Abstract

Data science skills (e.g., analyzing, modeling, and visualizing large datasets) are increasingly needed by undergraduates in environmental science. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline which integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed the efficacy of the modules with 596 students and 26 instructors over three years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.
Keywords: active learning, ecosystem modeling, National Ecological Observatory Network (NEON), training program, undergraduate education