The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Issue analysis of the admission officer system using topic analysis

토픽 분석을 이용한 학생부종합전형의 쟁점 분석

  • Hong, Younghee (Education Policy Institute, Busan Metropolitan City Office of Education)
  • 홍영희 (부산광역시교육청 교육정책연구소)
  • Received : 2019.02.11
  • Accepted : 2019.04.09
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

An important issues in Korea society in 2018 was the revision of the university entrance examination system. Among the discussions, in order to grasp what the issue of admission officer system is, attention was focused on the function of media such as monitoring and criticism as well as the tried topic analysis of related news articles. As a result, the reorganization of the College Scholastic Ability Test (CSAT) was derived and showed the sensitivity of Korean society towards the CSAT. Topics directly related to the admission officer system were the selection factor and fairness of the university entrance examination system in relation to the selection factor.

지난 2018년, 우리사회를 뜨겁게 달구었던 이슈 중 하나로 대입제도 개편에 관한 논쟁을 꼽을 수 있겠다. 그 중에서도 학생부종합전형에 대한 쟁점이 무엇인가를 파악하기 위해 감시와 비판이라는 언론의 기능에 주목하여 관련 뉴스 기사에 대한 토픽 분석을 시도해 보았다. 그 결과 수능체제 개편 논의가 비중있는 주제로 등장하여 수능시험에 대한 한국 사회의 민감성을 보여 주었다. 학생부종합전형과 직접적 관련이 있는 주제로는 학생부종합전형의 세부적인 선발 요소에 대한 논의가 등장하였고, 대입전형의 공정성에 관한 논의와 밀접한 관계를 보였다.

Keywords

GCGHDE_2019_v32n3_423_f0001.png 이미지

Figure 3.1. change in the number of news articles.

GCGHDE_2019_v32n3_423_f0002.png 이미지

Figure 3.3. The Rate of perplexity change.

GCGHDE_2019_v32n3_423_f0003.png 이미지

Figure 3.4. 5 topics obtained by latent Dirichlet allocation modeling.

GCGHDE_2019_v32n3_423_f0004.png 이미지

Figure 3.5. Visualization of 5 topics.

GCGHDE_2019_v32n3_423_f0005.png 이미지

Figure 3.2. 10-fold cross-validation of topic modelling.

Table 3.1. Number of final collected news articles

GCGHDE_2019_v32n3_423_t0001.png 이미지

Table 3.2. Some keyword of 5 topics

GCGHDE_2019_v32n3_423_t0002.png 이미지

References

  1. Arun, R., Suresh, V., Veni Madhavan, C. E., and Narasimha Murthy, M. N. (2010). On finding the natural number of topics with latent Dirichlet allocation: some observations. In Zaki, M.J., Yu, J. X., Ravindran, B., Pudi, V. (Eds), Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science, (Vol. 6118, pp. 391-402), Springer, Berlin, Heidelberg.
  2. Bae, J. W., Son, J. E., and Song, M. (2013). Analysis of twitter for 2012 South Korea presidential election by text mining techniques, Journal of Intelligence and Information Systems, 19, 141-156.
  3. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet allocation, Journal of Machine Learning Research, 3, 993-1022.
  4. Cao, J., Xia, T., Li, J., Zhang, Y., and Tang, S. (2009). A density-based method for adaptive LDA model selection, Neurocomputing, 72, 1775-1781. https://doi.org/10.1016/j.neucom.2008.06.011
  5. Cho, A., Lee, K. H., and Cho, W. S. (2015). Latent mobility pattern analysis of bus passengers with LDA, Journal of the Korean Data and Information Science Society, 26, 1061-1069. https://doi.org/10.7465/jkdi.2015.26.5.1061
  6. Choi, H. D. and An, J. W. (2015). How does the general public understand science and technology issues?: a case on the nuclear power issue using topic modeling approach, Journal of Technology Innovation, 23, 152-175. https://doi.org/10.14386/SIME.2015.23.4.152
  7. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990). Indexing by latent semantic analysis, Journal of the American Society for Information Science, 41, 391-407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
  8. Grant, S., Cordy, J. R., and Skillicorn, D. B. (2013). Topicsmodels: an R package for fitting topic models. Journal of Statistical Software, 40, 1-30.
  9. Griffiths, T. L. and Steyvers, M. (2004). Finding scientific topics. In Proceedings of the National Academy of Sciences 101, supply 1, 5228-5235. https://doi.org/10.1073/pnas.0307752101
  10. Grun, B. and Hornik, K. (2011). Topicsmodels: an R package for fitting topic models. Journal of Statistical Software, 40, 1-30.
  11. Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 50-57.
  12. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer, New York.
  13. Jang, D. H. and Park, K. M. (2017). Policy issue discernment of the ocean and fishery field by the press: focus on the big data analysis on journal articles of central daily news papers, The Journal of Maritime Business, 37, 195-203.
  14. Jeong, D. M., Kim, J, S., Kim, G. N,, Heo, J. U., and On, B. W. (2013). A proposal of a keyword extraction system for detecting social issues, Journal of Intelligence and Information Systems, 19, 1-23.
  15. Joo, Y. H. and Kim, S. C. (2017). A study on the comprehensive school report policy for the university admission in Korea, The Journal of Educational Administration, 35, 141-168.
  16. Kang, B. I., Song, M., and Jho, W. S. (2013). A study on opinion mining of newspaper texts based on topic modeling, Journal of the Korean Library and Information Science Society, 47, 315-334. https://doi.org/10.16981/kliss.47.201609.315
  17. Khan, R. A. and Kanth, S. (2016). Text mining: knowledge discovery from unstructured data, Artificial Intelligent Systems and Machine Learning, 8, 71-77.
  18. Kim, J. E. and Baek, S. G. (2016). Analysis of issues on the college and university structural reform evaluation using text big data analytics, Asian Journal of Evaluation, 17, 409-436.
  19. Kim, S. H. and Yoon, J. W. (2016). Analysis system for SNS issues per country based on topic model, Journal of KIISE, 43, 1201-1209. https://doi.org/10.5626/JOK.2016.43.11.1201
  20. Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling, Springer, New York.
  21. Lee, S. J. (2018). An analysis of conflicting issues on the 'School Performance Records'-centered reform of college admissions system in South Korea, Journal of Learner-Centered Curriculum and Instruction, 18, 923-944.
  22. Park, C. H. (2017). Big data analysis on the demand for the Nuri Curriculum policies based on word clouds and social network analysis, Journal of Early Childhood Education, 37, 79-91. https://doi.org/10.18023/kjece.2017.37.5.004
  23. Sievert, C. and Shirley, K. (2014). LDAvis:a method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63-70.
  24. Yu, Y. L. and Baek, S. G. (2016). Issue analysis of the related mass media's news articles on the 2015 revised national curriculum using automated text analysis, The Journal of Curriculum and Evaluation, 19, 127-156. https://doi.org/10.29221/jce.2016.19.3.127
  25. Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., and Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling, BMC Bioinformatics, 16, S8.