Science & Education

, Volume 24, Issue 7–8, pp 957–981 | Cite as

Exploring the Effect of Embedded Scaffolding Within Curricular Tasks on Third-Grade Students’ Model-Based Explanations about Hydrologic Cycling

  • Laura ZangoriEmail author
  • Cory T. Forbes
  • Christina V. Schwarz


Opportunities to generate model-based explanations are crucial for elementary students, yet are rarely foregrounded in elementary science learning environments despite evidence that early learners can reason from models when provided with scaffolding. We used a quasi-experimental research design to investigate the comparative impact of a scaffold test condition consisting of embedded physical scaffolds within a curricular modeling task on third-grade (age 8–9) students’ formulation of model-based explanations for the water cycle. This condition was contrasted to the control condition where third-grade students used a curricular modeling task with no embedded physical scaffolds. Students from each condition (n scaffold = 60; n unscaffold = 56) generated models of the water cycle before and after completion of a 10-week water unit. Results from quantitative analyses suggest that students in the scaffolded condition represented and linked more subsurface water process sequences with surface water process sequences than did students in the unscaffolded condition. However, results of qualitative analyses indicate that students in the scaffolded condition were less likely to build upon these process sequences to generate model-based explanations and experienced difficulties understanding their models as abstracted representations rather than recreations of real-world phenomena. We conclude that embedded curricular scaffolds may support students to consider non-observable components of the water cycle but, alone, may be insufficient for generation of model-based explanations about subsurface water movement.


Modeling Task Elementary Student Water Cycle Epistemic Feature Water Cycle Process 
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.



This material is based upon work supported by the National Science Foundation (DRL-1443223 and DRL-1020316). We appreciate the interest and cooperation of Tina Vo, Christopher Soldat, Julie Foltz, Sheila Barron, and the students and teachers who made this research possible.


  1. Adúriz-Bravo, A. (2013). A ‘semantic’ view of scientific models for science education. Science & Education, 22(7), 1593–1611.CrossRefGoogle Scholar
  2. Baek, H., Schwarz, C., Chen, J., Hokayem, H., & Zhan, L. (2011). Engaging elementary students in scientific modeling: The MoDeLS fifth-grade approach and findings. In M. S. Khine & I. M. Saleh (Eds.), Models and modeling (pp. 195–218). Netherlands: Springer.CrossRefGoogle Scholar
  3. Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Science, 36, 421–441.CrossRefGoogle Scholar
  4. Berland, L., Schwarz, C., Krist, C., Kenyon, L., Lo, A., & Reiser, B. (2015). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching.Google Scholar
  5. Besson, U. (2010). Calculating and understanding: Formal models and causal explanations in science, common reasoning and physics teaching. Science & Education, 19(3), 225–257.CrossRefGoogle Scholar
  6. Braaten, M., & Windschitl, M. (2011). Working toward a stronger conceptualization of scientific explanation for science education. Science Education, 95(4), 639–669.CrossRefGoogle Scholar
  7. Covitt, B. A., Gunckel, K. L., & Anderson, C. W. (2009). Students’ developing understanding of water in environmental systems. The Journal of Environmental Education, 40(3), 37–51.CrossRefGoogle Scholar
  8. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.Google Scholar
  9. Develaki, M. (2007). The model-based view of scientific theories and the structuring of school science programmes. Science & Education, 16(7–8), 725–749.CrossRefGoogle Scholar
  10. Driver, R., Asoko, H., Leach, J., Scott, P., & Mortimer, E. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(5), 5–12.CrossRefGoogle Scholar
  11. Forbes, C. T., Zangori, L., & Schwartz, C. (2015). Empirical validation of integrated learning performances for hydrologic phenomena: 3rd-grade students’ model-driven explanation-construction. Journal of Research in Science Teaching.Google Scholar
  12. FOSS. (2005). Teacher guide: Water. Berkeley, CA: Delta Education Inc.Google Scholar
  13. Gall, M. D., Gall, J. P., & Borg, W. R. (2003). Educational research: An introduction. New York, NY: Pearson Education Inc.Google Scholar
  14. Gilbert, J., Boulter, C., & Rutherford, M. (2000). Explanations with models in science education. In J. Gilber & C. Boulter (Eds.), Developing models in science education (pp. 193–208). Norwell, MA: Kluwer.CrossRefGoogle Scholar
  15. Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(S3), S342–S353.CrossRefGoogle Scholar
  16. Gunckel, K. L., Covitt, B. A., Salinas, I., & Anderson, C. W. (2012). A learning progression for water in socio-ecological systems. Journal of Research in Science Teaching, 49(7), 843–868.Google Scholar
  17. Halloun, I. (2007). Mediated modeling in science education. Science & Education, 16(7–8), 653–697.CrossRefGoogle Scholar
  18. Henriques, L. (2002). Children’s ideas about weather: A review of the literature. School Science and Mathematics, 102(5), 202–215.CrossRefGoogle Scholar
  19. Hmelo-Silver, C. E., & Azevdeo, R. (2009). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15(1), 53–61.CrossRefGoogle Scholar
  20. Kleinbaum, D. G., Kupper, L. L., Muller, K. E., & Nizam, A. (1998). Applied regression analysis and other multivariable methods. New York, NY: Duxbury Press.Google Scholar
  21. Kyriakopoulou, N., & Vosniadou, S. (2014). Using theory of mind to promote conceptual change in science. Science & Education, 23(7), 1447–1462.CrossRefGoogle Scholar
  22. Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–387). New York, NY: Cambridge University Press.Google Scholar
  23. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.CrossRefGoogle Scholar
  24. Manz, E. (2012). Understanding the codevelopment of modeling practice and ecological knowledge. Science Education, 96(6), 1071–1105.CrossRefGoogle Scholar
  25. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: National Academies Press.Google Scholar
  26. Patton, M. Q. (2001). Qualitative research and evaluation methods. Thousand Oaks, CA: Sage.Google Scholar
  27. Puntambekar, S., & Kolodner, J. L. (2005). Toward implementing distributed scaffolding: Helping students learn science from design. Journal of Research in Science Teaching, 42(2), 185–217.Google Scholar
  28. Salmon, W. (1998). Causality and explanation. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  29. Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88(3), 345–372.CrossRefGoogle Scholar
  30. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Acher, A., Fortus, D., et al. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learner. Journal of Research in Science Teaching, 46(6), 632–654.Google Scholar
  31. Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.CrossRefGoogle Scholar
  32. Scientific Practices Research Group. (n.d.). Epistemic considerations rubrics. (Unpublished documents). Evanston IL: Northwestern University. Google Scholar
  33. Sensevy, G., Tiberghien, A., Santini, J., Laubé, S., & Griggs, P. (2008). An epistemological approach to modeling: Cases studies and implications for science teaching. Science Education, 92(3), 424–446.CrossRefGoogle Scholar
  34. Sherin, B., Reiser, B. J., & Edelson, D. (2004). Scaffolding analysis: Extending the scaffolding metaphor to learning artifacts. The Journal of Learning Sciences, 13(3), 387–421.CrossRefGoogle Scholar
  35. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
  36. Zangori, L., Forbes, C. T., & Schwarz, C. (2014). Investigating the effect of curricular scaffolds on 3rd-grade students’ model-based explanations for hydrologic cycling. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O’Connor, T. Lee, & L. D’Amico (Eds.), Learning and becoming in practice: The International Conference of the Learning Sciences (ICLS) 2014 (Vol. 2, pp. 942–946). Boulder, CO: International Society of the Learning Sciences.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Laura Zangori
    • 1
    Email author
  • Cory T. Forbes
    • 2
    • 3
  • Christina V. Schwarz
    • 4
  1. 1.Department of Learning, Teaching, and Curriculum, College of EducationUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.School of Natural ResourcesUniversity of Nebraska-LincolnLincolnUSA
  3. 3.Department of Teaching, Learning, and Teacher Education, College of Education and Human SciencesUniversity of Nebraska-LincolnLincolnUSA
  4. 4.Department of Teacher Education, College of EducationMichigan State UniversityEast LansingUSA

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