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Technological Tools in the Introductory Statistics Classroom: Effects on Student Understanding of Inferential Statistics

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Abstract

While technology has become an integral part of introductory statistics courses, the programs typically employed are professional packages designed primarily for data analysis rather than for learning. Findings from several studies suggest that use of such software in the introductory statistics classroom may not be very effective in helping students to build intuitions about the fundamental statistical ideas of sampling distribution and inferential statistics. The paper describes an instructional experiment which explored the capabilities of Fathom, one of several recently-developed packages explicitly designed to enhance learning. Findings from the study indicate that use of Fathom led students to the construction of a fairly coherent mental model of sampling distributions and other key concepts related to statistical inference. The insights gained point to a number of critical ingredients that statistics educators should consider when choosing statistical software. They also provide suggestions about how to approach the particularly challenging topic of statistical inference.

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REFERENCES

  • Batanero, C., Estepa, A. and Godino, J.D. (1997). Evolution of students' understanding of statistical association in a computer-based teaching environment. In J.B. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 198-212). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Behrens, J.T. (1997). Toward a theory and practice of using interactive graphics in statistical education. In J.B. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 111-121). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Ben-Zvi, D. (2000). Toward understanding the role of technological tools in statistical learning. Mathematical Thinking and Learning 2: 127-155.

    Article  Google Scholar 

  • Burrill, G. (1997). Discussion: How technology is changing the teaching and learning of statistics in secondary school. In J.B. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 71-74). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Erickson, T. (2000). Data in Depth. Exploring Mathematics with Fathom. Emeryville, CA: Key Curriculum Press.

    Google Scholar 

  • Finzer, W. (1999). Fathom. [Software] (1993). Berkeley, CA: Key Curriculum Press.

    Google Scholar 

  • in_Teaching_Math.pdf}).

  • Finzer, W., and Timothy E. (1998). DataSpace - A computer learning environment for data analysis and statistics based on dynamic dragging, visualization, simulation, and networked collaboration. Proceedings of the Fifth International Conference on Teaching of Statistics. Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Garfield, J. (1997). Preface. In J. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. ix-xi). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Garfield, J., Hogg, B., Schau, C. and Whittinghill, D. (2002). First courses in statistical science: The status of educational reform efforts. Journal of Statistics Education [Online], 10(2): (http://www.amstat.org/publications/jse/v10n2/garfield.html).

  • Hawkins, A. (1997a). Myth-conceptions. In J.B. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. vii-viii). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Hawkins, A. (1997b). Children's understanding of sampling in surveys. In J.B. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 1-14). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Hoyles, C., and Noss R. (1994). Dynamic geometry environments: What's the point? The Mathematics Teacher 87(9): 176-117.

    Google Scholar 

  • Joiner, B.L. and Ryan, B.F. (2000). MINITAB(r) Handbook. Pacific Grove, CA: Brooks/Cole Publishing Co.

    Google Scholar 

  • Lee, C. (1997). Promoting active learning in an introductory statistics course using the PACE strategy. Proceedings of the 6th International Symposium on Mathematics Education (pp. 199-206). Mexico City.

  • Lee, C. (1998). An Assessment of the PACE strategy for an introductory statistics course. Proceedings of the Fifth International Conference on Teaching Statistics.Singapore.

  • Lee, C. (2000). Computer-assisted approach for teaching statistical concepts. Journal of Computers in the Schools 16(1): 193-208.

    Article  Google Scholar 

  • Lipson, K. (1997). What do students gain from simulation exercises? An evaluation of activities designed to develop an understanding of the sampling distribution of a proportion. In J. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 137-150). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Lock, R.H. (2002). Using Fathom to promote interactive explorations of statistical concepts. Proceedings of the Sixth International Conference on Teaching Statistics. Durban, South Africa.

    Google Scholar 

  • Meletiou, M., Lee, C.M. and Myers, M. (1999). The role of technology in the introductory statistics classroom: Reality and potential. Proceedings of the International Conference on Mathematics/Science Education and Technology. San Antonio, Texas.

  • Meletiou-Mavrotheris, M. and Stylianou, D. (2003). Graphical representation of data: The effect of the use of a dynamical statistics technological tool. To appear in: Proceedings of the Sixth International Conference on Computer Based Learning in Science.

  • Moore, D. (1997). New pedagogy and new content: The case of statistics. International Statistical Review 65(2): 123-165.

    Google Scholar 

  • Noss, R. and Hoyles, C. (1996). Windows on Mathematical Meanings: Learning Cultures and Computers. London: Kluwer Academic Publishers.

    Google Scholar 

  • Pratt, D.C. (1998). The Construction of Meanings in and for a Stochastic Domain of Abstraction. Ph.D. Thesis. University of London.

  • Rubin, A. (2002). Interactive visualizations of statistical relationships: What do we gain? Proceedings of the Sixth International Conference on Teaching Statistics. Durban, South Africa.

    Google Scholar 

  • Schau, C. and Mattern, N. (1997). Assessing students' understanding of statistical relationships. In I. Gal and J.B. Garfield (Eds), The Assessment Challenge in Statistics Education. Burke, VA: IOS Press.

    Google Scholar 

  • Scheaffer, R.L. (1997). Discussion. International Statistical Review 65(2): 156-158.

    Google Scholar 

  • Scheaffer, R.L., Gnanadesikan, M., Watkins, A. and Witmer, J.F. (1996). Activity-Based Statistics: Instructor Resources. New York: Springer-Verlag, Inc.

    Google Scholar 

  • Shaughnessy, J.M., Garfield, J.B. and Greer, B. (1997). Data handling. In A.J. Bishop, K. Clements, C. Keitel, J. Kilpatrick and C. Laborde (Eds), International Handbook on Mathematics Education (pp. 205-237). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Starkings, S. (1997). How technological introduction changes the teaching of statistics and probability at the college level. In J. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 233-254). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Thomas, D.A. (1984). Understanding the central limit theorem. Mathematics Teacher 77: 542-543.

    Google Scholar 

  • Thompson, P.W. and Saldanha, L. (2000). Conceptual issues in understanding sampling distributions and margins of error. Proceedings of the 22nd Annual Meeting of the International Group for the Psychology of Mathematics Education. Tucson, Arizona.

  • Watson, J. and Baxter, J. (1997). Learning the unlikely at distance as an information technology enterprise: Development and research. In J. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 288, 302). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Wild, C.J. and Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review 67(3): 223-265.

    Google Scholar 

  • Wilensky, U. (1993). Connected Mathematics - Building Concrete Relationships with Mathematical Knowledge. PhD Thesis, Massachusetts Institute of Technology.

  • Wood, M. (1997). Computer packages as a substitute for statistical training? In J. Garfield and G. Burrill (Eds), Research on the Role of Technology in Teaching and Learning Statistics (pp. 267-278). Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Yu, C.H. and Behrens, J.T. (1994). Identification of misconceptions in learning statistical power with dynamic graphics as remedial tool.ASAProStEd: 242-246.

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Authors and Affiliations

  1. Cyprus Ministry of Education, Cyprus

    Maria Meletiou-Mavrotheris

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  1. Maria Meletiou-Mavrotheris
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Meletiou-Mavrotheris, M. Technological Tools in the Introductory Statistics Classroom: Effects on Student Understanding of Inferential Statistics. International Journal of Computers for Mathematical Learning 8, 265–297 (2003). https://doi.org/10.1023/B:IJCO.0000021794.08422.65

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  • DOI: https://doi.org/10.1023/B:IJCO.0000021794.08422.65

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