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Problem-Centered Data Science Education in the Agricultural and Biological Engineering Classroom: Analyzing Air Quality Index Data in R  Public Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  pp. 1-6
Authors:   Natalie Nelson
Keywords:   Active learning, problem-centered education, data acumen, agricultural and biological engineering, wildfires.


Students analyze real-world observations in R by writing code for data wrangling and visualization

Students are provided with opportunities to think critically about the spatial resolution of data

The exercise is presented in the context of a real-world case study involving wildfires

Abstract. In the presented lesson, students will wrangle and visualize U.S. Environmental Protection Agency Air Quality Index (AQI) data from North Carolina during a time period when a peat bog fire produced an expansive smoke plume that impacted large swaths of the state. The lesson includes a lecture and exercise, with the exercise requiring students to write code in R, an open-source statistical software environment. Lesson learning objectives include (1) Describe the goals of an exploratory data analysis, (2) Apply an exploratory data analysis in R, (3) Explain what the AQI is and how it is calculated, and (4) Assess how the spatial resolution of data influences conclusions. Materials provided with the lesson include lecture slides, data, an R script, and a recorded lesson synopsis.

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(Course Material Part A Download - 30.3 MB Zip file)
(Course Material Part B Download - 6.6 MB Zip file)

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