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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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This content will become publicly available on June 10, 2026
Reinterpreting Lamarckianism as Generative Mechanistic Reasoning about Natural Selection
Students often express Lamarckian ideas—that changes acquired during an organism’s lifetime can be inherited—when reasoning about natural selection. Researchers have described this reasoning as arising from incorrect and unproductive misconceptions. Using the theoretical tools of resource theory and data from interviews with college students, we argue that an alternative explanation for students’ apparent Lamarckian reasoning is that they are seeking to provide mechanisms that can account for trait change. Unlike canonical populationlevel mechanisms, organism-level mechanisms are grounded in plausible changes to organismal forms, physiologies, or behaviors. We found that organism-level mechanistic reasoning arose in interviews when students recognized a need for a mechanistic explanation and shifted into an epistemological framing of in-the-moment knowledge construction. Rather than interpret Lamarckian ideas as misconceptions, we argue that they can be viewed as evidence of students' generative epistemological resources for seeking and providing mechanisms.
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- Award ID(s):
- 2300437
- PAR ID:
- 10637287
- Publisher / Repository:
- International Society of the Learning Sciences
- Date Published:
- Page Range / eLocation ID:
- 484 to 492
- Subject(s) / Keyword(s):
- knowledge-in-pieces mechanistic reasoning natural selection
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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