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Contributed Talk

Session: : Education: Research And Assessment

COS 196-5 - Macrosystems EDDIE: A modular curriculum to teach undergraduates ecological forecasting using R Shiny applications improves student confidence, familiarity, and conceptual understanding

Wednesday, August 9, 2023
2:30 PM – 2:45 PM PDT
Location: D140
Abstract: Ecological forecasting is an emerging approach in ecology which provides an estimate of future ecological conditions with uncertainty, enabling preemptive management of important ecosystem services. Ecological forecasts are a powerful test of the scientific method because ecologists make a hypothesis of how an ecosystem works; embed the hypothesis in a model; use the model to make a forecast; and then assess the accuracy of the forecast, which indicates whether the hypothesis and forecast model are supported or need to be updated and improved. Consequently, ecological forecasting can generate new ecological understanding via a hands-on, engaging application of the scientific method. Despite the importance of ecological forecasting, to date there have been few opportunities for undergraduate training in this emerging subdiscipline. Our team has developed the first formalized curriculum that teaches foundational concepts of ecological forecasting to undergraduates as part of the Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration; MacrosystemsEDDIE.org) program. We use large, publicly available datasets from the National Ecological Observatory Network (NEON) to engage students in ecology and improve their quantitative reasoning via a series of four three-hour teaching modules embedded in R Shiny applications. Each module can be taught alone or in sequence with other modules; is adaptable for use in introductory, intermediate, and advanced courses; and can be taught face-to-face or virtually. In this study, we examined the efficacy of the Macrosystems EDDIE curriculum in teaching students ecological forecasting concepts by analyzing student responses on pre-module and post-module assessments. Specifically, we compared student growth in courses that taught a single module vs. courses that taught multiple modules to assess differences in student learning of ecological forecasting concepts. We found that student confidence and familiarity with ecological forecasting increased after completion of a single module, and this effect was greater in courses that integrated multiple modules. In addition, while the greatest gains in student understanding of key forecasting concepts occurred after completion of multiple modules, completion of a single module was effective in increasing students’ ability to correctly answer questions about complex, quantitative forecasting concepts, such as quantification of forecast uncertainty and assimilation of data into models to update forecast output. In conclusion, our findings indicate that a modular approach to teaching ecological forecasting using R Shiny applications can improve undergraduate quantitative reasoning and understanding of ecological forecasting concepts.