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