Journal of Digital Convergence (디지털융복합연구)

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

DOI QR Code

An analysis of public perception on Artificial Intelligence(AI) education using Big Data: Based on News articles and Twitter

빅데이터 분석을 통해 본 AI교육에 대한 사회적 인식: 뉴스기사와 트위터를 중심으로

  • Lee, Sang-Soog (Department of Journalism and Mass Communication, Hanyang University) ;
  • Yoo, Inhyeok (Department of Industrial Engineering, Inha University) ;
  • Kim, Jinhee (Department of Education, Seoul National University)
  • 이상숙 (한양대학교 신문방송학과) ;
  • 유인혁 (인하대학교 산업공학과) ;
  • 김진희 (서울대학교 교육학과)
  • Received : 2020.03.02
  • Accepted : 2020.06.20
  • Published : 2020.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to understand the public needs for AI education actively promoted and supported by the current government. In doing so, 11 metropolitan news articles and Twitter posts regarding AI education that have been posted from January 1, 2018 to December 31, 2019 were collected. Then, word frequency analysis using TF(Term Frequency) method and LDA(Latent Dirichlet Allocation) method of topic modeling analysis were conducted. The topics of the news articles turn out to be a macroscopic policy support such as 'training female manpower in the AI field' and 'curriculum reform of university and K-12', whereas the topics of twitter delineate more detailed social perception on future society, such as future competencies and pedagogical methods, including 'coexistence with intelligent robots', 'coding education', and 'humane education competence development'. The findings are expected to be used to suggest the implications for the composition and management of AI curriculum as well as the basic framework of human resources development in the future industry.

본 연구는 현 정부가 적극적으로 추진·지원하는 AI교육에 관한 대중의 요구를 파악하는 데 그 목적이 있다. 이를 위해 2018년 1월 1일부터 2019년 12월 31까지 AI교육에 대한 11개의 중앙지 뉴스기사와 트위터 게시글을 수집하여 단어 빈도분석과 토픽모델링분석을 실시하였다. 단어빈도 분석은 TF(Term Frequency)기법을, 토픽모델링분석은 잠재 디리클레 할당(Latent Dirichlet Allocation)기법을 사용하였다. 분석결과, 뉴스기사는 AI분야의 여성인재 육성, 대학교육과정의 변화, K-12의 소프트웨어 교육 및 교육과정 변화 등 거시적인 정책 지원에 대한 토픽이, 트위터에서는 지능형로봇과의 공존시대와 같은 보다 구체적인 미래시대에 대한 사회적 인식과 코딩교육, 인간의 고유역량개발 등과 같은 미래역량과 교육방법론 등에 대한 토픽이 도출되었다. 이러한 연구결과는 AI교육과정 구성 및 운영 방안과 미래 산업 인재 양성 정책 개발을 위한 시사점을 제공해 줄 수 있을 것으로 기대한다.

Keywords

References

  1. Korea Foundation for the Advancement of Science and Creativity. (2019). 2019 AI Convergence Education Policy Data Collection. Seoul: KOFAC.
  2. National Science and Technology Council. (2016). The National Artificial Intelligence Research and Development Strategic Plan. Washington D.C. : NSTC
  3. Ministry of Science and ICT. (2019. 12. 17). A leader in AI beyond a leader in IT. Sejong: MSIC.
  4. M. Ryu & S. Han. (2018). The Educational Perception on Artificial Intelligence by Elementary School Teachers. Journal of The Korean Association of Information Education, 22(3), 317-324. DOI: 10.14352/jkaie.2018.22.3.317
  5. S. Shin, M. Ha & J. K. Lee. (2017). High School Students' Perception of Artificial Intelligence: Focusing on Conceptual Understanding, Emotion and Risk Perception. Korean Association For Learner-Centered Curriculum And Instruction, 17(21): 289-312. DOI:10.22251/jlcci.2017.17.21.289
  6. K. Kim & Y. Park. (2017). A Development and Application of the Teaching and Learning Model of Artificial Intelligence Education for Elementary Students. Journal of The Korean Association of Information Education, 21(1), 137-147. DOI: 10.14352/jkaie.2017.21.1.137
  7. J. Kim & N. Park. (2019). Development of a board game-based gamification learning model for training on the principles of artificial intelligence learning in elementary courses. Journal of The Korean Association of Information Education, 23(3), 229-235. DOI: 10.14352/jkaie.2019.23.3.229
  8. E. Lee. (2020). A Comparative Analysis of Contents Related to Artificial Intelligence in National and International K-12 Curriculum. The Journal of Korean Association of Computer Education, 23(1), 37-44 https://doi.org/10.32431/KACE.2020.23.1.003
  9. J. M. Kim et al. (2020). Proposing the informatics standard curriculum scheduled to be revised in 2022. The Journal of Korean Association of Computer Education. 23(1), 1-28. https://doi.org/10.32431/KACE.2020.23.1.001
  10. H. S. Bae. (2008). Theory-driven educational program evaluation. Seoul: Wonmisa.
  11. Head, B. (2010). Evidence-based policy: principles and requirements. In Strengthening Evidence Based Policy in the Australian Federation, Volume 1: Proceedings, Roundtable Proceedings, Productivity Commission, Canberra, 13-26.
  12. S. Baek. (2009, Oct.). Educational Evaluation of Knowledge-Convergence Era in 21st Century. The Fall Conference of Korean Educational Research Association. (pp. 69-74). Seoul.
  13. C. Adrian et al. (2016). Big data analytics implementation for value discovery: a systematic literature review. Journal of Theoretical and Applied Information Technology, 93(2), 385-393.
  14. K. Kwon, J. Park & C. Ku. (2014). Agenda Development Using Education Big Data: Focusing on Social Network Analysis. Seoul:KERIS.
  15. H. Choi & J. Ahn. (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(4), 152-175. https://doi.org/10.14386/SIME.2015.23.4.152
  16. W. Kim & C. Ku. (2015). A Study on the Utilization of Big Data in Educational Information Policy: Focusing on an application of Social Media Data. Seoul: KERIS.
  17. KoNLPy(Korean NLP in Python): http://konlpy.org.
  18. G. Salton & C. Buckley. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523. DOI: 10.1016/0306-4573(88)90021-0
  19. Griffiths, T. L., &Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  20. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  21. C. Jacobi, W. Van Atteveldt, & K. Welbers. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism, 4(1), 89-106. DOI: 10.1080/21670811.2015.1093271
  22. M. Roder, A. Both & A. Hinneburg. (2015, February). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining (pp. 399-408). Shanghai: ACM.
  23. DoE. (2014). National Curriculum in England: Framework For Key Stages 1 to 4. London: DoE.
  24. CSTA. (2017). CSTA K-12 Computer Science Standards, Revised 2017. New York: CSTA.
  25. M. Kim & W. G. Lee. (2014). A study on India's CMC(Computer Masti Curriculum) based on Bruner's educational theories. The Journal of Korean Association of Computer Education, 17(6), 59-69. https://doi.org/10.32431/KACE.2014.17.6.006
  26. CBSE(2019). Department of Skill Education, Artificial Intelligence, Curriculum for class VIII(Inspire module). New Delhi: CBSE.
  27. ACARA(2008). Information and Communication Technology Capability learning continuum. Sydney: ACARA.
  28. MoE. (2015). Technology.Home Economics/Informatics Curriculum. 2015-74. Sejong: MoE.
  29. J. Yoo. (2019). A study on AI Education in Graduate School through IPA. Journal of The Korean Association of Information Education, 23(6), 675-687 https://doi.org/10.14352/jkaie.2019.23.6.675
  30. OECD. (2019). Artificial Intelligence in Society. Paris: OECD.
  31. U. C. Jeon. (2017). A Study on the Current Status of Artificial Intelligence Education in Each Country. Review of Korean society of internet information, 18(1), 13-18.
  32. J. Yoo. (2019). A study on AI Education in Graduate School through IPA. Journal of The Korean Association of Information Education, 23(6), 675-687. DOI: 10.14352/jkaie.2019.23.6.675
  33. Y. E. Kim & H. C. Kim. (2019). A Study on Middle School Students' Perception on Intelligent Robots as companions. The Journal of Korean Association of Computer Education, 22(4), 35-45. https://doi.org/10.32431/KACE.2019.22.4.004
  34. S. Y. Song. (2019). A Study of the Task of the Ethics Education of Human Nature in the Relationship between Humans and AI Robotics. Journal of Ethics, 1(126), 91-115. https://doi.org/10.15801/je.1.126.201909.91