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Students’ Understanding of Variation on Entering Tertiary Education

  • Robyn Reaburn
Part of the Bold Visions in Educational Research book series (BVER)

Abstract

Researchers use statistics to make judgements about their data. Did the new drug work better than the old drug? What is the best combination of feed, temperature and water conditions for breeding abalone in an artificial environment? What methods of teaching fractions work? In all these situations, researchers only have access to a part of the population, a sample. It is not possible to examine all the people who will ever take a drug to see how it works, nor is it possible to test all the students who are learning about fractions.

Keywords

Inferential Statistic Partial Credit Partial Credit Model Australian Curriculum Coin Toss 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Australian Curriculum Assessment and Reporting Authority. (2012, January). The Australian Curriculum: Mathematics, Version 3.0. Sydney: ACARA.Google Scholar
  2. Biggs, J., & Collis, K. (1982). Evaluating the quality of learning: The SOLO taxonomy (Structure of the Observed Learning Outcome). New York: Academic Press.Google Scholar
  3. Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Malwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  4. Kahneman, D., & Tversky, A. (1982). Subjective probability: A judgement of representativeness. In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgement under uncertainty: Heuristics and biases (pp. 32–47). New York: Cambridge University Press.CrossRefGoogle Scholar
  5. Masters, G. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174.CrossRefGoogle Scholar
  6. Reaburn, R. (2011). Students’ understanding of statistical inference: Implications for teaching. PhD Thesis, University of Tasmania: Tasmania, Australia.Google Scholar
  7. Reading, C., & Shaughnessy, J. (2005). Reasoning about variation. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 201–226). The Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
  8. Rubin, A., Bruce, B., & Tenney, Y. (1991). Learning about sampling: Trouble at the core of statistics. In D. Vere-Jones (Ed.), Proceedings of the 3 rd International Conference on Teaching Statistics (Vol. 1, pp. 314–319.). Voorburg, The Netherlands: International Statistical Institute.Google Scholar
  9. Shaughnessy, J. M., Watson, J., Moritz, J., & Reading, C. (1999). School mathematics students’ acknowledgment of statistical variation. In There’s more to life than centers. Presession Research Symposium 77th Annual National Council of Teachers of Mathematics Conference, CA: NTCM.Google Scholar
  10. Torok, R., & Watson, J. (2000). Development of the concept of statistical variation: An exploratory study. Mathematical Education Research Journal, 12(2), 147–169.CrossRefGoogle Scholar
  11. Watson, J., & Kelly, B. (2004). Statistical variation in a chance setting: A two-year study. Educational Studies in Mathematics, 57(1), 121–144.CrossRefGoogle Scholar
  12. Watson, J., Kelly, B., Callingham, R., & Shaughnessy, J. (2003). The measurement of school students’ understanding of statistical variation. International Journal of Mathematical Education in Science and Technology, 34(1), 1–29.CrossRefGoogle Scholar
  13. Wild, C., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.Google Scholar

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© Sense Publishers 2014

Authors and Affiliations

  • Robyn Reaburn

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