Journal of The Korean Association of Information Education (정보교육학회논문지)

Korea Association of Information Education (한국정보교육학회)
권호 표지
DOI QR코드

DOI QR Code

Study on the Direction of Universal Big Data and Big Data Education-Based on the Survey of Big Data Experts

보편적 빅데이터와 빅데이터 교육의 방향성 연구 - 빅데이터 전문가의 인식 조사를 기반으로

  • Received : 2020.04.13
  • Accepted : 2020.04.27
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

최근 데이터 관련 법안이 개정되면서 빅데이터의 활용 분야는 점차 확장되고 있으며, 빅데이터 교육에 대한 관심이 증가하고 있다. 그러나 빅데이터를 활용하기 위해서는 높은 수준의 지식과 스킬이 필요하고, 이를 모두 교육하기에는 오랜 시간과 많은 비용이 소요된다. 이에 본 연구를 통해 산업 현장에서 사용되는 광범위한 영역의 빅데이터를 보편적 빅데이터(Universal Big Data)로 정의하고, 대학교 수준에서 보편적 빅데이터를 교육하기 위해서 중점적으로 교육해야 할 지식 영역을 산출하고자 한다. 이를 위해 빅데이터 관련 산업에 종사하는 전문인력을 구분하기 위한 기준을 마련하고, 설문 조사를 통해 빅데이터에 대한 인식을 조사했다. 조사 결과에 의하면 전문가들은 컴퓨터과학에서 의미하는 빅데이터보다 광범위한 범위의 데이터를 빅데이터로 인식하고 있었으며, 빅데이터의 가공 과정에 반드시 빅데이터 처리 프레임워크 또는 고성능 컴퓨터가 필요한 것은 아니라고 인식하고 있었다. 이는 빅데이터를 교육하기 위해서는 컴퓨터과학(공학)적 지식과 스킬보다는 빅데이터의 분석 방법과 응용 방법을 중심으로 교육해야 한다는 것을 의미한다. 분석 결과를 바탕으로 본 논문에서는 보편적 빅데이터 교육을 위한 새로운 패러다임을 제안하고자 한다.

Keywords

References

  1. Forbes(2019). The 7 Biggest Technology Trends That Will Transform Telecoms In 2020. Retrieved from https://www.forbes.com/
  2. Forbes(2020). Why 2020 Will Be The Year Enterprise Applications Go Cloud Native. Retrieved from https://www.forbes.com/
  3. Gartner(2020). Gartner Top 10 Strategic Technology Trends for 2020. Retrieved from https://www.gartner.com/
  4. Pena-Lopez, Ismael(2017). OECD digital economy outlook 2017.
  5. Schwab, Klaus(2017). The fourth industrial revolution. Currency.
  6. Chulho Jang, & Minseop Kim(2019). A Study on the Contribution of Data Industry to the Korean Economy: An Input-Output Analysis, Journal of Economics Studies, 37(4), 1-22.
  7. Rajaraman, Anand, & Jeffrey David Ullman(2011). Mining of massive datasets. Cambridge University Press.
  8. Joon-Soo Kim(2018). Big Data Monthly, National Information Society Agency, 42, 2018.
  9. Kyoo-Sung Noh, Seong Taek Park, & Kyung-Hye Park(2015). Convergence Study on Big Data Competency Reference Model, Journal of Digital Convergence, 13(3), 55-64. https://doi.org/10.14400/JDC.2015.13.3.55
  10. Hyun Min Song(2015). A Study of Development for job Competency in the bigdata using DACUM method, Korea University master's thesis.
  11. Korea Data Agency(2018). 2018 Data Industry Survey, Korea Data Agency.
  12. Do-Sik Choi(2017). Problems of Big Data Analysis Education and Their Solutions, Journal of the Korea Convergence Society, 8(12), 265-274. https://doi.org/10.15207/JKCS.2017.8.12.265
  13. SeungHwa Jung, & Jaewoo Do(2019). Case Study on Operation of Big Data Educational Program, Journal of Education & Culture (JOEC), 25(5), 621-640. https://doi.org/10.24159/joec.2019.25.1.621
  14. Hwa-Min Jeong, & Youngsook Song(2019). A Study on Factors Affecting the Effectiveness of Big Data Training - Based on Perception of Participants in Consortium for HRD Ability Magnified Program -, The Journal of Learner-Centered Curriculum and Instruction (JLCCI), 18(4), 29-45.
  15. Eun-Han Jeong, & Kyng-Ihl Kim(2018). A Study on Improvement of Accounting Curriculum in Big Data Age, Journal of Convergence for Information Technology (JCIT), 8(5), 142-152.
  16. Hunkoog Jho(2017). The changes of higher education and the tasks of general education according to the fourth industrial revolution, Korean Journal of General Education, 11(2), 52-89.
  17. Gandomi Amir, & Murtaza Haider(2015). Beyond the hype: Big data concepts, methods, and analytics, International journal of information management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
  18. Apache Hadoop. Retrieved from https://hadoop.apache.org/
  19. Traffic information of Seoul. Retrieved from http://news.seoul.go.kr/
  20. Jong Soo Park, & Keumsook Lee(2015). Time-distance accessibility computation of Seoul bus system based on the T-card transaction big databases, Journal of the Economic Geographical Society of Korea, 18(4), 539-555. https://doi.org/10.23841/egsk.2015.18.4.539
  21. Hee Jeoung Moon(2019). A Study on Visualizing Method and Expression for Big Data, Smart Media Journal, 8(1), 59-66.
  22. Soo-Hyung Park(2016). Report on research results to develop data experts (Kdata 16-013), Korea Data Agency.
  23. Ji Hei Kang(2016). Study on the current status of Data Science curriculum in Library and Information Science and its direction, Journal of Korean Library and Information Science Society 47(3), 343-363. https://doi.org/10.16981/kliss.47.201609.343
  24. Wooje Cho(2018). Creating Value for Education through Big Data Analysis Education Programs, The Korean Journal of Bigdata 3(2), 123-130. https://doi.org/10.36498/kbigdt.2018.3.2.123
  25. Youn-Soo Park, & Minjeong Lee(2020). A Study on Improving Computational Thinking Education of University by Reflecting Learner's Perception and Instructor's Opinion, Korean Journal of General Education, 14(1), 167-191.
  26. Ji-Wun Jung(2018). Research and development of national technical qualification items for big data analysis articles, Human Resources Development Service of Korea.
  27. Korea Policy Briefing. Retrieved from http://www.korea.kr/
  28. Armbrust Michael, Fox Armando, Katz Randy, & Konwinski Andy(2010). A view of cloud computing, Communications of the ACM, 53(4), 50-58. https://doi.org/10.1145/1721654.1721672
  29. Botta Alessio, Donato Walterde, Persico Valerio, & Pescape Antonio(2016). Integration of cloud computing and internet of things: a survey, Future generation computer systems, 56, 684-700. https://doi.org/10.1016/j.future.2015.09.021
  30. Announcement of average wages for SW engineers in 2018. Retrieved from https://www.sw.or.kr/

Cited by

  1. 엔트리를 활용한 초등 데이터 과학 교육 사례 연구 vol.24, pp.5, 2020, https://doi.org/10.14352/jkaie.2020.24.5.473
  2. 비전공자 대상 기초 데이터과학 실습 커리큘럼 vol.12, pp.2, 2020, https://doi.org/10.14702/jpee.2020.265
  3. Research on SW Education in the COVID-19 Era:Focusing on Computational Thinking and ICT vol.22, pp.4, 2021, https://doi.org/10.9728/dcs.2021.22.4.629