality, indicating the multifaceted roles of novice nutrition teachers and the need for increased support. Excessive workload and stress related to school meal management negatively impacted adaptation, highlighting the need for systematic management and capacity-building training programs. Mentoring and consulting systems played a crucial role in enhancing professional development, leading to better adaptation and higher job satisfaction. Additionally, stress and anxiety during the appointment preparation process were significant factors influencing adaptation, suggesting the need for improvements in the training curriculum at teacher education institutions. These findings provide valuable insights for developing policies to support the adaptation of novice nutrition teachers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.88-95
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• 2021

This study aims to provide implications for establishing support policies for students by empirically analyzing data on university students dropouts. To this end, data of students enrolled in D University after 2017 were sampled and collected. The collected data was analyzed using topic modeling(LDA: Latent Dirichlet Allocation) technique, which is a probabilistic model based on text mining. As a result of the study, it was found that topics that were characteristic of dropout students were found, and the classification performance between groups through topics was also excellent. Based on these results, a specific educational support system was proposed to prevent dropout of university students. This study is meaningful in that it shows the use of text mining techniques in the education field and suggests an education policy based on data analysis.

• Journal of Korean Home Economics Education Association
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• v.36 no.1
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• pp.39-54
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• 2024

This study analyzed Naver, Daum, Google, YouTube, and Twitter using TEXTOM for two years and four years as of January 18, 2020. The results are as follows. First, the total number and volume of keyword search data related to youth and diet were slightly higher after COVID-19, showing that interest increased due to COVID-19. Second, as a result of frequency analysis, 'education' was the highest before COVID-19, and 'health' was the highest after COVID-19, showing that interest in health is increasing due to the increased importance of health and immunity due to COVID-19. Third, as a result of frequency weight analysis of the top 50 keywords, 'education' showed the highest frequency before COVID-19, and 'acne' after COVID-19. Fourth, the results visualized using word cloud showed that the keywords 'education' before COVID-19 and 'health' after COVID-19 appeared the largest and boldest, showing the highest frequency and importance. As a result of the above results, we were able to use the text mining method to apply it to eating habits, and we used materials visualized as a highly readable word cloud in units such as eating problems in adolescence and balanced meal planning and selection in the home economics curriculum to improve the teaching of the class. The direction of proper eating habits education, including using it as a medium, was presented.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.433-447
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• 2013

The purpose of this study is to find out the factors of the students' achievement on the computer training courses which are based on computer practice. In order to improve the academic achievement of the students, it is necessary to analyze the factors affecting academic achievement and apply the results of the analysis to education. In particular, it is necessary to study for finding out factors of the academic achievement in practical computer training courses, because these courses are different from other courses focusing on the theory. In this study, in order to find out the factors, the logistic regression analysis and the decision tree analysis which is the field of data mining were peformed. For the experimental data, the test results of the MOS certification of the S university in seoul were used. Through logistic regression analysis it is found that the factors of the professors, class size, lecture time, group(lecture period) are important in order. Through decision tree analysis of data mining, it is found that there are some additional factors ; entrance year, whether the course is retaken, and the classroom environment. and these various factors effect the academic achievement compositively as identified through the model tree. The tree model was presented as a result of the analysis, and the importance of the factors is expressed numerically from multiple tree models by using the proposed mathematical formula.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.81-88
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• 2011

Association rule mining searches for interesting relationships among items in a given database. There are three primary quality measures for association rule, support and confidence and lift. In order to improve the efficiency of existing mining algorithms, constraints were applied during the mining process to generate only those association rules that are interesting to users instead of all the association rules. When we create relation rule, we can often find a lot of rules. This can find rule that direct relativity by intervening variable does not exist. In this study we try to discovery an insignificant rule in association rules by intervening variable. Result of this study can understand relativity about rule that is created in relation rule more exactly.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.97-105
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• 2020

This study was to identify the main predictors of smartphone overdependece of infants perceived by mothers and to provide basic data useful for education and practice. For this, data-mining decision tree analysis was performed using SPSS program, and the fianl 410 researches were used for analysis. The results. In the case of the whole infants, the most important predictor of smartphone dependence was father's leisure activity parenting participation. For boys, their father's leisure activity was the most dependent on their smartphone dependence. However, even if father's participation in leisure activities was high, smartphone overdependence increased again when mother's overprotection and permissive attitude were high. Finally, For girls, the most influential variable on smartphone dependence was warmth and encouragement among mothers' parenting attitudes.

• The Korean Fashion and Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• Child Health Nursing Research
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• v.16 no.1
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• pp.84-92
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• 2010

Purpose: One of the main reasons why mothers quit breast feeding is that the volume of breast milk is inadequate due to insufficiency in suckling. We believe suckling experience may be a factor affecting nipple confusion. So an alternative feeding method, namely cup, spoon, finger, or nasogastric tube feeding may be needed to prevent nipple confusion. The purpose of this study was to construct a predictive model for demand for alternative feeding education by nurses. Methods: A descriptive design with structured self-report questionnaires was used for this study. Data from 175 nurses working in hospitals in Busan were collected between April 1 and 15, 2009. Data were analyzed by decision tree method, one of the data mining techniques using SAS 9.1 and Enterprise Miner 4.3 program. Results: Of the nurses, 81.1% demanded alternative feeding education and 5 factors showed that most of them expressed intention to pay, desire to know about alternative feeding, age, and learning experience. From these results, the derived model is considered appropriative for explaining and predicting demand for alternative feeding education. Conclusion: This confirms that knowledge and compliance in alternative breast feeding for newborn babies should be correct and any inaccuracies or insufficient information should be supplemented.