Journal of The Korean Association of Information Education (정보교육학회논문지)

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

DOI QR Code

The Development and Application of the Big Data Analysis Course for the Improvement of the Data Literacy Competency of Teacher Training College Students

예비교사의 데이터 리터러시 역량 증진을 위한 빅데이터 분석 교양강좌의 개발 및 적용

  • Received : 2022.03.22
  • Accepted : 2022.04.06
  • Published : 2022.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, basic literacy education related to digital literacy and data literacy has been emphasized for students who will live in a rapidly developing future digital society. Accordingly, demand for education to improve big data and data literacy is also increasing in general universities and universities of education as basic knowledge. Therefore, this study designed and applied big data analysis courses for pre-service teachers and analyzed the impact on data literacy. As a result of analyzing the interest and understanding of the input program, it was confirmed that it was an appropriate form for the level of pre-service teachers, and there was a significant improvement in competencies in all areas of 'knowledge', 'skills', and 'values and attitudes' of data literacy. It is hoped that the results of this study will contribute to enhancing the data literacy of students and pre-served teachers by helping with systematic data literacy educational research.

최근, 급격히 발전하는 미래 디지털 사회를 살아갈 학생들의 디지털 리터러시와 데이터 리터러시 관련 기초소양 교육이 강조되고 있다. 이에 일반 대학과 교육 대학에서도 기초소양으로서 빅데이터 및 데이터 리터러시 향상을 위한 교육의 수요가 많아지고 있다. 이에 본 연구는 예비교사를 위한 빅데이터 분석 교양강좌를 설계 및 적용하고 데이터 리터러시에 미치는 영향을 분석하였다. 투입 프로그램에 대한 흥미도와 이해도 분석 결과, 예비교사의 수준에 적절한 형태임을 확인했으며, 데이터 리터러시의 '지식', '기능', '가치와 태도'의 모든 영역에서 유의미한 역량의 향상이 있는 것을 확인하였다. 본 연구의 결과가 체계적인 데이터 리터러시 관련 교육 연구에 도움을 주어 학생과 예비교사들의 데이터 리터러시를 증진하는데 이바지할 수 있기를 기대한다.

Keywords

References

  1. Bae, H. (2019). Educational Implications of Data Literacy in Social Studies. Theory and Research in Citizenship Education, 51(1), 95-120. https://doi.org/10.35557/TRCE.51.1.201903.004
  2. Bhargava, R., & D'Ignazio, C. (2015, June). Designing tools and activities for data literacy learners. In Workshop on Data Literacy, Webscience.
  3. Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. portal: Libraries and the Academy. 11(2), 629-657. https://doi.org/10.1353/pla.2011.0022
  4. D'Ignazio, C., & Bhargava, R. (2015, September). Approaches to building big data literacy. In Proceedings of the Bloomberg data for good exchange conference.
  5. Han, S. W. (2018). A study about the concept of data literacy based on digital humanities. Journal of the Korean Society for information Management, 35(4), 223-236. https://doi.org/10.3743/KOSIM.2018.35.4.223
  6. Hattwig, D., Bussert, K., Medaille, A., Burgess, J. (2013). Visual literacy standards in higher education: New opportunities for libraries and student learning. Libraries and Academy, 13(1), 61-89. https://doi.org/10.1353/pla.2013.0008
  7. Hong, J., Kim, Y. (2020) Development of AI Data Science Education Program to Foster Data Literacy of Elementary School Students. Journal of The Korean Association of Information Education, 24(5), 463-471. https://doi.org/10.14352/jkaie.2020.24.5.463
  8. Hur, K. (2020). A Study on Elementary Education Examples for Data Science using Entry. Journal of The Korean Association of Information Education, 24(5), 473-481. https://doi.org/10.14352/jkaie.2020.24.5.473
  9. Hur, K.. (2021). A Case Study of Basic Data Science Education using Public Big Data Collection and Spreadsheets for Teacher Education. Journal of The Korean Association of Information Education, 25(3), 459-469. https://doi.org/10.14352/jkaie.2021.25.3.459
  10. Jun, S., Jeon, Y., Jeong, I. (2021). Design of Liberal Arts Subjects for Artificial Intelligence Education for Pre-Teachers in Elementary and Secondary Schools. Journal of The Korean Association of Information Education, 25(5), 859-869. https://doi.org/10.14352/jkaie.2021.25.5.859
  11. Kim, H. (2020). Analysis of Data Literacy in the Core Curriculum to Improve Students' 4C Skills: Communication, Collaboration, Critical Thinking, and Creativity. Korean Journal of General Education, 14(6), 147-159. https://doi.org/10.46392/kjge.2020.14.6.147
  12. Kim, J., Sohn, E. (2021) Development of Data Science Course and Analysis of Computational Thinking Effect for non-Major Students. Journal of The Korean Association of Information Education 24(3), 23-31.
  13. Kim, S., Kim, S., Kim, H. (2019). Analysis of International Educational Trends and Learning Tools for Artificial Intelligence Education. The Journal of Korean Association of Computer Education, 23(2), 25-28. https://doi.org/10.32431/KACE.2020.23.6.003
  14. Kim, S., Kim, T. (2021). A Study of the Definition and Components of Data Literacy for K-12 AI Education. Journal of The Korean Association of Information Education, 25(5), 691-704. https://doi.org/10.14352/jkaie.2021.25.5.691
  15. Kim, Y. (2018). Data Science Education Program to Improve Computational Thinking and Creativity. Doctor thesis. Jeju. Jeju National Unversity.
  16. KOFAC. (2019). 2019 Global SW Education Conference Issue Paper: Age of AI, SW Education is reconsidered. Seoul. KOFAC.
  17. Koo, D., Kim, D. (2020). Data science education program based on problem solving learning. The Journal of Korea Elementary Education, 31, 203-215.
  18. Korean Government. (2020). A Plan to Spread AI SW Education for People Across the Country. https://www.4th-ir.go.kr/article/download/710.
  19. Korean Government. (2020) National Strategy for Artificial Intelligence. https://www.korea.kr/common/download.do?fileId=190114031&tblKey=GMN.
  20. Lee, E. (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
  21. Lee, H., Kim, S., Kim, H., Lee, W., Lim, S., Park, S. (2019). A study on measuring the digital literacy level of elementary and middle school students at the national level in 2018. Daegu, KERIS.
  22. Lee, Y., Jang, B., Lim, J., Park, J., Seo, M., Lee, D., Kim, H. (2021) Basic Artificial Intelligence, Seoul, Cmass.
  23. Mandinach, E. B., Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37. https://doi.org/10.3102/0013189X12459803
  24. Mandinach, E. B., Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366-376. https://doi.org/10.1016/j.tate.2016.07.011
  25. Ministry of Education. (2021) Key Points of the 2022 Revised Curriculum Introductions, https://www.moe.go.kr/boardCnts/view.do?boardID=294&boardSeq=89671&lev=0&searchType=null&statusYN=W&page=1&s=moe&m=020402&opType=N.
  26. No, K. (2019). The proper methods of statistical analysis for dissertation SPSS & AMOS. Seoul. Hanbit.
  27. OECD. (2018). The future of education and skills: Education 2030. OECD Education Working Papers.
  28. Oh, K., Kim, H. (2020). An Analysis of the Influence big data analysis-based AI education on Affective Attitude towards Artificial Intelligence. Journal of The Korean Association of Information Education, 24(5), 463-471. https://doi.org/10.14352/jkaie.2020.24.5.463
  29. Park, Y., Lee, S. (2020). Study on the Direction of Universal Big Data and Big Data Education-Based on the Survey of Big Data Experts. Journal of The Korean Association of Information Education, 24(2), 201-214. https://doi.org/10.14352/jkaie.2020.24.2.201
  30. Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure you know what's being reported? Critique and recommendations. Research in nursing & health, 29(5), 489-497. https://doi.org/10.1002/nur.20147
  31. Qilk. (2018). Data literacy workforce assesment. https://thedataliteracyproject.org/resources.
  32. Shields, M. (2005). Information literacy, statistical literacy, data literacy. IASSIST quarterly, 28(2-3), 6-6. https://doi.org/10.29173/iq790
  33. Song, Y. (2021). The Data-Driven Debate (DDD) Instructional Model for Improving Data Literacy. Master Thesis. Seoul. Department of Education The Graduate School Seoul National University
  34. Swan, K., Vahey, P. J., Rafanan, K., Stanford, T. (2009). Challenges to cross-disciplinary curricula: Data literacy and divergent disciplinary perspectives. In Annual Conference of the American Educational Research Association. San Diego, CA.