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Inflow and outflow analysis of double majors using social network analysis

사회 연결망 분석을 이용한 복수전공 유입 및 유출 분석

  • Cho, Jang-Sik (Department of Informational Statistics, Kyungsung University)
  • 조장식 (경성대학교 정보통계학과)
  • Received : 2012.05.30
  • Accepted : 2012.07.02
  • Published : 2012.07.31

Abstract

Recently, the number of students who get double majors has tended to increase in many universities. As results, many problems occur because immoderate inflow of double-major students is concentrated in a specific popular department. In this paper, we study the characteristic of inflow and outflow of double majors using social network analysis and decision tree analysis. According to the results, SAT score affected the inflow of double majors the most. Additionally, department category, course evaluation score, employment rate also affected the inflow of double majors in the order named. On the other hand, department category affected the outflow of double majors the most. Additionally, SAT score, employment rate, course evaluation score also affected the outflow of double majors in the order named.

Acknowledgement

Supported by : 경성대학교

References

  1. Bigg, D., de Ville, B. and Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18, 49-62. https://doi.org/10.1080/02664769100000005
  2. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and regression trees, Wadsworth, Belmont
  3. Cho, J. S. (2010). The influence analysis of admission variables on academic achievements. Journal of the Korean Data & Information Science Society, 21, 729-736.
  4. Cho, J. S. (2011). Determinants of job finding using student's characteristic information. Journal of the Korean Data & Information Science Society, 22, 849-856.
  5. Choi, K. H. and Lee, Y. W. (2011). The deduction of objective linguistic information using statistical methods- The grouping of the possibility of interdisciplinary research. Journal of the Korean Data & Information Science Society, 22, 49-55.
  6. Choi, S., Kang, C., Choi, H. and Kang, B. (2011). Social network analysis for a soccer game. Journal of the Korean Data & Information Science Society, 22, 1053-1063.
  7. Huh, M. H. (2010). Introduction to social network analysis using R, Freedom Academy, Seoul.
  8. Kass, G. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119-127. https://doi.org/10.2307/2986296
  9. Kim, Y. H. (2007). Social network analysis, Pakyoungsa. Seoul.
  10. Loh, W. and Shih, Y. (1997). Split selection methods for classification trees. Statistica Sinica, Taiwan.
  11. Scott, J. (2000). Social network analysis : A handbook, Sage Publications, London.
  12. Son, D. W. (2010). Social network analysis, Kyungmoon Publishers, Seoul.
  13. SPSS Inc. (1988). AnswerTree 1.0 user's guide, SPSS Inc., Chicago.
  14. Wasserman, S. and Faust, K. (1995). Social network analysis : Method and applications, Cambridge University Press, Cambridge.

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