Abstract
We used the Rule Space Model, a cognitive diagnostic model, to measure the learning progression for thermochemistry for senior high school students. We extracted five attributes and proposed their hierarchical relationships to model the construct of thermochemistry at four levels using a hypothesized learning progression. For this study, we developed 24 test items addressing the attributes of exothermic and endothermic reactions, chemical bonds and heat quantity change, reaction heat and enthalpy, thermochemical equations, and Hess’s law. The test was administered to a sample base of 694 senior high school students taught in 3 schools across 2 cities. Results based on the Rule Space Model analysis indicated that (1) the test items developed by the Rule Space Model were of high psychometric quality for good analysis of difficulties, discriminations, reliabilities, and validities; (2) the Rule Space Model analysis classified the students into seven different attribute mastery patterns; and (3) the initial hypothesized learning progression was modified by the attribute mastery patterns and the learning paths to be more precise and detailed.
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References
Alonzo, A. Z., & Gotwals, A. W. (2012). Learning progressions in science: current challenges and future directions. Rotterdam: Sense Publishers.
American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York: Oxford University Press.
Anderson, C. W. (2008). Learning progressions for environmental science literacy: overview of the interactive poster symposium. In Annual Meeting of the National Association for Research in Science Teaching, Baltimore, MD
Ayyildiz, Y., & Tarhan, L. (2012). The effective concepts on students’ understanding of chemical reactions and energy. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi (HU Journal of Education), 42, 72–83.
Birenbaum, M., Kelly, A. E., & Tatsuoka, K. K. (1993). Diagnosing knowledge states in algebra using the rule-space model. Journal for Research in Mathematics Education, 24(5), 442–459.
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: fundamental measurement in the human sciences (2nd ed.). Mahwah: Lawrence Erlbaum Associates Publishers.
Boo, H. K. (1998). Students’ understandings of chemical bonds and the energetics of chemical reactions. Journal of Research in Science Teaching, 35(5), 569–581.
Briggs, D. C., & Alonzo, A. C. (2012). The psychometric modeling of ordered multiple-choice item responses for diagnostic assessment with a learning progression. In A. C. Alonzo & A. W. Gotwals (Eds.), Learning progressions in science: current challenges and future directions (pp. 293–316). Rotterdam: Sense Publishers.
Dogan, E., & Tatsuoka, K. (2008). An international comparison using a diagnostic testing model: Turkish students’ profile of mathematical skills on TIMSS-R. Educational Studies in Mathematics, 68(3), 263–272.
Duncan, R. G., & Hmelo-Silver, C. E. (2009). Learning progressions: aligning curriculum, instruction, and assessment. Journal of Research in Science Teaching, 46(6), 606–609.
Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. L., & Reckase, M. D. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21(4), 347–360.
Harlen, W., & Bell, D. (2010). Principles and big ideas of science education. Hatfield Herts: Association for Science Education.
Lee, H. S., & Liu, O. L. (2010). Assessing learning progression of energy concepts across middle school grades: the knowledge integration perspective. Science Education, 94(4), 665–688.
Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: a variation on Tatsuoka's rule-space approach. Journal of Educational Measurement, 41(3), 205–237.
Li, F., Yu, N., & Xin, T. (2009). Development of diagnostic math test for grader 4 and grader 5 based on the rule space model. Psychological Development and Education, 3, 113–118.
Liu, X., & Collard, S. (2004). Using the Rasch model to validate stages of understanding the energy concept. Journal of Applied Measurement, 6(2), 224–241.
Liu, X., & McKeough, A. (2005). Developmental growth in students’ concept of energy: analysis of selected items from the TIMSS database. Journal of Research in Science Teaching, 42(5), 493–517.
Michell, J. (2008). Is psychometrics pathological science? Measurement, 6(1–2), 7–24.
Ministry of Education of the People's Republic of China. (2003). Curriculum standards of chemistry for senior high school. Beijing: People’s Education Press.
National Research Council. (1996). National science education standards. Washington: National Academy Press.
National Research Council. (2007). Taking science to school: learning and teaching science in grades K-8. Washington: The National Academies Press.
National Science Resource Center. (1997). Science for all children: a guide to improving elementary science education in your school district. Washington: National Academies Press.
Neumann, K., Viering, T., Boone, W. J., & Fischer, H. E. (2013). Towards a learning progression of energy. Journal of Research in Science Teaching, 50(2), 162–188.
Plummer, J. D., & Maynard, L. (2014). Building a learning progression for celestial motion: an exploration of students’ reasoning about the seasons. Journal of Research in Science Teaching, 51(7), 902–929.
National Research Council. (2012). A framework for K–12 science education: practices, crosscutting concepts, and core ideas. Washington: National Academies Press.
Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. (Copenhagen, Danish Institute for Educational Research), expanded edition (1980) with foreword and afterword by B.D. Wright. Chicago: The University of Chicago Press.
Song, X. (2004). Textbook for chemistry of senior high school. Beijing: People’s Education Press.
Stevens, S. Y., Delgado, C., & Krajcik, J. S. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47(6), 687–715.
Tan, K.-C. D., Taber, K. S., Goh, N.-K., & Chia, L.-S. (2005). The ionisation energy diagnostic instrument: a two-tier multiple-choice instrument to determine high school students’ understanding of ionisation energy. Chemistry Education Research and Practice, 6(4), 180–197.
Tatsuoka, K. K. (1983). Rule space: an approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20(4), 345–354.
Tatsuoka, K. K. (1995). Architecture of knowledge structures and cognitive diagnosis: a statistical pattern recognition and classification approach. In P. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitively diagnostic assessment (pp. 327–359). New York: Routledge.
Tatsuoka, K. K. (2009). Cognitive assessment: an introduction to the rule space method. New York: Routledge.
Wang, L. (2004). Textbook for chemistry of senior high school. Jinan: Shandong Science & Technology Press.
Wang, Z. (2006). Textbook for chemistry of senior high school. Nanjing: Jiangsu Education Press.
Wilson, M. (2005). Constructing measures: an item response modeling approach. Mahwah: Lawrence Erlbaum Associates.
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The preparation of this paper was supported by grant 12JZD040 from the Key Projects of Philosophy and Social Sciences Research, Ministry of Education, People’s Republic of China.
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Fu Chen and Shanshan Zhang are the co-first authors.
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Chen, F., Zhang, S., Guo, Y. et al. Applying the Rule Space Model to Develop a Learning Progression for Thermochemistry. Res Sci Educ 47, 1357–1378 (2017). https://doi.org/10.1007/s11165-016-9553-7
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DOI: https://doi.org/10.1007/s11165-016-9553-7