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Rebecca Jordan




My current and planned research initiatives focus on understanding how individuals reason with scientific data. In particular, I am seeking to understand how individuals generate and test explanations for complex phenomena.

I investigate this question working with several audiences (e.g., grade 6-12 students, undergraduate and graduate students, and the public involved in citizen science) to test general questions about causal reasoning with regard to individual decision-making:
I am currently testing the idea that when confronting complex issues and systems, a mechanistic or causal mental model is most associated with transfer of ideas to new contexts, which is a prime goal for learning.

I am also seeking to understand typical biases related to information, data, and data visualizations that individuals have when confronting novel environmental scenarios and how these biases interact with their use of evidence to support explanations.
Finally, I am testing the idea that by engaging citizen science participants or other learners in explicit and measurable learning endeavors, field scientists can not only gather essential data with respect to environmental interactions, but also can use learning as an environmental management tool.