GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 253-12
Presentation Time: 10:45 AM


BHATTACHARYA, Devarati, Natural Resources, University of Nebraska- Lincoln, 518 South Hardin Hall, 3310 Holdrege Street, Lincoln, NE 68583, FORBES, Cory, School of Natural Resources, University of Nebraska-Lincoln, 3310 Holdrege St, Lincoln, NE 68583, CHANDLER, Mark A., Center for Climate Systems Research, Columbia University, NASA/GISS, 2880 Broadway, New York, NY 10025 and CARROLL STEWARD, Kimberly, Natural Resources, University of Nebraska- Lincoln, Lincoln, NE 68503

The adoption of the Next Generation Science Standards (NGSS; NGSS Lead States, 2013) has created a momentum for climate literacy in science education. So far, instruction of global climate change (GCC) has focused on developing students’ understanding about the science inherent to the phenomenon using data-based inquiries, where students observe and explain long-term trends in the Earth’s climate. However, comprehending carbon imbalancesover vast spatial and temporal scales and the impacts of increased carbon dioxide remains challenging. Model-based teaching helps students in understanding such abstract concepts. In this study, we analyze 9th-grade geoscience students’ reasoning from data collected as part of a NSF-funded Project-CliMES (Climate Literacy and Modeling Epistemology of Science). A sequence of eight lessons were implemented in ten classrooms at four secondary school sites in one partner school district. Using student task data and interviews collected through two parallel data-driven activities, a non-model-based (N=90) and a model-based investigation (N=80), we asked, “Howdo students use models to reason about data-driven climate-related phenomena?”Data were analyzed using a mixed method approach grounded in Evidence-Based Reasoning Framework (Brown et al., 2010). We observed that both model- and non-model-based activities engaged students in an investigation of “big data”. However, EzGCM facilitated a more systematic process, where students analyzed data quantitatively and qualitatively through simulations, graphs, and visualizations. They compared IPCC predicted scenarios with a control simulation (a reference period) through multiple modalities. This allowed for a robust analysis of temperature anomalies and comprehending the global and spatial extent of increase in surface temperature and carbon dioxide. Subsequently, students were able to establish a simple causal link between “surface temperature”, time and CO2. This study establishes that model-based teaching can effectively engage learners in methods specific to climate science. These findings have significant implications for curriculum design and implementation of model-based techniques for teaching and learning about the Earth’s climate and GCC.