Abstract
Developing students' ability to interpret the vast amount of quantitative data they encounter on a daily basis has become a major task for today's educators. However, very little attention has been given to students' strategies of analyzing multivariate data. This study investigated how students interpret and analyze multivariate data organized in tables and the nature of external visual displays that they tend to create and use for this purpose. Ten middle school students were asked to think aloud while solving five problems demanding an analysis of data organized in tables. The students were then interviewed. Results indicated that (1) students based their conclusions on only part of the data; (2) students did not use either efficient or sufficient visual representations; (3) students did not apply mathematical operations efficiently; and (4) students referred to or built a context to the problem. The results of the current research may assist educators to design efficient curricula while being aware of and taking into account (1) students' difficulties in employing previously learned mathematical devices to analyze data, (2) students' skills in choosing appropriate and efficient visual representations to present and interpret the data, and (3) strategies employed by students in analyzing multivariate data.
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REFERENCES
Bowen, G. M., Roth, W.-M., and McGinn, M. K. (1999). Interpretation of graphs by university biology students and practicing scientists: Toward a social practice view of scientific representation practices. Journal of Research in Science Teaching 36: 1020-1043.
Chi, M. T. H., Feltovich, P., and Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science 5: 121-152.
Cohen, V. (1989). News and Numbers, Iowa State University Press, Ames, Iowa.
DiSessa, A. A., Hammer, D., Sherin, B., and Kolpakowski, T. (1991). Inventing graphing: Meta-representational expertise in children. Journal of Mathematical Behavior 10: 117-160.
Guba, E., and Lincoln, Y. S. (1989). Fourth Generation Evaluation, Sage, Beverly Hills, CA.
Hancock, C., Kaput, J. J., and Goldsmith, T. L. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist 27: 337-364.
Heller, J., and Reif, F. (1984). Prescribing effective human problem-solving processes: Problem description in physics. Cognition and Instruction 1: 177-216.
Hesse, M. B. (1965). Forces and Fields: A Study of Action at a Distance in the History of Physics, Littlefield, Adams, Totowa, NJ.
Huff, D. (1954). How to Lie with Statistics, Norton, New York.
Inhelder, B., and Piaget, J. (1958). The Growth of Logical Thinking from Childhood to Adolescence, Basic Books, New York.
Jordan, B., and Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences 4: 39-103.
Kroll, D. L., and Miller, T. (1999). Insights from research on mathematical problem solving in the middle grades. In Owens, D. T. (Ed.), Research Ideas for the Classroom: Middle Grades Mathematics, Macmillan, New York, pp. 58-77.
Larkin, J. H., and Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11: 65-99
Lehrer, R., and Romberg, T. (1996). Exploring children's data modeling. Cognition and Instruction 14: 69-108.
Mayer, E. R. (1989). Models for understanding. Review of Educational Research 59: 43-64.
National Council of Teachers of Mathematics (1989). Curriculum and Evaluation Standards for School Mathematics, NCTM, Reston, VA.
Patton, M. Q. (1990). Qualitative Evaluation and Research Methods, 2nd ed., Sage, Newbury Park, CA.
Paulos, J. A. (1996). A Mathematician Reads the Newspaper, Doubleday, New York.
Roth, W. M. (1995). Affordances of computers in teacher-student interactions: The case of Interactive Physics?. Journal of Research in Science Teaching 32: 329-347.
Schutz, A. (1967). The Phenomenology of the Social World (Walsh, G., and Lenhert, F., Trans.), Northwestern University Press, Chicago.
Seidman, E. I. (1998). Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences, Teachers College Press, New York.
Steen, L. A. (Ed.) (1997). Why Numbers Count: Quantitative Literacy for Tomorrow's America, The College Board, New York.
Steen, L. A. (1999). Numeracy: The new literacy for a data drenched society. Educational Leadership 15: 8-13.
Strauss, A. L. (1987). Qualitative Analysis for Social Scientists, Cambridge University Press, New York.
Tufte, R. E. (1983). The Visual Display of Quantitative Information, Graphics Press, Cheshire, CT.
Tufte, R. E. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative, Graphics Press, Cheshire, CT.
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Eshach, H., Schwartz, J.L. Understanding Children's Comprehension of Visual Displays of Complex Information. Journal of Science Education and Technology 11, 333–346 (2002). https://doi.org/10.1023/A:1020690201324
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DOI: https://doi.org/10.1023/A:1020690201324