Overview of the Macrosystems EDDIE
curriculum
The Macrosystems EDDIE ecological forecasting curriculum for
undergraduates comprises four standalone modules: Introduction to
Ecological Forecasting, Understanding Uncertainty in Ecological
Forecasts, Using Data to Improve Ecological Forecasts, and Using
Ecological Forecasts to Guide Decision-Making (Fig. 1). Like all EDDIE
modules, Macrosystems EDDIE ecological forecasting modules are designed
using the 5E (Engagement, Exploration, Explanation, Expansion,
Evaluation) instructional model (Bybee et al. 2006), which is
implemented through a scaffolded A-B-C structure (O’Connell et al.
2024). In all modules, Activity A Engages students and asks them to
Explore the module’s focal topic, Activity B further Explains and asks
students to Expand on that topic, and Activity C Evaluates students’
understanding of the topic (Carey et al. 2015, O’Reilly et al. 2017).
The three-part scaffolded structure also maximizes the adaptability of
Macrosystems EDDIE modules to various classroom contexts, as instructors
can choose whether to complete just Activity A, Activities A and B, or
all three activities in one to three-hour course periods. Each module
can be taught individually or instructors may choose to implement
multiple modules throughout their curriculum; example use cases are
detailed in the Course implementation section below.
The modules in the Macrosystems EDDIE ecological forecasting curriculum
are designed to both 1) introduce ecological forecasting concepts and 2)
develop data science skills (Fig. 1). To accomplish the first goal, each
module covers a foundational concept in ecological forecasting, and
students then apply the forecasting concept to a NEON lake site of their
choice. To develop data science skills, students use environmental data
collected by NEON (Keller et al. 2008, Goodman et al. 2015) as the basis
for their forecasting analyses. Working with NEON datasets requires
students to evaluate the quality of the data (e.g., gaps, outliers,
biases) and confront how inherent variability and error in environmental
datasets may affect their analyses. In addition, each module asks
students to interpret data visualized using various methods, ranging
from time series and scatterplots to probabilistic forecasts and
histograms. Finally, each module focuses on one or more foundational
quantitative skills in ecological forecasting, including building and
calibrating ecological models, generating forecasts, quantifying the
uncertainty associated with predictions, using new observations to
update forecast models, and designing forecast visualizations to
effectively communicate forecast output.
Macrosystems EDDIE modules include a comprehensive set of instruction
materials and are suitable for implementation in a variety of class
contexts (Fig. 1). All modules are delivered through an R Shiny
interface, where R code is used to render a website that students can
access in their internet browser (Chang et al. 2023). This permits a
user-friendly, point-and-click interface for introductory students and
aims to lower the intimidation barrier to ecological forecasting, as
students do not need to have any coding skills to generate a forecast.
For classrooms where gaining R coding skills is a learning objective,
two of the modules (Understanding Uncertainty in Ecological Forecasts
and Using Data to Improve Ecological Forecasts) have Rmarkdown
activities in addition to R Shiny materials. The Rmarkdown activities
enable students to access and modify the code underlying the R Shiny app
and complete module activities in the R programming environment (Xie et
al. 2018).
All Macrosystems EDDIE ecological forecasting materials are designed to
provide instructors with “just-in-time” training (sensu Novak
et al. 1999) on data science skills as they prepare to teach the modules
in their classrooms. In addition to the R Shiny application (and
RMarkdown file if applicable), each module includes an introductory
(~30 minute) Microsoft PowerPoint presentation with
slide notes; a Microsoft Word student handout with pre-class readings,
activities, and questions associated with the module; a comprehensive
instructor manual with learning objectives; detailed guidelines for
module implementation and answer keys; and a “quick start” guide to
the R Shiny applications. Notably, instructor manuals include strategies
for teaching and recommendations for implementing the modules across a
variety of course schedules (e.g., three, one-hour class sessions vs.
one, three-hour lab period) and modalities (e.g., virtual, face-to-face,
hybrid).
All module teaching materials are licensed under the CC BY-NC-SA 3.0
license allowing modification for classroom use and are published in the
Environmental Data Initiative repository (Moore et al. 2023a, 2024b,
Woelmer et al. 2023b, Lofton et al. 2024c), and all module code is
published in the Zenodo repository (Moore et al. 2023b, 2023c, 2024a,
Woelmer et al. 2022, Lofton et al. 2024a, 2024b). In addition, all
module code is maintained and updated at the Macrosystems EDDIE GitHub
organization (https://github.com/MacrosystemsEDDIE). We encourage and
welcome instructors and students to adapt and modify these materials for
their classrooms, projects, and research.