Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

A Case Study on Characteristics of Gender and Major in Career Preparation of University Students from Low-income Families: Application of Text Frequency Analysis and Association Rules

저소득층 대학생들의 진로준비과정에서의 성별·전공별 특성에 대한 사례연구: 텍스트 빈도분석과 연관분석의 적용

  • Lee, Jihye (College of General Education, Hallym University) ;
  • Lee, Shinhye (Department of Education, Seoul National University)
  • 이지혜 (한림대학교 교양기초교육대학) ;
  • 이신혜 (서울대학교 교육학과)
  • Received : 2018.10.15
  • Accepted : 2018.12.20
  • Published : 2018.12.28
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to understand and to infer the implications from the career preparation experiences of low-income university students in the context of high youth unemployment rate and the polarization of the social classes. For this purpose, we selected 13 university students who received scholarship from the S scholarship foundation and conducted analysis using text mining techniques based on the six-time interviews. According to the results, university students seem to be influenced by home environment and income level when recalling previous academic experience or designing career during the interview process. Also, these differences were found to have different characteristics according to gender and major. This study is meaningful in that the qualitative research data is analyzed by applying the text mining technique in a convergent way. As a result, the college life and career preparation of low-income university students were explored through the frequency and relation of words.

이 연구는 청년들의 높은 비정규직 비율과 계층 양극화의 우려 속에서 저소득층 대학생의 진로준비과정에 대해 이해하고 시사점을 구하기 위한 것이다. 이를 위하여 S 장학재단에서 장학금 지원을 받는 13명의 대학생들을 연구 대상으로 선정하였고, 6회의 인터뷰를 진행하여 그 축어록을 바탕으로 텍스트마이닝 기법을 활용한 분석을 실시하였다. 분석 결과, 대학생들은 인터뷰 과정에서 이전의 학업 경험을 회상하거나 진로를 설계할 때, 가정환경과 소득수준의 영향을 받는 것으로 보이며, 이러한 차이는 성별, 전공별로 다른 특성이 있는 것으로 나타났다. 이 연구는 질적 연구 방법으로 축적된 자료에 텍스트마이닝 기법을 융합적으로 적용하여 분석한 연구로 종래의 진로연구에 비하여 방법론적 확장을 시도했다는 의의를 갖고 있다. 그 결과, 저소득층 대학교 장학생들의 성별 및 전공별 진로준비과정의 차이를 대학생활 및 진로준비와 관련된 단어들의 관계를 통해 탐색적으로 살펴볼 수 있었다.

Keywords

DJTJBT_2018_v16n12_61_f0001.png 이미지

Fig. 2. Difference of words percentage by gender greater than one percent

DJTJBT_2018_v16n12_61_f0002.png 이미지

Fig. 3. Association analysis results - male

DJTJBT_2018_v16n12_61_f0003.png 이미지

Fig. 4. Association analysis results - female

DJTJBT_2018_v16n12_61_f0004.png 이미지

Fig. 5. Comparison of word frequency ratio by major

DJTJBT_2018_v16n12_61_f0005.png 이미지

Fig. 6. Association analysis results - humanitiesand social sciences

DJTJBT_2018_v16n12_61_f0006.png 이미지

Fig. 7. Association analysis results - science and engineering

DJTJBT_2018_v16n12_61_f0007.png 이미지

Fig. 8. Association analysis results - education

Table 1. List of 103 words (numbers in parentheses indicate frequency)

DJTJBT_2018_v16n12_61_t0001.png 이미지

Table 2. Percentage of words per session (%)

DJTJBT_2018_v16n12_61_t0002.png 이미지

Table 3. Percentage of words per gender (%)

DJTJBT_2018_v16n12_61_t0003.png 이미지

Table 4. Example of association analysis results - male

DJTJBT_2018_v16n12_61_t0004.png 이미지

Table 5. Example of association analysis results - female

DJTJBT_2018_v16n12_61_t0005.png 이미지

Table 6. Percentage of words per major (%)

DJTJBT_2018_v16n12_61_t0006.png 이미지

Table 7. Example of association analysis results - humanities and social sciences

DJTJBT_2018_v16n12_61_t0007.png 이미지

Table 8. Example of association analysis results - science and engineering

DJTJBT_2018_v16n12_61_t0008.png 이미지

Table 9. Example of association analysis results - education

DJTJBT_2018_v16n12_61_t0009.png 이미지

References

  1. H. R. Min. (2003). A Study on the Experiences and Costs of the Private Tutoring of University. Students for Preparing Employment. Korean Journal of Sociology of Education, 13(3), 133-149.
  2. J. S. Jung & H. H. Kim. (2009). Analysis on the Influential Factors on Private Tutoring Expenditure of Korean College Students. The Journal of Economics and Finance of Education, 18(3), 89-122.
  3. H. B. Park & S. S. Kim. (2011). An Analysis on The Effects of Individual Background, Employment Efforts, and University Selectivity on Labor Market Outcomes of College and University Graduates. Korean Journal of Sociology of Education, 21(3), 77-98.
  4. H. J. Chu. (2012). Individual and College Effects on New Graduates. The Korean Journal of Educational Administration, 30(1), 603-626.
  5. B. J. Kim & H. J. Seo. (2013). Analysis on Student and University Variables Affecting First Employment of College Graduates. The Journal of Economics and Finance of Education, 22(4), 243-268.
  6. K. H. Park, J. S. Kim, C. H. Kim, J. Y. Namkung, S. J. Beak, H. J. Yang, S. S. Kim, W. J. Kim, B. W. Ha & K. Y. Han. (2017). Comprehensive Analysis of Current Situation of Educational Gap in Korea, Korea Educational Development Institute. Chungcheongbuk-do: KEDI(RR2017-17).
  7. J. K. Ahn & H. J. Bae. (2011). The effects of working while in college on the labor market transitions and outcomes. Korean Journal of Sociology of Education, 21(4), 149-180.
  8. J. S. Yang. (2014). Graduation Status and Labor Market Performance of 4-year college graduates. KRIVET Issue Brief, 56. Seoul: KRIVET.
  9. J. H. Lee & J. E. Chae. (2013). Low-income Students' Adaptation to University. Journal of Lifelong Learning Society, 9(1), 1-27.
  10. H. N. Jung. (2016). Differences in worker characteristics and labor market performance by types of work experience at school. Employment issues, 9(5), 78-87.
  11. J. H. Lee & M. H. Hwang. (2017). Case Study on Career Preparation Experiences of University Students from Low-Income Families. Journal of Lifelong Learning Society, 13(3), 57-83. https://doi.org/10.26857/JLLS.2017.08.13.3.57
  12. J. S. Shim. (2011). Analysis of Conflict Frames Using Semantic Network Analysis. Korea Public Administration Journal, 20(2), 183-212.
  13. D. H. Goh & R. P. Ang. (2007). An introduction to association rule mining: An application in counseling and help-seeking behavior of adolescents. Behavior Research Methods, 39(2), 259-266. https://doi.org/10.3758/BF03193156
  14. G. Shmueli, N. R. Patel & P. C. ruce. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. New Jersey: John Wiley and Sons.
  15. R. Srikant &R. Agrawal. (1996, June). Mining quantitative association rules in large relational tables. Acm Sigmod Record, 25(2), 1-12. https://doi.org/10.1145/235968.233311