Ecosystems around the globe are changing at an unprecedented rate as a result of human activities. These ecosystem responses are complex, non-linear, and driven by feedbacks across multiple temporal and spatial scales, necessitating new approaches for prediction. Consequently, scientists are increasingly using simulation models of ecological phenomena, based on large datasets of high-frequency sensor observations, to predict the effects of future change. Conducting this modeling, as well as interpreting model results, requires skills in data analysis, quantitative reasoning, and distributed computing. Despite their increasing importance, however, simulation modeling and computational skills are rarely taught in undergraduate classrooms, representing a major gap in training students to become quantitatively-literate citizens able to tackle complex environmental challenges. In response to this challenge, we have developed a suite of teaching modules as part of Project EDDIE (Environmental Data-Driven Inquiry and Exploration; projecteddie.org), a collaboration of ecologists and educators to integrate large dataset analysis into ecological laboratory activities. We taught a subset of the EDDIE modules, which use time series analysis and modeling to explore the effects of climate change on lakes, to undergraduate and graduate classes at three different universities and assessed student gains with pre- and post-module surveys.
Results/Conclusions
Assessment of both undergraduate and graduate classes found that participation in the module activities improved students’ understanding of ecological concepts, as well as their comfort level and interest in using simulation models. In particular, both undergraduate and graduate students responded very positively to developing hypotheses about climate change effects on lakes and then testing those hypotheses with numerical simulation modeling and distributed computing in the R environment. Across institutions, students consistently exhibited gains in quantitative literacy, with the largest gains experienced by students with the lowest self-reported comfort working with data prior to participating in the modules. Our experience suggests that high-frequency data and models can be a powerful teaching tool for ecological instruction, and that working with real data cements the “real world” application of basic ecological concepts.