In a world increasingly shaped by the impacts of climate change, equipping students with the tools to analyze, interpret, and make informed decisions based on climate data has never been more critical. This article explores how integrating computer programming, specifically using Python as an illustrative tool, into project-based learning (PBL) transforms climate science education by enabling students to work with authentic datasets, uncover trends, and propose data-driven solutions. Rooted in the Next Generation Science Standards (NGSS), this approach emphasizes computational thinking, scientific inquiry, and real-world problem-solving. Students engage with authentic climate datasets, such as atmospheric CO2 levels and global temperature records, to investigate the link between greenhouse gas emissions and global warming. For instance, students engage with historical CO2 and temperature datasets, enabling them to identify trends and correlations, such as the link between rising emissions and global warming. They visualize climate trends, explore global disparities in carbon emissions, and model future climate scenarios using Python scripts in Google Colab. These hands-on activities foster critical thinking, environmental responsibility, and computational literacy while preparing students to contribute meaningfully to addressing one of the most pressing issues of their generation. By integrating technology into environmental science education, this approach empowers students to connect data-driven insights with meaningful climate action.
Pongprapan Pongsophon (Thu,) studied this question.