• Jung Chih ChenEmail author
  • Barbara J. Reys
  • Robert E. Reys


This study analyzes measurement learning expectations involving area and volume in grades 1–8 across several U.S. states and high-performing TIMSS Asian countries, including Singapore, Taiwan, and Japan. Based on a review of official curriculum documents, results of this study indicate that the mathematics content, grade placement, and cognitive level of learning expectations related to selected measurement topics vary markedly across states and countries. This variability in learning expectations results in striking differences in students’ opportunity to learn.


learning expectation opportunity to learn TIMSS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Achieve, Inc., (2004). Mathematics Achievement Partnership, K-8 Mathematics Expectations, December 2004 Draft. Available:
  2. Bakeman, R. (2000). Behavioral observation and coding. In H.T. Reis & C.M. Judge (Eds.), Handbook of research methods in social and personality psychology. New York: Cambridge University Press.Google Scholar
  3. CCSSO (1999). Survey of instructional content grade K-8 mathematics. Washington, DC: Council of Chief State School Officers.Google Scholar
  4. Chen, J.C. (2005). Analysis of selected geometry and measurement learning expectations in some Asian countries and U.S. states as specified in national and state curriculum documents. Columbia, USA: Doctoral dissertation, University of Missouri.Google Scholar
  5. De Lange, J. (2007). Large-scale assessment of mathematics education. In F.K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 1111–1144. )Charlotte, NC: Information Age Publishing.Google Scholar
  6. Floden, R.E. (2002). The measurement of opportunity to learn. In C. P. Andrew & A. Gamoran (Eds.), Methodological advances in cross-national surveys of educational achievement. Washington, DC: National Academy Press.Google Scholar
  7. Kloosterman, P. & Lester, F.K. (2004). Results and interpretations of the 1990–2000 mathematics assessments of the national assessment of educational progress. Reston, VA: National Council of Teachers of Mathematics.Google Scholar
  8. Kolbe, R.H. & Burnett, M.S. (1991). Content-analysis research: An examination of applications with directives for improving research reliability and objectivity. Journal of Consumer Research, 18, 243–250.CrossRefGoogle Scholar
  9. McKnight, C.C., Crosswhite, F.J., Dossey, J.A., Kifer, E., Swafford, J.O., Travers, K.J. & Cooney, T.J. (1987). The underachieving curriculum: assessing U.S. school mathematics from an international perspective. Champaign, Illinois: Stipes Publishing Company.Google Scholar
  10. National Council of Teachers of Mathematics. (2000). Principles and Standards for School Mathematics. Reston, VA: Author.Google Scholar
  11. Porter, A.C. (2002). Measuring the content of instruction: uses in research and practice. Educational Researcher, 31(7), 3–14. October.CrossRefGoogle Scholar
  12. Reys, B.J.(Ed.). (2006). The intended mathematics curriculum as represented in state-level curriculum standards, Charlotte, NC: Information Age Publishing.Google Scholar
  13. Reys, B.J., Robinson, E., Sconiers, S. & Mark, J. (1999). Mathematics curricula based on rigorous national standards: What, why and how? Phi Delta Kappan, 80(6), 454–456.Google Scholar
  14. Schmidt, W.H., McKnight, C.C., Valverde, G.A., Houang, R.T. & Wiley, D.E. (1997). Many visions, many aims, volume 1. A cross-national investigation of curricular intentions in school mathematics. TIMSS, Boston: Kluwer Academic Publishers.Google Scholar
  15. Schmidt, W.H., McKnight, C.C., Houang, R.T., Wang, H.C., Wiley, D.E., Cogan, L.S. & Wolfe, R.G. (2001). Why schools matter: a cross-national comparison of curriculum and learning. San Francisco, CA, USA: JOSSEY-BASS, A Wiley Company.Google Scholar
  16. The No Child Left Behind Act. (2001). Public Law No. 107–110. Retrieved January 13, 2005, from
  17. Valverde, G.A., Bianchi, L.J., Wolfe, R.G., Schmidt, W.H. & Houang, R.T. (2002). According to the book: using TIMSS to investigate the translation of policy into practice through the world of textbooks. The Netherlands: Kluwer Academic Publishers.Google Scholar
  18. Venezky, R.L. (1996). Textbooks in school and society. In P. W. Jackson (Ed.), Handbook of research on curriculum. A project of the American educational research association. New York: Simon & Schuster Macmillan.Google Scholar
  19. Webb, N.L. (1993). Mathematics education reform in California. In OECD documents: proceedings of a conference. Science and mathematics education in the United States: Eight innovations. (pp. 117–142. )France: OECD.Google Scholar
  20. Weiss, I., Pasley, J., Smith, P., Banilower, E. & Heck, D. (2003). Looking inside the classroom: a study of k-12 mathematics and science education in the United States. Chapel Hill, NC: Horizon Research.Google Scholar
  21. Whitman, D. (2004). The mad, mad world of textbook adoption. Washington, DC: Thomas B. Fordham Institute.Google Scholar
  22. Wilson, L.D. & Blank, R.K. (1999). Improving Mathematics Education Using Results from NAEP and TIMSS. Council of Chief State School Officers, State Education Assessment Center.Google Scholar
  23. Yaffee, R.A. (2005). Common correlation and reliability analysis with SPSS for Windows. Retrieved June 30, 2005, from

Copyright information

© National Science Council, Taiwan 2008

Authors and Affiliations

  • Jung Chih Chen
    • 1
    Email author
  • Barbara J. Reys
    • 2
  • Robert E. Reys
    • 2
  1. 1.Department of Applied MathematicsNational Chiayi UniversityChiayiTaiwan
  2. 2.Department of Learning, Teaching & CurriculumUniversity of MissouriColumbiaUSA

Personalised recommendations