Description

Project EDDIE Environmental Data-Driven Inquiry & Exploration) is a community effort aimed at developing teaching resources and instructors that address quantitative reasoning and scientific concepts using open inquiry of publicly available data. Project EDDIE modules are designed with an A-B-C structure to make them flexible and adaptable to a range of student levels and course structures.

Successful students will produce a series of figures in R representing different vegetation and landscape variables, then compare these via scatterplots and correlation. They will do this first with a single data set, then select a different National Ecological Observation Network site and create a narrated presentation comparing the two sites and offering potential reason for differences in correlations/vegetation health. Students will understand how to quantify the influence of topography on vegetation across multiple biomes. Students will practice statistical methods (e.g. correlation) and develop higher-order thinking skills including hypothesis generation and synthesis. The final goal is for students to interpret the large scale spatial patterns of correlations, attributing their variation to geographical drivers like latitude, biome, or geologic history.

Modifications to the original resource includes:

• Implementing this project through a cloud based platform for easier use in remote-based instruction scenarios (synchronous or asynchronous)
• Additions to the worksheet to improve clarity in questions asked of students 
• Formation of a modified "part C" or culminating project that is an individual assignment and provides a more extensive means for the student to evaluate and compare ecological regions. This includes instructions for students to produce a 3-5 minute narrated presentation of their findings, further increasing the application of higher-order thinking skills. 
• Additional details are provided in the uploaded implementation plan document

Overall Learning Goals are for students to:

• Test whether plant growth (greenness and height) is driven more by elevation, slope, or aspect.
• Analyze spatial raster data in R, moving between making maps and doing non-spatial statistical tests.
• Consider macroscale (continental scale) patterns of relationships between topography and vegetation.
• Assess two ecologically different regions and determine potential processes and conditions affecting each region

Full module details

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