Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6390
Title: Interpreting Graphs to Distinguish Factors That Impact Climate Change
Authors: McBride, Elizabeth
Linn, Marcia
Vitale, Jonathan
Keywords: Design
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: McBride, E., Linn, M., & Vitale, J. (2020). Interpreting Graphs to Distinguish Factors That Impact Climate Change. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 3 (pp. 1653-1656). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: Scientists use models and graphs to distinguish among factors that impact a phenomenon (for example, the impact of CO2 accumulation on climate change) and factors that do not impact the phenomenon (for example the role of ozone depletion on climate change). In this paper, we compare two forms of exploration of time series line graphs: plan and typical. In the plan condition, students plan an experiment with a model by graphing the level of a system parameter (e.g., concentration of greenhouse gases) and the predicted response of an outcome variable (e.g., temperature). They then run the model to observe the accuracy of their predictions. In the typical condition, students run the simulation immediately and adjust the parameter level as they see fit. Students produced more informative experiments in the plan condition than the typical condition. Students in the plan condition made inferences by comparing their prediction to the outcome.
URI: https://doi.dx.org/10.22318/icls2020.1653
https://repository.isls.org//handle/1/6390
Appears in Collections:ICLS 2020

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