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Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis

  • Yizhu Gao
  • Xiaoming Zhai
  • Björn Andersson
  • Pingfei Zeng
  • Tao XinEmail author
Article
  • 320 Downloads

Abstract

We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume, and Relative density on the basis of the prior level. Then, students gradually master Archimedes’ theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative–Quantitative–Integrative explanatory model.

Keywords

Buoyancy Learning progression Latent class analysis Rule space model Conceptual change 

References

  1. Alonzo, A. C., & Gearhart, M. (2006). Considering learning progressions from a classroom assessment perspective. Measurement: Interdisciplinary Research and Perspectives, 14, 99–104.Google Scholar
  2. Alonzo, A. C., & Gotwals, A. W. (2012). Learning progressions in science: current challenges and future directions. In A. Alonzo & A. Gotwals (Eds.), Learning progressions in science (pp. 257–292). The Netherlands: Sense Publishers.CrossRefGoogle Scholar
  3. Alonzo, A. C., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389–421.CrossRefGoogle Scholar
  4. Chen, Y. (2015). Cognitive diagnosis and remedial teaching on junior middle school students’ concepts of buoyancy. Unpublished master dissertation. Zhe Jing: Zhe Jing Normal University.Google Scholar
  5. Chen, F., Zhang, S. S., Guo, Y. F., & Xin, T. (2016). Applying the rule space model to develop a learning progression for thermochemistry. Research in Science Education, 47, 1357–1378.  https://doi.org/10.1007/s11165-016-9553-7.CrossRefGoogle Scholar
  6. Chen, F., Yan, Y., & Xin, T. (2017). Developing a learning progression for number sense based on the rule space model in China. Educational Psychology, 37, 1–17.CrossRefGoogle Scholar
  7. Chi, M. T. H. (1992). Conceptual change across ontological categories: examples from learning and discovery in science. In R. Giere (Ed.), Cognitive models of science: Minnesota studies in the philosophy of science (pp. 129–186). Minneapolis: University of Minnesota Press.Google Scholar
  8. Chi, M. T. H., Slotta, J. D., & De Leeuw, N. (1994). From things to processes: a theory of conceptual change for learning science concepts. Learning and Instruction, 4(1), 27–43.CrossRefGoogle Scholar
  9. Chi, M. T. H., Roscoe, R. D., Disessa, A. A., Vosniadou, S., Ivarsson, J., Schoultz, J., … Pintrich, P. R. (2003). Reconsidering conceptual change: issues in theory and practice. Science Education, 87(6), 913–916.Google Scholar
  10. Clement, J. (2008). The role of explanatory models in teaching for conceptual change. In S. Vos-niadou (Ed.), International handbook of research on conceptual change (pp. 417–452). New York: Routledge.Google Scholar
  11. diSessa, A. A. (1988). Knowledge in pieces. In G. Forman & P. B. Pufall (Eds.), Constructivism in the computer age (pp. 49–70). Hillsdale: Erlbaum.Google Scholar
  12. diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.CrossRefGoogle Scholar
  13. diSessa, A. A. (2007). Changing conceptual change. Human Development, 50(1), 39–46.CrossRefGoogle Scholar
  14. diSessa, A. A. (2013). A bird’s-eye view of the “pieces” vs. “coherence” controversy (from the “pieces” side of the fence). In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 31–48). New York: Routledge.Google Scholar
  15. diSessa, A. A., Gillespie, N. M., & Esterly, J. B. (2004). Coherence versus fragmentation in the development of the concept of force. Cognitive Science, 28(6), 843–900.CrossRefGoogle Scholar
  16. Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (2007). Taking science to school: learning and teaching science in grades K-8. Washington: National Academics.Google Scholar
  17. Ebel, R. L. (1965). Measuring educational achievement. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  18. Fulmer, G. W., Liang, L. L., & Liu, X. (2014). Applying a force and motion learning progression over an extended time span using the force concept inventory. International Journal of Science Education, 36(17), 2918–2936.CrossRefGoogle Scholar
  19. Guzzetti, B. J. (1993). Critical review of qualitative research on conceptual change from science education. Paper presented at the annual meeting of the National Reading Conference, Charleston, SC.Google Scholar
  20. Hardy, I., Jonen, A., Möller, K., & Stern, E. (2006). Effects of instructional support within constructivist learning environments for elementary school students’ understanding of “floating and sinking”. Journal of Educational Psychology, 98(2), 307–326.CrossRefGoogle Scholar
  21. Havu-Nuutinen, S. (2005). Examining young children’s conceptual change process in floating and sinking from a social constructivist perspective. International Journal of Science Education, 27(3), 259–279.CrossRefGoogle Scholar
  22. Joung, Y. J. (2009). Children’s typically-perceived-situations of floating and sinking. International Journal of Science Education, 31(1), 101–127.CrossRefGoogle Scholar
  23. Kennedy, C. A., & Wilson, M. (2007). Using progress variables to interpret student achievement and progress. Berkeley: University of California, BEAR Center.Google Scholar
  24. Li, F., Yu, N., & Xin, T. (2009). Development of diagnostic math test for grade 4 and grade 5 based on the rule space model. Psychological Development and Education, 3, 113–118.Google Scholar
  25. Loverude, M. E., Kautz, C. H., & Heron, P. R. L. (2003). Helping students develop an understanding of Archimedes’ principle. I. Research on student understanding. American Journal of Physics, 71(11), 1178–1187.CrossRefGoogle Scholar
  26. Maclin, D., Grosslight, L., & Davis, H. (1997). Teaching for understanding: a study of students’ pre-instruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction, 15(3), 317–393.CrossRefGoogle Scholar
  27. Magnusson, S. J., Templin, M., & Boyle, R. A. (1997). Dynamic science assessment: a new approach for investigating conceptual change. Journal of the Learning Sciences, 6(1), 91–142.CrossRefGoogle Scholar
  28. McCloskey, M. (1983a). Intuitive physics. Scientific American, 248(4), 122–130.CrossRefGoogle Scholar
  29. McCloskey, M. (1983b). Naive theories of motion. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 299–324). Hillsdale: Erlbaum.Google Scholar
  30. Mills, R., Tomas, L., & Lewthwaite, B. (2016). Learning in earth and space science: a review of conceptual change instructional approaches. International Journal of Science Education, 38(5), 1–24.CrossRefGoogle Scholar
  31. Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles: Muthen & Muthen.Google Scholar
  32. National Research Council. (2001). Knowing what students know. Washington: National Academy Press.Google Scholar
  33. National Research Council. (2007). In R. A. Duschl, H. A. Schweingruber, & A. W. Shouse (Eds.), Taking science to school; learning and teaching science in grades K-8. Washington DC: The National Academic Press.Google Scholar
  34. 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.CrossRefGoogle Scholar
  35. Norman, D. A., & Rumelhart, D. E. (1981). The LNR approach to human information processing. Cognition, 10, 235–240.CrossRefGoogle Scholar
  36. Piaget, J. (1930). The child’s conception of causality. London: Kegan Paul.Google Scholar
  37. Piaget, J. (1969). The child’s conception of physical causality. Totowa: Littlefield.Google Scholar
  38. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: toward a theory of conceptual change. Science Education, 66(2), 211–227.CrossRefGoogle Scholar
  39. Şahin, Ç., & Çepni, S. (2012). Effect of different teaching methods and techniques embedded in the 5E instructional model on students’ learning about buoyancy force. Eurasian Journal of Physics and Chemistry Education, 4(2), 97–127.Google Scholar
  40. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 15–18.CrossRefGoogle Scholar
  41. Shao, J. X., & Liu, M. (2017). The design and improvement of buoyancy experiment teaching. Teaching & Administration, 31, 63–65.Google Scholar
  42. She, H. C. (2002). Concepts of a higher hierarchical level require more dual situated learning events for conceptual change: a study of air pressure and buoyancy. International Journal of Science Education, 24(9), 981–996.CrossRefGoogle Scholar
  43. She, H. C. (2005). Enhancing eighth grade students’ learning of buoyancy: the interaction of teachers’ instructional approach and students’ learning preference styles. International Journal of Science and Mathematics Education, 3(4), 609–624.CrossRefGoogle Scholar
  44. Smith, C. L., Carey, S., & Wiser, M. (1985). On differentiation: a case study of the development of the concepts of size, weight, and density. Cognition, 21(3), 177–237.CrossRefGoogle Scholar
  45. Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). Implications of research on children’s learning for standards and assessment: a proposed learning progression for matter and the atomic-molecular theory. Measurement: Interdisciplinary Research and Perspectives, 4, 1–98.Google Scholar
  46. Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 610–631.CrossRefGoogle Scholar
  47. Soto Lombana, C., Otero, J., & Sanjosé López, V. (2005). A review of conceptual change research in science education. Revista De Educación En Ciencias, 6(1), 5–8.Google Scholar
  48. Steedle, J. T., & Shavelson, R. J. (2009). Supporting valid interpretations of learning progression level diagnoses. Journal of Research in Science Teaching, 46(6), 699–715.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. Taber, K. S. (2010). Understanding the nature and processes of conceptual change: an essay review. Education Review, 14(1), 1–17.Google Scholar
  51. Talanquer, V. (2009). On cognitive constraints and learning progressions: the case of “structure of matter”. International Journal of Science Education, 31(15), 2123–2136.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. Ünal, S., & Costu, B. (2005). Problematic issue for students: does it sink or float? Asia-Pacific Forum on Science Learning and Teaching, 6(1), 1.Google Scholar
  54. Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4(1), 45–69.CrossRefGoogle Scholar
  55. Vosniadou, S., & Brewer, W. F. (1987). Theories of knowledge restructuring in development. Review of Educational Research, 57(1), 51–67.CrossRefGoogle Scholar
  56. Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: a study of conceptual change in childhood. Cognitive Psychology, 24(4), 535–585.CrossRefGoogle Scholar
  57. Wilson, M. (2005). Constructing measures: an item response modeling approach. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  58. Wilson, M. (2009). Measuring progressions: assessment structures underlying a learning progression. Journal of Research in Science Teaching, 46(6), 716–730.CrossRefGoogle Scholar
  59. Wilson, M. R., & Bertenthal, M. W. (2005). Systems for state science assessment. Washington DC: The National Academic Press.Google Scholar
  60. Yin, Y. (2005). The influence of formative assessments on student motivation, achievement, and conceptual change. Unpublished doctoral dissertation. Stanford: Stanford University.Google Scholar
  61. Yin, Y., Shavelson, R. J., Ayala, C. C., Ruiz-Primo, M. A., Brandon, P. R., Furtak, E. M., … Young, D. B. (2008a). On the impact of formative assessment on student motivation, achievement, and conceptual change. Applied Measurement in Education, 21(4), 335–359.Google Scholar
  62. Yin, Y., Tomita, M. K., & Shavelson, R. J. (2008b). Diagnosing and dealing with student misconceptions: floating and sinking. Science Scope, 31, 34–39.Google Scholar
  63. Yin, Y., Tomita, M. K., & Shavelson, R. J. (2014). Using formal embedded formative assessments aligned with a short-term learning progression to promote conceptual change and achievement in science. International Journal of Science Education, 36(4), 531–552.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Yizhu Gao
    • 1
  • Xiaoming Zhai
    • 2
  • Björn Andersson
    • 3
  • Pingfei Zeng
    • 4
  • Tao Xin
    • 1
    Email author
  1. 1.Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
  2. 2.Graduate School of EducationStanford UniversityStanfordUSA
  3. 3.Centre for Educational MeasurementUniversity of OsloOsloNorway
  4. 4.College of Teacher EducationZhejiang Normal UniversityJinghua CityChina

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