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

Educational Technology Research and Development

Volumn 65, Issue 3, 2017, Pages 579-595

  Tu, Wendy ^a   Snyder, Martha M ^b  

^a SANTA MONICA COLLEGE   (United States)

^b NOVA SOUTHEASTERN UNIVERSITY   (United States)

Author keywords

Educational technology; First Principles of Instruction; Instructional design; Real data; Statistics education

Indexed keywords

EID: 84988700668     PISSN: 10421629     EISSN: 15566501     Source Type: Journal    
DOI: 10.1007/s11423-016-9482-1     Document Type: Article

References (26)

1. American Statistical Association. (2005). GAISE college report. Retrieved September 8, 2009, from http://www.amstat.org/education/gaise.
2. Brown, E. N., & Kass, R. E. (2009). What is statistics? American Statistician, 63(2), 105–110. doi:10.1198/tast.2009.0019.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Easterling, R. G. (2010). Passion-driven statistics. The American Statistician, 64(1), 1–5. doi:10.1198/tast.2010.09180.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Gould, R. (2010). Statistics and modern student. Internal Statistical Review, 78(2), 297–315. doi:10.1111/j.1751-5823.2010.00117.x.
15. Gould, R., & Ryan, C. (2013). Introductory Statistics: Exploring the World through Data. Boston, MA: Pearson.
16. 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.
17. 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.
18. 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.
19. Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59.
20. Merrill, M. D. (2007). A task-centered instructional strategy. Journal of Research on Technology in Education, 40(1), 5–22.
21. 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.
22. 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.
23. Nolan, D., & Temple Lang, D. (2009). Comment to “What is statistics?”. American Statistician, 63(2), 117–121. doi:10.1198/tas.2009.0024.
24. 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.
25. Trumpower, D. (2010). Mad libs statistics: A ‘Happy’ activity. Teaching Statistics, 32(1), 17–20.
26. Yin, R. K. (2009). Case study research: Design and methods (4th ed.). Thousand Oaks: Sage.