Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety
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Author
Meixner, T.
Ciancarelli, B.
Farrell, E.P.
García, S.D.
Josek, T.
Kelly, M.M.
Meister, P.
Soule, D.
Darner, R.
Affiliation
Department of Hydrology and Atmospheric Sciences, University of ArizonaIssue Date
2023-03-29
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RoutledgeCitation
Meixner, T., Ciancarelli, B., Farrell, E. P., García, D. S., Josek, T., Kelly, M. M., … Darner, R. (2023). Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety. Journal of Geoscience Education, 1–11. https://doi.org/10.1080/10899995.2023.2193810Journal
Journal of Geoscience EducationRights
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC . This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (https://creativecommons.org/licenses/by-nc-nd/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Learning in asynchronous online environments has gained importance over the last several decades, and educational environment shifts from the COVID-19 pandemic appear to have increased this need. Science educators and students need information about which approaches work in the asynchronous environment where informal feedback tends to be reduced, compared to other teaching modalities. In this study, we asynchronously implemented a learning module across 5 institutions that guided students (N = 199) from prescriptive data analysis through guided inquiry and eventually to open inquiry. The module focuses on the science behind climate change. Students work with the same authentic data sets used by professional scientists to examine geologic history and causes of climate change. By analyzing contemporary atmospheric carbon dioxide and temperature data and then using the 800,000-year record available from the Vostok ice core proxy record of atmospheric properties, students identify the causes of climate change and discover the unprecedented nature of recent atmospheric changes. Using a pre/post-module assessment, we demonstrate improvement in students’ understanding of climate change processes and statistical methods used to analyze data. However, there was no evidence that the module develops students’ scientific reasoning about the relationship between causation and correlation. Students maintained that correlation is not causation, even when a robust causal mechanism (i.e., the greenhouse effect) explains the link between atmospheric carbon dioxide and temperature. Finally, our analysis indicated that generally, anxiety about climate change was reduced during the module, such that students become less anxious about the climate change the more they learn about it. However, science-denying students experienced much higher anxiety about climate change than students who accepted the scientific consensus about climate change. Climate science-dissenting students were so few in this study that a statistical comparison was not possible, but this intriguing finding warrants further investigation of the role of anxiety in science denial. Mainly, this study demonstrates how asynchronous online learning environments can indeed support the achievement of learning objectives related to conducting authentic science, such as increasing understanding of climate change and statistical concepts, all while not provoking anxiety about climate change. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.Note
Open access articleISSN
1089-9995Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1080/10899995.2023.2193810
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Except where otherwise noted, this item's license is described as © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC . This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (https://creativecommons.org/licenses/by-nc-nd/4.0/).