Journal archive

The Korean Society for Journalism & Communication Studies - Vol. 63 , No. 5

[ Special Issue ]
Korean Journal of Journalism & Communication Studies - Vol. 63, No. 5, pp. 87-115
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 31 Oct 2019
Received 09 Aug 2019 Revised 08 Oct 2019 Accepted 10 Oct 2019
https://doi.org/10.20879/kjjcs.2019.63.5.003

빅데이터 시대의 데이터 저널리즘 교육에 관한 소고 : 무엇을, 어떻게 할 것인가?
윤호영**
**서울시립대 융합전공학부 빅데이터전공 객원교수 (hoyoungemail@gmail.com)

A Note on Data Journalism Education in the Era of Big Data : What to do, and How to do?
Ho Young Yoon**
**Visiting Faculty, Dept. of Cross-Disciplinary Studies, University of Seoul (hoyoungemail@gmail.com)

초록

본 연구는 빅데이터 시대에 데이터 저널리즘 교육을 활성화시키기 위한 방안을 고민하는 연구이다. 이를 위해 먼저 데이터 저널리즘의 등장 배경과 개념적 정의를 논하고, 데이터 저널리즘 교육과 관련된 쟁점을 정리하였다. 다음으로, 미국을 중심으로 데이터 저널리즘의 교육 현황을 점검하면서, 데이터 분석의 학습 과정에서 저널리즘적 시각이 추가되는 통계적 추론 능력이 중요하다는 점을 강조하였다. 또한, 데이터 저널리즘 교육의 활성화를 위해서는 대학 교육 뿐만 아니라 현업 환경을 고려한 학습 체계를 구성하는 것이 중요하다는 점을 언급하였다. 마지막으로, 데이터 저널리즘의 활성화를 위해 저널리즘 시각 육성을 위한 학부수업, 현업의 데이터 저널리즘 성과를 모은 학습 교재 데이터베이스, 그리고 대학원의 고급 기술 과정이 필요하다는 점을 제시하였다.

Abstract

The purpose of this paper is to invigorate discussion on data journalism education. The paper begins with an introduction of data journalism, which can be defined as a journalism practice pertaining to presenting data analysis for stories of interest to the public. The data analysis process for journalism practice, like many data analyses in other disciplines, includes collection, pre-processing, and visualization. After the introduction, the paper gives an overview of the debates on data journalism education: is it imperative to teach it; should it be taught at higher education institutions or elsewhere; should it be an independent course in journalism coursework, or can it be taught within disciplines that do not apply a journalism approach? While there is little consensus on how to teach data journalism and on the depth of data analysis required by journalism students, educators in journalism schools mostly agree that both statistical reasoning and coding skills are gaining ground as independent and essential subjects of the journalism curriculum. The issue here is the lack of teaching materials and human resources of a journalism approach, not the need or demand for data journalism. The paper also reviewed current syllabi for data journalism courses offered by several institutions so as to understand the sequence of data journalism coursework. With the exception of a small number of schools, it was observed that many data journalism courses tend to take the form of data analysis courses, making them indistinguishable from general social science courses. As data journalism education is in its initial stages, more systematic efforts need to be placed in the school education system. A few leading schools have already integrated field work and curriculum materials as capstone projects. Finally, the paper suggested tentative solutions for data journalism education that enriches through undergraduate study, industry practices, and graduate study. To summarize, first, it is suggested that a database system for data journalism education will greatly improve the effectiveness and efficiency of data journalism education. The database system will not only help teachers but also students in a way that provides standardized course materials and examples that everybody in the field can share as common ground. Second, it is also suggested that the data journalism education system needs to be aligned with industry practices, relevant institutions and associations, and academic environment. Previous literature has revealed that professionalism and the cultural heritage of journalism practices have led to diverse trajectories of data journalism education. In this respect, we are encouraged to set up our own system that harmonizes current efforts to grow data journalists in our society, both in and outside the academic setting.


Keywordsdata journalism, journalism education, big data, data analysis, journalism practice
키워드: 데이터 저널리즘, 저널리즘 교육, 빅데이터, 데이터 분석, 저널리즘 실천

Acknowledgments

본 논문에 대한 건설적인 비판과 격려를 통해 논고의 수준을 높혀주신 익명의 심사자 선생님분들에게 감사드리며, 또한 편집이사 정영주 선생님과 편집간사 김하늘 선생님의 기술적인 도움에 고마움을 전합니다.


References
1. Kim, Y. H. (2018, December 26). Data Journalism. New Hope for Korean Press. ZDNet Korea. Retrieved 7/20/19 from https://www.zdnet.co.kr/view/?no=20181226113636
2. Yang, S. H. (2019). Utopia of Heavy Industry family: Industiral City Geoje – Light and Shadow. Spring of May Publishing.
3. Chae, B. S. (2017, November 19). Data journalism of Korea, where are we at? Bloter.net. Retrieved 7/20/19 from https://www.bloter.net/archives/295225
4. Korea Press Foundation. (2018). Information: Data journalism team project business plan. Korea Press Foundation.
5. Hong, K. H. (2017, June 23). Reason why tiger fights against wolf ... Politics and competitive exclusion. Polinews. Retrieved 7/20/19 from http://polinews.co.kr/news/article.html?no=318357&sec_no=25
6. Anderson, C. W. (2015). Between the unique and the pattern: Historical tensions in our understanding of quantitative journalism. Digital journalism, 3(3), 349-363.
7. Anderson, C. W., Bell, E., & Shirky, C. (2012). Post industrial journalism: Adapting to the present, Tow Center for Digital Journalism.
8. Berret, C., & Phillips, C. (2016). Teaching data and computational journalism. Columbia School of Journalism.
9. Carlson, M. (2015). Metajournalistic discourse and the meanings of journalism: Definitional control, boundary work, and legitimation. Communication Theory, 26(4), 349-368.
10. Coddington, M. (2015) Clarifying Journalism’s Quantitative Turn, Digital Journalism, 3(3), 331-348.
11. Cushion, S., Lewis, J., & Callaghan, R. (2017). Data journalism, impartiality and statistical claims: Towards more independent scrutiny in news reporting. Journalism Practice, 11(10), 1198-1215.
12. Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital journalism, 3(3), 398-415.
13. Folkerts, J., Hamilton, J. M., & Lemann, N. (2004). Educating journalists: A new plea for the university tradition. Columbia Journalism School.
14. Gynnild, A. (2014). Journalism innovation leads to innovation journalism: The impact of computational exploration on changing mindsets. Journalism, 15(6), 713-730.
15. Heravi, B. R. (2019). 3Ws of Data Journalism Education: What, where and who?, Journalism Practice, 13(3), 349-366.
16. Hewett, J. (2016). Learning to teach data journalism: Innovation, influence and constraints. Journalism, 17(1), 119-137.
17. Howard, A. B. (2014). The art and science of data-driven journalism. New York: Two Center for Digital Jounralism. Retrieved 7/20/2019 from http://towcenter.org/wp-content/uploads/2014/05/Tow-Center-Data-Driven-Journalism.pdf.
18. Khazan, O. (2013, October 21). Should journalism schools require reporters to ‘learn code’? No. The Atlantic. Retrieved 7/20/2019 from https://www.theatlantic.com/education/archive/2013/10/should-journalism-schools-require-reporters-to-learn-code-no/280711
19. Lewis, S. C. (2012). The tension between professional control and open participation: Journalism and its boundaries. Information, Communication & Society, 15(6), 836-866.
20. Martin, J. D. (2017). A census of statistics requirements at US journalism programs and a model for a “statistics for journalism” course. Journalism & Mass Communication Educator, 72(4), 461-479.
21. McConway, K. (2016). Statistics and the media: A statistician’s view. Journalism, 17(1), 49-65.
22. Nguyen, A., & Lugo-Ocando, J. (2016). Introduction: The state of data and statistics in journalism and journalism education: Issues and debates. Journalism, 17(1), 3-17.
23. Reilly, S. (2017). The Need to Help Journalists with Data and Information Visualization. IEEE Computer Graphics and Applications, 37(2), 8–10.
24. Schudson, M., & Anderson, C. (2009). Objectivity, professionalism, and truth seeking in journalism. In The handbook of journalism studies (pp. 108-121). Routledge.
25. Spinner, J. (2014, September 23). The big conundrum: Should journalists learn code? American journalism review. Retrieved 7/20/2019 from https://ajr.org/2014/09/24/should-journalists-learn-code/
26. Splendore, S., Di Salvo, P., Eberwein, T., Groenhart, H., Kus, M., & Porlezza, C. (2016). Educational strategies in data journalism: A comparative study of six European countries. Journalism, 17(1), 138-152.
27. Waisbord, S. (2013). Reinventing professionalism: Journalism and news in global perspective. John Wiley & Sons.
28. Wenger, D. H., & Potter, D. (2018). Advancing the Story: Quality Journalism in a Digital World. (4th Eds.), CQ Press.
29. Yarnall, L., Johnson, J. T., Rinne, L., & Ranney, M. A. (2008). How post-secondary journalism educators teach advanced CAR data analysis skills in the digital age. Journalism & Mass Communication Educator, 63(2), 146-164.

부록
1. 김익현 (2018, 12, 26). 데이터 저널리즘, 한국 언론의 새로운 희망. <ZDNet Korea>. Retrieved 7/20/19 from https://www.zdnet.co.kr/view/?no=20181226113636
2. 양승훈 (2019). <중공업 가족의 유토피아: 산업 도시 거제, 빛과 그림자>. 서울: 오월의 봄.
3. 채반석 (2017, 11, 19). 한국의 데이터 저널리즘, 어디까지 왔나?. <Bloter.net> Retrieved 7/20/19 from https://www.bloter.net/archives/295225
4. 한국언론진흥재단 (2018). 데이터 저널리즘 팀 프로젝트 사업 계획서 및 공지 안내문. 언론인 연수팀. 한국언론진흥재단.
5. 홍경환 (2017, 6, 23). 호랑이와 늑대가 싸우는 이유. 정치와 경쟁적 배제. <폴리뉴스>. Retrieved 7/20/19 from http://polinews.co.kr/news/article.html?no=318357