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소셜 빅 데이터 분석을 통한 지방대학의 위기 분석과 그 극복 방안

Analysis and Overcoming Measures of Crisis in Local Universities through Social Big Data Analysis

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주제분류행정학

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(Purpose) Recently, universities have called for a comprehensive and strategic overhaul of human resource development due to changes in the number of school-age population, entering an aging society, diversity in education demand due to population diversification, popularization of higher education, and non-face-to-face learning due to Covid-19. In particular, special measures are needed as the crisis at local universities is accelerating due to the disappearance of local areas and a sharp drop in the number of admissions population. To establish an effective and responsive university support policy, social changes and opinions which cannot be identified by simple questionnaire survey and interviews with the participants should be reflected through the social big data in which various social perceptions and participants’ opinions are explicitly and implicitly reflected. In this paper, I analyzed how university members and the general public are changing their perceptions of the crisis of local universities due to the decrease in local population over the past five years through social big data analysis. (Design/methodology/approach) Data collection period is a total of 5 years from April 1st 2016 to March 31st 2021, and the data were collected yearly basis. Collection channels are blogs (Naver, Daum) and Facebook. Keywords were crisis of local universities, lack of recruitment, closure of universities, abolition of departments, enrollment quota, recruitment rate of freshmen, shortage of enrollment rate, etc.. Textom and UCINET6 / Netdraw were used as analysis tools. Regarding analysis techniques, keywords were identified through the analysis of occurrence frequency of yearly collected data, and yearly centrality (connection centrality, eigenvetor centrality) were analyzed. Based on yearly keywords, semantic network analysis was subsequently conducted. Total number of collected data was 97,491, (Findings) According to an analysis of the annual topic terms, keywords such as "university", "enrollment recruitment", "not filled", "enrollment quota", "local university", "recruitment of students" and "new students" appeared frequently. As a result of checking the centrality value, keywords such as "university", "enrollment recruitment", "not filled", "enrollment quota", Other influential keywords included "grade", "local university", "new students", "recruitment", "admission", "student" and "local". The keyword's meaning network showed a high degree of connection between "university-local", "university-capital university", "university-local university", "university-enrollment recruitment", and "university-filling" (Research implications or Originality) In the policy alternative section, the government's policies related to the adjustment of the admission quota of universities, measures at the local university level, departmental efforts, and measures to attract students to local and local universities were proposed.

Abstract

(Purpose) Recently, universities have called for a comprehensive and strategic overhaul of human resource development due to changes in the number of school-age population, entering an aging society, diversity in education demand due to population diversification, popularization of higher education, and non-face-to-face learning due to Covid-19. In particular, special measures are needed as the crisis at local universities is accelerating due to the disappearance of local areas and a sharp drop in the number of admissions population. To establish an effective and responsive university support policy, social changes and opinions which cannot be identified by simple questionnaire survey and interviews with the participants should be reflected through the social big data in which various social perceptions and participants’ opinions are explicitly and implicitly reflected. In this paper, I analyzed how university members and the general public are changing their perceptions of the crisis of local universities due to the decrease in local population over the past five years through social big data analysis. (Design/methodology/approach) Data collection period is a total of 5 years from April 1st 2016 to March 31st 2021, and the data were collected yearly basis. Collection channels are blogs (Naver, Daum) and Facebook. Keywords were crisis of local universities, lack of recruitment, closure of universities, abolition of departments, enrollment quota, recruitment rate of freshmen, shortage of enrollment rate, etc.. Textom and UCINET6 / Netdraw were used as analysis tools. Regarding analysis techniques, keywords were identified through the analysis of occurrence frequency of yearly collected data, and yearly centrality (connection centrality, eigenvetor centrality) were analyzed. Based on yearly keywords, semantic network analysis was subsequently conducted. Total number of collected data was 97,491, (Findings) According to an analysis of the annual topic terms, keywords such as "university", "enrollment recruitment", "not filled", "enrollment quota", "local university", "recruitment of students" and "new students" appeared frequently. As a result of checking the centrality value, keywords such as "university", "enrollment recruitment", "not filled", "enrollment quota", Other influential keywords included "grade", "local university", "new students", "recruitment", "admission", "student" and "local". The keyword's meaning network showed a high degree of connection between "university-local", "university-capital university", "university-local university", "university-enrollment recruitment", and "university-filling" (Research implications or Originality) In the policy alternative section, the government's policies related to the adjustment of the admission quota of universities, measures at the local university level, departmental efforts, and measures to attract students to local and local universities were proposed.

참고문헌 (21)

  1. Brudney. J. L., "Handbook of Public Administration" (1989), Jossey-Bass Pub
  2. 변수연, "전문대-4년제 통합을 겪은 교수들의 경험 탐색을 통한 대학 통합의 현상 분석", 한국교육학연구, 24(2), pp.167~194 (2018), 안암교육학회[DOI]
  3. 조용란, "The Impact of Local Universities Students’ Youth Entrepreneurship on Intention of Start - Up and Its Revitalization Strategies", 한국비교정부학보, 20(2), pp.245~281 (2016), 한국비교정부학회[DOI]
  4. 조용란, "Activation Plan for Start-up of University Students through Social Big Data Analysis", 한국비교정부학보, 22(1), pp.155~178 (2018), 한국비교정부학회[DOI]
  5. Choi, Soo-Jin, "Network Analysis for Communication Research" (2016), Communication Books
  6. Han, Tae-Hee, "2021 Summer Conference of KAPA, discussions", ~ (2021)
  7. 정기오, "지방대학위기 현상과 원인:지방균형발전정책의 담론분석을 위한 시론", 한국교육, 30(2), pp.233~260 (2003), 한국교육개발원
  8. 주상현, "Exploring the Future Directions for the Government-Funded University Development Projects Using Social Network Analysis: Based on the Editorials on University Development and the Fourth Industrial Revolution Published in Major Korean Newspapers", 한국거버넌스학회보, 25(1), pp.237~256 (2018), 한국거버넌스학회[DOI]
  9. Ju, Sang-Hyun, "University entrance crisis and survival of local universities", 2021 Summer Conference of KAPA(Restructuring Government and Public Service to the Era of Digital Transformation and Pandemic), presentation paper (2021)
  10. Kim, Min-Hee, "Improvement direction and challenges of University Financial Support Projects", < Yeungnam University, Research center for higher education policy, 2016, ~
  11. 김성우, "빅데이터의 정책적 활용과 과제 - 빅데이터 추진 현황과 정책적 적용 쟁점을 중심으로 -", 한국비교정부학보, 18(3), pp.309~324 (2014), 한국비교정부학회[DOI]
  12. 곽재현, "빅데이터를 활용한 욜로(YOLO) 현상 분석: 여행소비를 중심으로", 관광연구저널, 32(2), pp.21~34 (2018), 한국관광연구학회[DOI]
  13. Kwal, Oak-Gum, "2021 Summer Conference of KAPA, discussions", ~ (2021)
  14. Lee, Mi-Sook, "Analysis and utilization of characteristics of each environment based on social big data", 2021 Summer Conference of Korea Environmental Policy And Administration Society. presentation paper (2014)
  15. Lee, Sang-Yup, "Column", DaillyCC, ~ (2020)
  16. Lee, Sang-Yup, "Column", DaillyCC, ~ (2021)
  17. Lee, Soo-Sang, "Network Analysis Methodology" (2012), Nonhyeong
  18. 명성준, "Change Analysis and Development Direction of BK21 PLUS Project: Focusing on Social Big Data Analysis", 한국자치행정학보, 31(4), pp.69~87 (2017), 한국자치행정학회[DOI]
  19. O, Ik-Geun, "Study on the Awareness of Korean Tourism through Big Data Analysis", Tourism Sciences Society of Korea, International Tourism Conference, presentation paper, 78(3), pp.595~602 (2015)
  20. 손준종, "지방사립대학교수의 삶에 관한 연구", 교육문제연구, (24), pp.97~125 (2006), 교육문제연구소
  21. 송태민, "소셜 빅데이터 기반 저출산 정책 수요분석", 통계연구, pp.72~95 (2016), 통계청

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