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The Shape of Intellectual Demands in East-Asian Primary Science Curricula

  • Yew-Jin LeeEmail author
  • Mijung Kim
  • Qingna Jin
  • Hye-Gyoung Yoon
  • Kenji Matsubara
Chapter
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Part of the SpringerBriefs in Education book series (BRIEFSEDUCAT)

Abstract

What can we learn from this study that explored the intellectual demands of the intended primary science curriculum among six East-Asian states? First of all, we do not detect patterns in any state that showed that their learning objectives were skewed towards the higher-order categories in RBT. For all states with the exception of Japan, we find a general distribution of items that parallel those from many other curricula in the world (e.g., DeMers in Journal of Geography in Higher Education 33(Supplement 1):S70–S77, 2009; Fitzpatrick and Schulz in Canadian Journal of Science, Mathematics and Technology Education 15:136–154, 2015). That is, there was a clustering of items within the first three categories within the knowledge as well as the cognitive dimensions of RBT. Conversely, there was a scarcity of items beyond Analyze (ranging from 0 to 10 %) (Tables 1 and 2 in Chap. 4) just as there were very few objectives located in the metacognitive category (from 0 to 2 %). There seems to be some cause for further research into whether East-Asian students are exposed to sufficient challenge as their governments are eager to devise curricula that raise both the quality and quantity of scientific literacy among school-going populations. Recall, however, that these results might possibly be an effect of adopting RBT that was originally designed by educational psychologists rather than a true reflection of the intellectual work needed to do, understand, and apply science in school. Nor do these findings derived from our coding practices have any close relationship with what goes on in classrooms in these states. That is, excellent science instruction can occur despite supposedly low figures in RBT in the intended curriculum.

Keywords

Primary Science Intellectual Demands Excellent Science Education Metacognitive Categories School-going Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. DeMers, M. N. (2009). Using intended learning objectives to assess curriculum materials: The UGIS body of knowledge. Journal of Geography in Higher Education, 33(Supplement 1), S70–S77.CrossRefGoogle Scholar
  2. Fitzpatrick, B., & Schulz, H. (2015). Do curriculum outcomes and assessment activities in science encourage higher order thinking? Canadian Journal of Science, Mathematics and Technology Education, 15, 136–154.CrossRefGoogle Scholar
  3. Lee, Y.-J., Kim, M., & Yoon, H.-G. (2015). The intellectual demands of the intended primary science curriculum in Korea and Singapore: An analysis based on revised Bloom’s taxonomy. International Journal of Science Education, 37, 2193–2213.CrossRefGoogle Scholar
  4. TIMSS (2007). Appendix C. The test-curriculum matching analysis: Science. Available http://timss.bc.edu/timss2007/PDF/T07_S_IR_AppendixC.pdf

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Yew-Jin Lee
    • 1
    Email author
  • Mijung Kim
    • 2
  • Qingna Jin
    • 2
  • Hye-Gyoung Yoon
    • 3
  • Kenji Matsubara
    • 4
  1. 1.National Institute of EducationNanyang Technological UniversitySingaporeSingapore
  2. 2.University of AlbertaEdmontonCanada
  3. 3.Chuncheon National University of EducationChuncheonKorea (Republic of)
  4. 4.National Institute for Educational Policy ResearchTokyoJapan

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