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.
ABSTRACT
The COVID-19 pandemic introduced many challenges for research scientists: reduction of lab and field observation collection and in-person meetings. These new constraints forced researchers to remote work and virtual networking, dramatically influencing scientific inquiry. Such challenges are compounded for those in early stages of their career, where data collection and networking are vital to be seen as productive. However, during this trying time of remote work, we, as a collective of early-career oceanographers, were actively developing and improving on an already-existent hybrid community of practice. Through our experiences, we believe this type of framework can enhance virtual collaboration to the point that it outlasts the pandemic and helps create new synergies that will diversify and enhance scientific inquiry within the ocean science community. We describe a hybrid community of practice and an example workflow that models effective collaboration. We have found that three components to this model are necessary for effective collaboration, inspiration, and communication: 1) openly accessible data, 2) software, computational, and professional-development resources, and 3) a team science approach. In our experience, both the in-person and remote aspects of the model are important. In person collaboration is key to expanding the community of practice and invigorating those already within the community. Remote collaboration has been critical for effective collaborations between in-person activities and has proven to maximize outputs during in-person collaborations. While the three components of this model are not new to the scientific community, we believe that utilizing them strategically post-pandemic will diversify and expand scientific collaboration in oceanography.