This paper explores how MoDa, an integrated computational modeling and data environment, enabled students to express their ideas about diffusion and shift them toward canonical ideas. Drawing on data from an 8-day unit with two 6th-grade science classes, we analyze students' utterances in presentations, drawings, and written responses to document their diverse ideas about diffusion We present three case studies to illustrate how engaging with computational modeling in MoDa and the unit around it enabled students to shift from non-canonical ideas towards more canonical explanations of diffusion. In particular, we identify three factors that helped in shifting students’ ideas: the availability of code blocks to represent a diverse range of ideas including non-canonical ones, consistent access to video data of the phenomenon, and model presentations to the whole class. The paper illustrates how a computational modeling tool and curriculum can make students' diverse ideas visible and shift them toward canonical explanations.
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Right but Wrong: The Independence of Mechanistic Reasoning and Canonical Understanding in Studying Diffusion
This study explores how the interplay between data analysis and model design shifts 6th-grade students' understanding of diffusion from simple to sophisticated mechanistic reasoning and from non-canonical to canonical ideas about diffusion. Using mixed-methods qualitative analysis, we determine students' mechanistic reasoning and ideas about diffusion at five different points in a curricular sequence using a new tool for computational modeling called MoDa. With this data, we present a framework for the relationship between students' developing mechanistic reasoning and their canonical understanding, suggesting that they develop independently. Further, we illustrate how the computational modeling environment, MoDa, used in this study pushed students' mechanistic reasoning toward sophistication. Moreover, in allowing them to explore non-canonical mechanisms, MoDa supported their convergence on canonical scientific ideas about diffusion.
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- Award ID(s):
- 2010413
- PAR ID:
- 10427849
- Date Published:
- Journal Name:
- Annual International Conference of the National Association for Research in Science Teaching (NARST)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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