• Title/Summary/Keyword: Education Data Mining

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News data LDA on North Korean defector entrepreneurship: Focusing on the comparison of government policies from 2013 to 2021 (북한이탈주민 창업에 관한 뉴스 데이터 토픽 모델링 분석: 2013~2021년까지 정부 정책 비교를 중심으로)

  • Mun, Jun-Hwan
    • Journal of Digital Convergence
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    • v.20 no.3
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    • pp.145-155
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    • 2022
  • North Korean defectors are experiencing economic hardship due to the prolonged COVID-19 outbreak. In order to solve this problem, interest in starting a business is increasing. This study targeted the current and previous government, and discovered major topics through text mining of news data on North Korean defector starting a business to examine the start-up support policies according to the keynote of the present regime. Additionally, key factors for successful start-ups were derived through interviews with North Korean defectors who have done them. As a result of the analysis, it is necessary to focus on women and the youth, and to actively expand specialized entrepreneurship education and financial support for North Korean defectors. In addition, it was confirmed that there is a need for a practical and continuous entrepreneurship education program.

An empirical approach to analyzing effects of disease and activity limit on depression prevalence rate in the elderly depending on stress experience: KNHANES Data Analysis (스트레스 경험 유무에 따른 질병 및 활동제약이 고연령층 우울증에 미치는 영향에 관한연구: 국민건강영양조사 자료분석)

  • Jeon, Hyeon Gyu;Sim, Jae Mun;Lee, Kun Chang
    • Korean Journal of Health Education and Promotion
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    • v.33 no.1
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    • pp.13-22
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    • 2016
  • Objectives: By using six years of KNHANES dataset (2008~2013) about 60 ages older people, we analyzed how the depression prevalence rate in the elderly is influenced by disease and activity limit. Especially, to add a sense of more reality, we adopted stress experience as a control variable to see how the depression prevalence rate in the elderly is influenced by disease and activity limit depending on the stress experience. Methods : We adopted six years of KNHANES dataset, indicating that our results were based on long period of time capable of considering temporal patterns in the depression prevalence rate in the elderly. Total 1,160 elderly people in KNHANES were selected for our empirical analyses. Dependent variable is either 0 or 1 depending on whether the elderly people feel depression. Main explanatory variables for our study include disease and activity limit. Logistic regression analysis was applied for two group such as stress experience and non-experience. Results : According to the empirical results, stress factor is found to be significant in explaining the depression in the elderly. Depression prevalence rate increased when the elderly has stress experience: chronical disease(OR=1.650), chronical disease with activity limit(OR=3.388), non-chronical disease with stress(OR=11.841) chronical disease with stress (OR=13.561) and chronical disease with activity limit and stress(OR=28.691). Conclusions: The finding suggest that the Countermeasures of elderly's depression alleviation should include stress management.

A Study of The Determinants of Turnover Intention and Organizational Commitment by Data Mining (데이터마이닝을 활용한 이직의도와 조직몰입의 결정요인에 대한 연구)

  • Choi, Young Joon;Shim, Won Shul;Baek, Seung Hyun
    • Journal of the Korea Society for Simulation
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    • v.23 no.1
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    • pp.21-31
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    • 2014
  • In this article, data mining simulation is applied to find a proper approach and results of analysis for study of variables related to organization. Also, turnover intention and organizational commitment are used as target (dependent) variables in this simulation. Classification and regression tree (CART) with ensemble methods are used in this study for simulation. Human capital corporate panel data of Korea Research Institute for Vocation Education & Training (KRIVET) is used. The panel data is collected in 2005, 2007, and 2009. Organizational commitment variables are analyzed with combined measure variables which are created after investigation of reliability and single dimensionality for multiple-item measurement details. The results of this study are as follows. First, major determinants of turnover intention are trust, communication, and talent management-oriented trend. Second, the main determining factors for organizational commitment are trust, the number of years worked, innovation, communication. CART with ensemble methods has two ensemble CART methods which are CART with Bagging and CART with Arcing. Comparing two methods, CART with Arcing (Arc-x4) extracted scenarios with very high coefficients of determination. In this study, a scenario with maximum coefficient of determinant and minimum error is obtained and practical implications are presented. Using one of data mining methods, CART with ensemble method. Also, the limitation and future research are discussed.

A Study on Gamification Consumer Perception Analysis Using Big Data

  • Se-won Jeon;Youn Ju Ahn;Gi-Hwan Ryu
    • International Journal of Advanced Culture Technology
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    • v.11 no.3
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    • pp.332-337
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    • 2023
  • The purpose of the study was to analyze consumers' perceptions of gamification. Based on the analyzed data, we would like to provide data by systematically organizing the concept, game elements, and mechanisms of gamification. Recently, gamification can be easily found around medical care, corporate marketing, and education. This study collected keywords from social media portal sites Naver, Daum, and Google from 2018 to 2023 using TEXTOM, a social media analysis tool. In this study, data were analyzed using text mining, semantic network analysis, and CONCOR analysis methods. Based on the collected data, we looked at the relevance and clusters related to gamification. The clusters were divided into a total of four clusters: 'Awareness of Gamification', 'Gamification Program', 'Future Technology of Gamification', and 'Use of Gamification'. Through social media analysis, we want to investigate and identify consumers' perceptions of gamification use, and check market and consumer perceptions to make up for the shortcomings. Through this, we intend to develop a plan to utilize gamification.

A Study on Big Data-Based Analysis of Risk Factors for Depression in Adolescents

  • Chun-Ok Jang
    • International Journal of Advanced Culture Technology
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    • v.11 no.4
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    • pp.449-455
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    • 2023
  • The purpose of this study is to explore adolescent depression, increase understanding of social problems, and develop prevention and intervention strategies. As a research method, social big data was used to collect information related to 'youth depression', and related factors were identified through data mining and analysis of related rules. We used 'Sometrend Biz Tool' to collect and clean data from the web and then analyzed data in various languages. The study found that online articles about depression decreased during the school holidays (January to March), then increased from March to the end of June, and then decreased again from July. Therefore, it is important to establish a government-wide depression management monitoring system that can detect risk signs of adolescent depression in real time. In addition, regular stress relief and mental health education are needed during the semester, and measures must be prepared to deal with at-risk youth who share their depressed feelings in cyberspace. Results from these studies can be expected to provide important information in investigating and preventing youth depression and to contribute to policy development and intervention.

Design and Implementation of A Student Information Mining System (학생정보마이닝 시스템의 설계 및 구현)

  • Kong, Hyun-Seon;Kim, Myung
    • The Journal of Korean Association of Computer Education
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    • v.6 no.1
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    • pp.55-63
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    • 2003
  • Elementary schools and middle schools currently manage their student data by using the education administration system. One of its sub-systems called 'the academic affairs support system' is especially dedicated to handle school and academic affairs data. By allowing simple data search and statistical data calculations, it helps teachers easily integrate and manage education information resources. However, it is not easy for teachers to analyze the correlations among student data. In this paper, we showed by examples that a lot of meaningful information can be extracted by analyzing the relations among student data. Based on the results, we designed and implemented SIMS as a tool to provide teachers with such services. SIMS makes use of Association Rules for data correlation analyses. SIMS can be used in connection with the academic affairs support system, and is much easier to use than previously developed commercial products for similar services.

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Study on Educational Utilization Methods of Big Data (빅데이터의 교육적 활용 방안 연구)

  • Lee, Youngseok;Cho, Jungwon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.12
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    • pp.716-722
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    • 2016
  • In the recent rapidly changing IT environment, the amount of smart digital data is growing exponentially. As a result, in many areas, utilizing big data research and development services and related technologies is becoming more popular. In SMART learning, big data is used by students, teachers, parents, etc., from a perspective of the potential for many. In this paper, we describe big data and can utilize it to identify scenarios. Big data, obtained through customized learning services that can take advantage of the scheme, is proposed. To analyze educational big data processing technology for this purpose, we designed a system for big data processing. Education services offer the measures necessary to take advantage of educational big data. These measures were implemented on a test platform that operates in a cloud-based operations section for a pilot training program that can be applied properly. Teachers try using it directly, and in the interest of business and education, a survey was conducted based on enjoyment, the tools, and users' feelings (e.g., tense, worried, confident). We analyzed the results to lay the groundwork for educational use of big data.

EMI database analysis focusing on relationship between density and mechanical properties of sedimentary rocks

  • Burkhardt, Michael;Kim, Eunhye;Nelson, Priscilla P.
    • Geomechanics and Engineering
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    • v.14 no.5
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    • pp.491-498
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    • 2018
  • The Earth Mechanics Institute (EMI) was established at the Colorado School of Mines (CSM) in 1974 to develop innovations in rock mechanics research and education. During the last four decades, extensive rock mechanics research has been conducted at the EMI. Results from uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), point load index (PLI), punch penetration (PP), and many other types of tests have been recorded in a database that has been unexamined for research purposes. The EMI database includes over 20,000 tests from over 1,000 different projects including mining and underground construction, and analysis of this database to identify relationships has been started with preliminary results reported here. Overall, statistically significant correlations are identified between bulk density and mechanical strength properties through UCS, BTS, PLI, and PP testing of sedimentary, igneous, and metamorphic rocks. In this paper, bulk density is considered as a surrogate metric that reflects both mineralogy and porosity. From this analysis, sedimentary rocks show the strongest correlation between the UCS and bulk density, whereas metamorphic rocks exhibit the strongest correlation between UCS and PP. Data trends in the EMI database also reveal a linear relationship between UCS and BTS tests. For the singular case of rock coral, the database permits correlations between bulk density of the core versus the deposition depth and porosity. The EMI database will continue under analysis, and will provide additional insightful and comprehensive understanding of the variation and predictability of rock mechanical strength properties and density. This knowledge will contribute significantly toward the increasingly safe and cost-effective geostructures and construction.

Comparative Analysis of Work-Life Balance Issues between Korea and the United States (워라밸 이슈 비교 분석: 한국과 미국)

  • Lee, So-Hyun;Kim, Minsu;Kim, Hee-Woong
    • The Journal of Information Systems
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    • v.28 no.2
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    • pp.153-179
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    • 2019
  • Purpose This study collects the issues about work-life balance in Korea and United States and suggests the specific plans for work-life balance by the comparison and analysis. The objective of this study is to contribute to the improvement of people's life quality by understanding the concept of work-life balance that has become the issue recently and offering the detailed plans to be considered in respect of individual, corporate and governmental level for society of work-life balance. Design/methodology/approach This study collects work-life balance related issues through recruit sites in Korea and United States, compares and analyzes the collected data from the results of three text mining techniques such as LDA topic modeling, term frequency analysis and keyword extraction analysis. Findings According to the text mining results, this study shows that it is important to build corporate culture that support work-life balance in free organizational atmosphere especially in Korea. It also appears that there are the differences against whether work-life balance can be achieved and recognition and satisfaction about work-life balance along type of company or sort of working. In case of United States, it shows that it is important for them to work more efficiently by raising teamwork level among team members who work together as well as the role of the leaders who lead the teams in the organization. It is also significant for the company to provide their employees with the opportunity of education and training that enables them to improve their individual capability or skill. Furthermore, it suggests the roles of individuals, company and government and specific plans based on the analysis of text mining results in both countries.

Achievement and satisfaction research of the undergraduate orchestra club activities - A convergent aspects of statistical method and opinion mining (오케스트라 동아리 참여 대학생의 성취도 및 만족도 조사 - 통계적 방법과 오피니언 마이닝의 융합적 측면)

  • Choi, Sui;Choi, Kyoungho
    • Journal of the Korea Convergence Society
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    • v.6 no.4
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    • pp.25-31
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    • 2015
  • General student orchestra activity is known as desirable hobby for students of adolescent period, developing their creativity and sensitivity, give students sense of belonging and stability by resolving their social, emotional anxiety. Accordingly, his research investigated whether orchestra club activity also has similar effect on university students. As a result unlike of adolescent students, orchestra activity turned out to be not that helpful for the social, self-confidence improvement of university students, though achievement of the activity itself was high. Despite of the result, there exist positive factors; obviously the activity has positive factors analyzing through recognition analysis (opinion mining) using big data. Therefore national support is required also for the orchestra activity of the undergraduates.