DOI QR코드

DOI QR Code

Statistical Literacy of Fifth and Sixth Graders for the Data Presentation Task Based on the Speculative Data Generation Process

가상적 자료 생성 과정에 기반을 둔 자료 표현 과제에 대한 초등학교 5, 6학년 학생들의 통계적 소양

  • Moon, Eun-Hye (Graduate School of Korea National University of Education) ;
  • Lee, Kwangho (Korea National University of Education)
  • Received : 2018.07.14
  • Accepted : 2018.10.04
  • Published : 2018.10.31

Abstract

The purpose of this study is to analyze the level of statistical literacy among fifth and sixth graders in the data presentation task based on the speculative data generation process. For the research, the data presentation tasks based on the speculative data generation process was designed and statistical literacy standards for evaluating the student's level was presented based on prior studies. It is meaningful that the stepwise presentation of the students' statistical literacy and analysis of their developmental patterns can help them to find their current position and reach a higher level of performance. In this study, the standard of statistical literacy level was clarified based on the previous research, and a new perspective was presented about the data presentation instruction in the statistical education by analyzing the students' responses by each level.

본 연구는 가상적 자료 생성 과정에 기반을 둔 자료 표현 과제에서 나타나는 초등학교 5, 6학년 학생들의 통계적 소양 수준을 분석하고자 하였다. 이를 위하여 가상적 자료 생성 과정을 포함하는 자료 표현 과제를 설계하였으며, 선행연구를 바탕으로 자료 표현 영역에 대한 통계적 소양 분석 기준을 마련하였다. 학생들의 통계적 소양을 단계별로 제시하여 발달 양상을 분석하는 것은 학생들의 현재 위치를 파악하여 더 높은 수준의 수행 양상에 도달하도록 도울 수 있다는 점에서 의의가 있다. 이에 본 연구에서는 선행연구를 바탕으로 통계적 소양 수준 평가의 기준안을 명확히 하고, 각 수준 별 학생 반응을 자세히 분석하여 학교 통계 교육에서 자료 표현 지도에 대해 새로운 관점을 제시하였다.

Keywords

References

  1. Kang, H. Y. (2012). Study of the educational meaning of statistical literacy. The Korean Journal for History of Mathematics, 25(4), 121-137.
  2. Kim, J. S. (2018). Design and application of scatter plot and correlation class based on SRLE. Major in Mathematics Education, Graduate School of Education, Korea National University of Education.
  3. Ministry of Education. (2015). Mathematics Curriculum. Bulletin of MOE No. 2015-74 [Seperate Volume #8]
  4. No, A. R. & Yoo, Y. J. (2013). Korean high school students' understanding of the concept of correlation. Journal of Educational Research in Mathematics, 23(4), 467-490.
  5. Woo, J. H. (2000). An exploration of the reform direction of teaching statistics. School Mathematics, 2(1), 1-27.
  6. Chong, Y. O. (2005). Reflections on the directions of teaching function in the primary schools. The Journal of Education, 25(2), 147-168.
  7. Tak, B. J. (2017). Pre-Service mathematics teachers' statistical knowledge for teaching to develop statistical literacy: Focusing on the teaching of sample. Major in Mathematics Education, The Graduate School, Seoul National University.
  8. Hwang, H. J., Na, G. S., Choi, S. H., Park, G. M., Lim, J. H. & Seo, D. Y. (2016). New theory of mathematical education. Seolul: Moonum-sa.
  9. Biggs, J. B. & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy. New York: Academic Press.
  10. Fleiss, J. L. (1981). Statistical methods for rates and proportion. New York: Wiley.
  11. Gal, I. (2002). Adults' statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1-25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
  12. Garfield, J. & Ben-Zvi, D. (2008). Developing students' statistical reasoning: Connecting research and teaching practice. New York: Springer.
  13. Garfield, J., delMas, R., & Chance, B. (2003). Web-based assessment resource tools for improving statistical thinking. Paper presented at the annual meeting of the American Educational Research Association, Chicago.
  14. Jones, G. A., Langrall, C. W., Mooney, E. S. & Thornton, C. A. (2004). Models of development in statistical reasoning. In D. Ben-Zvi & Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking. Dordrecht, The Netherlands: Kluwer Academic Publishers.
  15. McKenzie, Danny L. & Padilla, Michael J. (1981). Patterns of reasoning: Correlational thinking. Paper presented at the 54th Annual Meeting of the National Association for Research in Science Teaching. Ellenville, NY.
  16. Moritz, J. (2004). Reasoning about covariation. In D. Ben-Zvi & Garfield(Eds.), The challenge of developing Statistical Literature, Reasoning, and Thinking. Dordrecht, The Netherlands: Kluwer Academic Publishers.
  17. Sharma, S. (2017). Definitions and models of statistical literacy: a literature review, Open Review of Educational Research, 4(1), 118-133. https://doi.org/10.1080/23265507.2017.1354313
  18. Sharma, S., Doyle, P., Shandil, V., & Talakia'atu, S. (2011). Developing statistical literacy with year 9 students. Research Information for Educational Research, 1, 43-60.
  19. Watson, J. M. (1997). Assessing statistical thinking using the media. In I. Gal & J. B. Garfield (Eds.). The Assessment Chanllenge in Statistics Education (pp.107-121). Amsterdam: IOS Press.
  20. Watson, J. M. (2000). Statistics in context. Mathematics Teacher, 93, 54-58.
  21. Watson, J. M. (2006). Statistical literacy at school: Growth and goals. Mahwah, NJ: Lawrence Erlbaum Associates.
  22. Watson, J. M. & Callingham, R. (2003). Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal, 2(2), 3-46.