Research in Mathematical Education (한국수학교육학회지시리즈D:수학교육연구)

Korean Society of Mathematical Education (한국수학교육학회)
권호 표지
DOI QR코드

DOI QR Code

Fostering Students' Statistical Thinking through Data Modelling

  • Ken W. Li (Retired Scholar)
  • Received : 2023.02.10
  • Accepted : 2023.04.21
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Statistical thinking has a broad definition but focuses on the context of regression modelling in the present study. To foster students' statistical thinking within the context, teaching should no longer be seen as transfer of knowledge from teacher to students but as a process of engaging with learning activities in which they develop ownership of knowledge. This study aims at collaborative learning contexts; students were divided into small groups in order to increase opportunities for peer collaboration. Each group of students was asked to do a regression project after class. Through doing the project, they learnt to organize and connect previously accrued piecemeal statistical knowledge in an integrated manner. They could also clarify misunderstandings and solve problems through verbal exchanges among themselves. They gave a clear and lucid account of the model they had built and showed collaborative interactions when presenting their projects in front of class. A survey was conducted to solicit their feedback on how peer collaboration would facilitate learning of statistics. Almost all students found their interaction with their peers productive; they focused on the development of statistical thinking with concerted effort.

Keywords

Acknowledgement

The author would like to express his sincere thanks to Dr. Barbara Jack for proofreading this article.

References

  1. Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre K-12 guidelines for assessment and instruction in statistics education II-A framework for statistics and data science education. American Statistical Association and National Council of Teachers of Mathematics. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf.
  2. Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy. Academic Press. https://doi.org/10.1016/C2013-0-10375-3
  3. Bishop, G., & Talbot, M. (2001). Statistical thinking for novice researchers in biological science. In C. Batanero (Ed.), Proceedings of the IASE Round Table Conference on Training Researchers in the Use of Statistics (pp. 215-226). International Association for Statistical Education and International Statistical Institute.
  4. Bude, L. (2006) Assessing students' understanding of statistics. In A. Rossman & B. Chance (Eds.), Proceedings of the 7th International Conference on Teaching Statistics. International Association for Statistical Education. https://www.stat.auckland.ac.nz/~iase/publications/17/6G3_BUDE.pdf
  5. Chance, B. (2002). Components of statistical thinking and implications for instruction and assessment. Journal of Statistics Education, 10(3), 1-14. https://doi.org/10.1080/10691898.2002.11910677
  6. daSilva, M. P. M., & Pinto, S. (2014). Teaching statistics through learning projects. Statistics Education Research Journal, 13(2), 177-186. https://doi.org/10.52041/serj.v13i2.289
  7. Dransfield, S. B., Fisher, N. I., & Vogel, N. J. (1999). Using statistics and statistical thinking to improve organization performance. International Statistical Review, 67(2), 99-121. https://doi.org/10.2307/1403389
  8. Hoerl, R. W., & Snee, R. (2010). Statistical thinking and methods in quality improvement: A look to the future. Quality Engineering, 22(3), 119-129. https://doi.org/10.1080/08982112.2010.481485
  9. Frischemeier, D., Kazak, S., Leavy, A., Meletiou-Mavrotheris, M., & Paparistodemou, E. (2022). International perspectives of early statistical thinking: Comparison of primary school curricula in different countries. In S. A. Peters, L. Zapata-Cardona, F. Bonafini & A. Fan (Eds.), Proceedings of the 11th International Conference on Teaching Statistics. International Association for Statistical Education. https://iase-web.org/icots/11/proceedings/pdfs/ICOTS11_123_FRISCHEM.pdf
  10. Gillies, R. M. (2012). Promoting reasoning, problem-solving and argumentation during small group discussions. In R.M. Gillies (Ed.), Pedagogy: New developments in the learning sciences (pp. 131-150). Nova Science Publishers.
  11. Li, K. W. (2015). Analysis of discourse practices in statistical computing laboratory. In S. Gerland (Ed.), Proceedings of the Second 21st Century Academic Forum Conference, (pp. 249-263). Harvard University, USA. www.21caf.org/uploads/1/3/5/2/13527682/20_hrd-531-li_ed2_fmt_logo.pdf
  12. Li, K. W. (2021). Developing regression heuristics through collective argumentation in computing laboratory. In R. Helenius (Ed.), Proceedings of the International Association for Statistical Association 2021 Satellite Conference. International Association for Statistical Education. https://iase-web.org/documents/papers/sat2021/IASE2021%20Satellite%20124_LI.pdf
  13. Li, K., & Goos, M. (2011). Reinforcing students' correlation comprehension. The International Journal of Learning, 17(11), 261-274.
  14. Li, K. W., & Goos, M. (2018). An analysis of patterns of classroom talk within an IT environment. In M. A. Sorto, A. White & L. Guyot (Eds.), Proceedings of the 10th International Conference on Teaching Statistics. International Association for Statistical Education. http://iase-web.org/icots/10/proceedings/pdfs/ICOTS10_3G1.pdf
  15. Mallows, C. (1998). The zeroth problem. The American Statistician, 52(1), 1-9. https://doi.org/10.2307/2685557
  16. Mercer, N. (2004). Sociocultural discourse analysis: Analyzing classroom talk as a social mode thinking. Journal of Applied Linguistics and Professional Practice, 1(2), 137-168. https://doi.org/10.1558/japl.v1.i2.137
  17. Mogro-Wilson, C., Reeves, M. G., & Charter, M. L. (2015). Applying sociocultural theory to teaching doctoral social work students. Journal of Teaching in Social Work, 35(1-2), 151-165. https://doi.org/10.1080/08841233.2014.976354
  18. Moore, D. S. (1997). New pedagogy and new content: The case of statistics (with discussion). International Statistical Review, 65(2), 123-137. https://doi.org/10.2307/1403333
  19. Negueruela-Azrola, E., & Garcia, P. N. (2016). Sociocultural theory and the language classroom. In G. Hall (Ed.), The routledge handbook of english language teaching (pp. 295-309). Routledge.
  20. Nussbaum, M., Alvarez, C., McFarlane, A., Gomez, F., Claro, S., & Radovic, D. (2009). Technology as small group face-to-face collaborative scaffolding. Computers and Education, 52(1), 147-153. https://doi.org/10.1016/j.compedu.2008.07.005
  21. Pfannkuch, M. (2000). A model for the promotion of statistical thinking? In J. Bana & A. Chapman (Eds.), Proceedings of the Twenty-third Annual Conference of the Mathematics Education Research Group of Australasia (pp. 503-510). Mathematics Education Research Group of Australasia.
  22. Putt, I., Perry, B., Jones, G., Thornton, C., Langrall, C., & Mooney, E. (2000). Primary school students' statistical thinking: A comparison of two Australian States. In J. Bana & A. Chapman (Eds.), Proceedings of the Twenty-third Annual Conference of the Mathematics Education Research Group of Australasia (pp. 519-526). Mathematics Education Research Group of Australasia.
  23. Schum, D. A. (2001). Evidential foundations of probabilistic reasoning. Northwestern University Press.
  24. Snee, R. D. (1990). Statistical thinking and its contribution to total quality. The American Statistician, 44(2), 116-121. https://doi.org/10.2307/2684144
  25. Stigler, J. W. & Son, J. Y. (2018). Modelling first: Approach to teaching introductory statistics. In M. A. Sorto, A. White & L. Guyot (Eds.), Proceedings of the 10th International Conference on Teaching Statistics. The International Statistical Institute. https://icots.info/10/proceedings/pdfs/ICOTS10_9C3.pdf?1531364299
  26. Sotos, A. E. C., Vanhoof, S., Noortgate, W. V. D., & Onghena, P. (2009). The transitivity misconception of Pearson's correlation coefficient. Statistics Education Research Journal, 8(2), 33-55. https://doi.org/10.52041/serj.v8i2.394
  27. Teran, T. E. (2020). Advantages of applying the project method to teaching of statistics. In P. Arnold (Ed.), The Proceedings of 2020 IASE Roundtable Conference. International Association for Statistical Education. http://www.iase-web.org/documents/papers/rt2020/IASE2020%20Roundtable%2036_TERAN.pdf
  28. Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
  29. Wang, L., Bruce, C. S., & Hughes, H. (2011). Sociocultural theories and their application in information literacy research and education. Australian Academic and Research Libraries, 42(4), 296-308. https://doi.org/10.1080/00048623.2011.10722242
  30. Watson, J., & Neal, D. (2012). Preparing students for decision-making in the 21st century - statistics and probability in the Australian curriculum: Mathematics. In B. Atweh, M. Goos, R. Jorgensen & D. Siemon (Eds.), Engaging the Australian curriculum mathematics - Perspectives from the field (pp. 89-114). Mathematics Education Research Group of Australasia.
  31. Zavershneva, E., & van der Veer, R. (2018). Vygotsky's notebooks. Springer.