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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
Article

Abstract

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.

Keywords

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.

Notes

Acknowledgments

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.

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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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