Session Information
24 SES 06 B, Teaching and Learning of Mathematics
Paper Session
Contribution
We are faced with quantitative information (graphs, tables, raw data, charts, etc.) everyday. Calls for reform recommended that students needs to learn to deal with this quantitative information effectively throughout their education (National Council of Teachers of Mathematics (NCTM), 2000; Australian Education Council, 1994). In other words, students need to have experiences of collecting, organizing, representing, analyzing and interpreting data as early as possible in their schooling (Mooney, Hofbauer, Langrall, and Johnson, 2001; Mooney, 2002; Shaughnessy, Garfield, & Greer, 1996). In line with these recommendations, development of statistical skills is part of middle school mathematics curricula in many countries. In Turkey, statistics and probability became a major strand in elementary school mathematics curriculum in 2005 (Ministry of National Education of Turkey, 2005). Although a substantial body of research has been conducted on elementary and middle school students’ individual statistical thinking on particular statistics concepts or processes (Curcio, 1987; Strauss & Bichler, 1988; Mokros & Russell, 1995; Watson & Moritz, 2000), there is a need for a coherent and valid framework for characterizing students’ statistical thinking for a teaching as envisioned by these documents. There have been some attempts to develop such frameworks for ascertaining the levels of thinking that can be expected from students (Mooney, 2002; Mooney, et al., 2001; Pfannkuch and Wild, 2003; Jones, et. Al., 2000). To guide the efforts to improve statistics education in Turkey, there is a need to examine the development of statistical understanding in middle school students. In this study, Mooney’s framework was used to assess Turkish students’ statistical thinking. Mooney’s Middle School Students’ Statistical Thinking Framework was based on Biggs and Collis’s (1991) general developmental model, Structured Observed Learning Outcome (SOLO). This framework describe that elementary school students exhibit four levels of statistical thinking across four processes (describing data, organizing and reducing data, representing data, and analyzing and interpreting data) in accord with Biggs and Collis’s (1991) model. More specifically, the purpose of this study was to investigate middle school students’ statistical thinking across four processes: describing data, organizing and reducing data, representing data, and analyzing and interpreting data using Mooney’s framework. In addition, it was aimed at determining how students’ statistical thinking levels change with respect to the grade level, gender, and mathematics achievement. Finally, we wanted to provide an empirical evidence for the validity of the framework by testing it with a student population with different social and cultural background from the student population in the original study in which the framework was developed and validated.
Method
Expected Outcomes
References
Australian Education Council (1994). Mathematics: A curriculum profile for Australian school. Carlton, VIC: Curriculum Corporation. Biggs, J.B., & Collis, K.F. (1991). Multimodal learning and intelligent behavior. In H. Rowe (Ed.), Intelligence: Reconceptualization and measurement (pp. 57-76). Hillsdale, NJ: Erlbaum. Curcio, F.R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18, 382-393. Jones, G.A., Thornton, C.A., Langrall, C.W., Mooney, E., Perry, B., & Putt, I. (2000). A framework for characterizing students’ statistical thinking. Mathematical Thinking and Learning, 2, 269-307. Ministry of National Education of Turkey (2005). Elementary mathematics curriculum. Ankara, Turkey: Department of Research and Development in Education. Mokros, J., & Russell, S.J. (1995). Children’s concepts of average and representativeness. Journal for Research in Mathematics Education, 26, 20-39. Mooney (2002). A Framework for Characterizing Middle School Students’ Statistical Thinking. Mathematical Thinking And Learning, 4(1), 23-63. Mooney, E. S., Langrall, C. W., Hofbauer, P. S., Johnson, Y. A. (2000). Refining A Framework On Middle School Students’ Statistical Thinking. Educational Resources Information Center (ERIC), ED, 476-626. National Council of Teachers of Mathematics (2000). Principles and Standards for School Mathematics. Reston, VA: Author. Pfannkuch, M. & Wild, C.J. (2003). Statistical thinking: How can we develop it? In Proceedings of the 54th International Statistical Institute Conference [CD-ROM]. Voorburg, The Netherlands: International Statistical Institute. Shaughnessy, J. M., Garfield, J., & Greer, B. (1996). Data handling. In A. J. Bishop, K. Clements, C. Keitel, J. Kilpatrick,&C. Laborde (Eds.), International handbook of mathematics education (Pt. 1, pp. 205–238). Dordrecht, The Netherlands: Kluwer. Strauss, S., & Bichler, E. (1988). The development of children's concepts of the arithmetic average. Journal for Research in Mathematics Education, 19, 64-80. Watson, J.M., & Moritz, J.B. (2000). The longitudinal development of the understanding of average. Mathematical Thinking and Learning, 2, 11-50.
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