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Developing conceptual understanding in a statistics course: Merrill’s First Principles and real data at work

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Abstract

Difficulties in learning statistics primarily at the college-level led to a reform movement in statistics education in the early 1990s. Although much work has been done, effective learning designs that facilitate active learning, conceptual understanding of statistics, and the use of real-data in the classroom are needed. Guided by Merrill’s First Principles of Instruction (First Principles), a blended, introductory college-level statistics course that incorporated real data was designed and implemented. A single descriptive case design was used to investigate how the course design facilitated learning and the development of statistical conceptual understanding (i.e., statistical literacy, reasoning, and thinking skills). Results from both quantitative and qualitative data analyses indicated that the course designed using First Principles as a guide was effective in promoting students’ conceptual understanding in terms of literacy, reasoning, and thinking statistically. However, students’ statistical literacy, specifically, the understanding of statistical terminology did not develop to a satisfactory level as expected.

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References

  • American Statistical Association. (2005). GAISE college report. Retrieved September 8, 2009, from http://www.amstat.org/education/gaise.

  • Brown, E. N., & Kass, R. E. (2009). What is statistics? American Statistician, 63(2), 105–110. doi:10.1198/tast.2009.0019.

    Article  Google Scholar 

  • Chance, B. L. (2002). Components of statistical thinking and implications for instruction and assessment. Journal of Statistics Education, 10(3). Retrieved March 26, 2010, from www.amstat.org/publications/jse/v10n3/chance.html.

  • Chick, H., & Pierce, R. (2010). Helping teachers to make effective use of real-world examples in statistics. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the eighth international conference on teaching statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg: International Statistical Institute. www.stat.auckland.ac.nz/~iase/publications.php.

  • Cobb, G. (1992). Teaching Statistics. In L. A. Steen (Ed.), Heeding the call for change (MAA Notes No. 22, pp. 3–46). The Mathematical Association of America.

  • David, I., & Brown, J. (2010). Implementing the change: Teaching statistical thinking not just methods. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the eighth international conference on teaching statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg: International Statistical Institute.

  • delMas, R. C. (2002). Statistical literacy, reasoning, and learning: A commentary. Journal of Statistics Education, 10(3). Retrieved March 26, 2010, from www.amstat.org/publications/jse/v10n3/delmas_discussion.html.

  • Easterling, R. G. (2010). Passion-driven statistics. The American Statistician, 64(1), 1–5. doi:10.1198/tast.2010.09180.

    Article  Google Scholar 

  • Elo, S., & Kyngäs, H. (2007). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. doi:10.1111/j.1365-2648.2007.04569.x.

    Article  Google Scholar 

  • Franklin, C., & Garfield, J. B. (2006). The GAISE project: Developing statistics education guidelines for pre K-12 and college courses. In G. Burrill (Ed.), Thinking and reasoning with data and chance: 2006 NCTM yearbook (pp. 375–435). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Garfield, J. (2002). The challenge of developing statistical reasoning. Journal of Statistics Education, 10(3). Retrieved March 26, 2010, from www.amstat.org/publications/jse/v10n3/garfield.html.

  • Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396.

    Article  Google Scholar 

  • Garfield, J. B., & Gal, I. (1999). Teaching and assessing statistical reasoning. In L. Stiff (Ed.), Developing mathematical reasoning in grades K-12: 1999 NCTM yearbook (pp. 207–219). Reston: National Council Teachers of Mathematics.

    Google Scholar 

  • Gould, R. (2010). Statistics and modern student. Internal Statistical Review, 78(2), 297–315. doi:10.1111/j.1751-5823.2010.00117.x.

    Article  Google Scholar 

  • Gould, R., & Ryan, C. (2013). Introductory Statistics: Exploring the World through Data. Boston, MA: Pearson.

  • Hoerl, R. W., & Snee, R. D. (2010). Moving the statistics profession forward to the next level. The American Statistician, 64(1), 10–13. doi:10.1198/tast.2010.09240.

    Article  Google Scholar 

  • Hogg, R. V. (1992). Towards lean and lively courses in statistics. In F. Gordon & S. Gordon (Eds.), Statistics for the Twenty-First Century (MAA Notes, No. 26, pp. 3–13). The Mathematical Association of America.

  • Meng, X. (2009). Desired and feared—What do we do now and over the next 50 years? The American Statistician, 63(3), 202–210. doi:10.1198/tast.2009.06045.

    Article  Google Scholar 

  • Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59.

    Article  Google Scholar 

  • Merrill, M. D. (2007). A task-centered instructional strategy. Journal of Research on Technology in Education, 40(1), 5–22.

    Article  Google Scholar 

  • Merrill, M. D. (2009). First principles of instruction. In C. M. Reigeluth & A. A. Carr-Chellman (Eds.), Instructional-design theories and models (Vol. 3, pp. 41–56)., Building a common knowledge base New York: Routledge.

    Google Scholar 

  • Merrill, M. D., & Gilbert, C. G. (2008). Effective peer interaction in a problem-centered instructional strategy. Distance Education, 29(2), 199–206. doi:10.1080/01587910802154996.

    Article  Google Scholar 

  • Nolan, D., & Temple Lang, D. (2009). Comment to “What is statistics?”. American Statistician, 63(2), 117–121. doi:10.1198/tas.2009.0024.

    Article  Google Scholar 

  • Rumsey, D. J. (2002). Statistical literacy as a goal for introductory statistics courses. Journal of Statistics Education, 10(3). Retrieved March 26, 2010, from www.amstat.org/publications/jse/v10n3/rumsey2.html.

  • Trumpower, D. (2010). Mad libs statistics: A ‘Happy’ activity. Teaching Statistics, 32(1), 17–20.

    Article  Google Scholar 

  • Yin, R. K. (2009). Case study research: Design and methods (4th ed.). Thousand Oaks: Sage.

    Google Scholar 

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Acknowledgments

We would like to thank Drs. Trudy Abramson and Nina Miville for their expert guidance as well as the students who agreed to participate in this research study.

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Correspondence to Wendy Tu.

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Tu, W., Snyder, M.M. Developing conceptual understanding in a statistics course: Merrill’s First Principles and real data at work. Education Tech Research Dev 65, 579–595 (2017). https://doi.org/10.1007/s11423-016-9482-1

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