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PIAGETIAN COGNITIVE LEVEL AND THE TENDENCY TO USE INTUITIVE RULES WHEN SOLVING COMPARISON TASKS

  • Reuven Babai
Article

ABSTRACT

According to the intuitive rules theory, students are affected by a small number of intuitive rules when solving a wide variety of science and mathematics tasks. The current study considers the relationship between students’ Piagetian cognitive levels and their tendency to answer in line with intuitive rules when solving comparison tasks. The findings indicate that the tendency to answer according to the intuitive rules varies with cognitive level. Surprisingly, a higher rate of incorrect responses according to the rule same A–same B was found for the higher cognitive level. Further findings and implications for science and mathematics education are discussed.

KEY WORDS

comparison task intuitive reasoning intuitive rules theory Piagetian cognitive level science reasoning task 

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REFERENCES

  1. Adey, P. (2005). Issues arising from the long-term evaluation of cognitive acceleration programs. Research in Science Education, 35, 3–22.CrossRefGoogle Scholar
  2. Adey, P., & Shayer, M. (1994). Really raising standards: Cognitive intervention and academic achievement. London: Routledge.Google Scholar
  3. Adey, P., Shayer, M., & Yates, C. (2001). Thinking science third edition, the materials of the CASE project. Cheltenham: Nelson Thornes.Google Scholar
  4. Babai, R., & Levit-Dori, T. (in press). Several CASE lessons can improve students’ control of variables reasoning scheme ability. Journal of Science Education and Technology. Google Scholar
  5. Babai, R., Brecher, T., Stavy, R., & Tirosh, D. (2006a). Intuitive interference in probabilistic reasoning. International Journal of Science and Mathematics Education, 4, 627–639.CrossRefGoogle Scholar
  6. Babai, R., Levyadun, T., Stavy, R., & Tirosh, D. (2006b). Intuitive rules in science and mathematics: A reaction time study. International Journal of Mathematical Education in Science and Technology, 37, 913–924.CrossRefGoogle Scholar
  7. Champagne, A. B., Klopfer, L. E., & Anderson, J. H. (1979). Factors influencing the learning of classical mechanics. Pittsburgh: University of Pittsburgh, Learning Research and Development Center.Google Scholar
  8. Fischbein, E. (1987). Intuition in science and mathematics. Dordrecht: Reidel.Google Scholar
  9. Fischbein, E. (1999). Intuitions and schemata in mathematical reasoning. Educational Studies in Mathematics, 38, 11–50.CrossRefGoogle Scholar
  10. Fischbein, E., & Schnarch, D. (1997). The evolution with age of probabilistic, intuitively based misconceptions. Journal for Research in Mathematics Education, 28, 96–105.CrossRefGoogle Scholar
  11. Greca, I. M., & Moreira, M. A. (2000). Mental models, conceptual models, and modeling. International Journal of Science Education, 22, 1–11.CrossRefGoogle Scholar
  12. Iqbal, H. M., & Shayer, M. (2000). Accelerating the development of formal thinking in Pakistan secondary school students: Achievement effects and professional development issues. Journal of Research in Science Teaching, 37, 259–274.CrossRefGoogle Scholar
  13. Mbano, N. (2003). The effects of a cognitive acceleration intervention programme on the performance of secondary school pupils in Malawi. International Journal of Science Education, 25, 71–87.CrossRefGoogle Scholar
  14. Perkins, D. N., & Simmons, R. (1988). Patterns of misunderstanding: An integrative model for science, math, and programming. Review of Educational Research, 58, 303–326.Google Scholar
  15. Piaget, J., & Inhelder, B. (1974). The child’s construction of quantities. London: Routledge.Google Scholar
  16. Shayer, M., & Adey, P. (1981). Towards a science of science teaching. London: Heinemann Educational Books.Google Scholar
  17. Shayer, M., & Adey, P. (2002). Learning intelligence. Buckingham: Open University Press.Google Scholar
  18. Shayer, M., Ginsburg, D., & Coe, R. (2007). Thirty years on a large anti-Flynn effect? The Piagetian test Volume & Heaviness norms. British Journal of Educational Psychology, 77, 25–41.CrossRefGoogle Scholar
  19. Shayer, M., Kuchemann, D. E., & Wylam, H. (1976). The distribution of Piagetian stages of thinking in British middle and secondary school children. British Journal of Educational Psychology, 46, 164–173.Google Scholar
  20. Shayer, M., & Wylam, H. (1978). The distribution of Piagetian stages of thinking in British middle and secondary school children II: 14–16 year-olds and sex differentials. British Journal of Educational Psychology, 48, 62–70.Google Scholar
  21. Shemesh, M., & Lazarowitz, R. (1988). The interactional effects of students’ cognitive levels and test characteristics on the performance of formal reasoning tasks. Research in Science & Technological Education, 6, 79–89.CrossRefGoogle Scholar
  22. Shemesh, M., & Lazarowitz, R. (1989). Pupils’ reasoning skills and their mastery of biological concepts. Journal of Biological Education, 23, 59–63.Google Scholar
  23. Stavy, R., & Babai, R. (2008). Complexity of shapes and quantitative reasoning in geometry. Mind, Brain, and Education, 2, 170–176.CrossRefGoogle Scholar
  24. Stavy, R., Babai, R., Tsamir, P., Tirosh, D., Lin, F. L., & McRobbie, C. (2006). Are intuitive rules universal? International Journal of Science and Mathematics Education, 4, 417–436.CrossRefGoogle Scholar
  25. Stavy, R., & Tirosh, D. (1996). Intuitive rules in science and mathematics: The case of ‘more of A–more of B’. International Journal of Science Education, 18, 653–667.CrossRefGoogle Scholar
  26. Stavy, R., & Tirosh, D. (2000). How students (mis-)understand science and mathematics: Intuitive rules. New York: Teachers College Press.Google Scholar
  27. Tirosh, D., & Stavy, R. (1999). Intuitive rules: A way to explain and predict students’ reasoning. Educational Studies in Mathematics, 38, 51–66.CrossRefGoogle Scholar
  28. Vosniadou, S., & Ioannides, C. (1998). From conceptual development to science education: A psychological point of view. International Journal of Science Education, 20, 1213–1230.CrossRefGoogle Scholar
  29. Zazkis, R. (1999). Intuitive rules in number theory: Example of ‘the more of A, the more of B’ rule implementation. Educational Studies in Mathematics, 40, 197–209.CrossRefGoogle Scholar

Copyright information

© National Science Council, Taiwan 2009

Authors and Affiliations

  1. 1.Department of Science Education The Constantiner School of EducationTel Aviv UniversityTel AvivIsrael

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