The Greenhouse Effect: Using Student-Generated Agricultural Data to Warm Up Students for Data-Based Decision Making

This article describes a collaborative project across multiple institutions to compare two versions of a regression activity: (a) a website version that collected student data through an online simulation with corresponding apps for visualization and (b) a traditional paper handout version that had a static dataset. This allowed us to evaluate the differences in student learning and attitudes when students have a stronger connection with the data through an interactive—and hopefully fun—data-collection process than they do when they are given a worksheet with a pre-collected dataset. While student and instructor responses indicated they liked the activity in both formats, the PreTest and PostTest results showed evidence of a higher gain in knowledge with the website version. Finally, we discuss how the website version connects with pedagogical strategies for the development of modern statistics and data science courses.

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Publication Details

Journal
Journal of Statistics and Data Science Education
Published
2024-12-13
DOI
https://doi.org/10.1080/26939169.2024.2441672
Primary Topic
Statistics Education and Methodologies
Type
article
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article

The Greenhouse Effect: Using Student-Generated Agricultural Data to Warm Up Students for Data-Based Decision Making

Tyler George, Scott Crawford, Abhishek Chakraborty, Lisa Kay et al.
Journal of Statistics and Data Science Education
Statistics Education and Methodologies
article

The Greenhouse Effect: Using Student-Generated Agricultural Data to Warm Up Students for Data-Based Decision Making

Tyler George, Scott Crawford, Abhishek Chakraborty, Lisa Kay, Ginger Holmes Rowell, Dennis K. Pearl, Shonda Kuiper, Lawrence M. Lesser, Annemarie Olsen
article en

Abstract

This article describes a collaborative project across multiple institutions to compare two versions of a regression activity: (a) a website version that collected student data through an online simulation with corresponding apps for visualization and (b) a traditional paper handout version that had a static dataset. This allowed us to evaluate the differences in student learning and attitudes when students have a stronger connection with the data through an interactive—and hopefully fun—data-collection process than they do when they are given a worksheet with a pre-collected dataset. While student and instructor responses indicated they liked the activity in both formats, the PreTest and PostTest results showed evidence of a higher gain in knowledge with the website version. Finally, we discuss how the website version connects with pedagogical strategies for the development of modern statistics and data science courses.

Journal of Statistics and Data Science EducationVol. 33(2)
Eastern Kentucky University (US), Pennsylvania State University (US), Lawrence University (US), The University of Texas at El Paso (US), Middle Tennessee State University (US), Grinnell College (US), Cornell University (US), Mitchell Institute (US), Texas A&M University (US)
Zero hunger
Openalex Percentile: Top 77%
Statistics Education and Methodologies
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