An active learning tool for quantitative genetics instruction using R and shiny

Jeffrey L. Neyhart, Eric Watkins

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Basic quantitative and population genetics topics are typically taught in introductory plant breeding courses and are critical for success in upper-level study. Active learning, including simulations and games, may be useful for instruction of these concepts, which rely heavily on theory and may be more challenging for students. The statistical computing language R is now routinely used in the analysis of plant breeding experiments, but the command-line interface of the language may be unsuitable for an introductory course. Here we describe qgshiny (quantitative genetics in shiny), an interactive application for performing simulations to understand basic theory in quantitative and population genetics. The initial version of the application includes modules on three core topics in quantitative genetics: randomly mating populations, genetic variance, and response to selection. Students can specify parameters and initiate simulations to assess their impact on responses such as allele frequency, genetic variance, and genetic gain, which together can be used to reinforce more general learning objectives. Feedback collected from students after engaging with the application suggests this tool can have a positive impact on student learning. The application is bundled in an R package, qgshiny, which is available through the Comprehensive R Archive Network (CRAN), on GitHub (https://github.com/neyhartj/qgshiny), or interactively through the shinyapps.io platform (http://neyhartj.shinyapps.io/qgshiny).

Original languageEnglish (US)
Article numbere20026
JournalNatural Sciences Education
Volume49
Issue number1
DOIs
StatePublished - 2020

Bibliographical note

Funding Information:
We are grateful to the students of PLSC 3401 (Spring 2019), HORT/AGRO 4401 (Spring 2017), and AGRO 5021 (Fall 2017) at the University of Minnesota for testing the application and providing feedback. We thank James Anderson for accommodating the use of this application in the AGRO 5021 course. We would also like to thank J.D. Walker and Paul Baepler, both with the Center for Educational Innovation at the University of Minnesota, for their input and advice on evaluating the effectiveness of the application in the classroom.

Publisher Copyright:
© 2020 The Authors. Natural Sciences Education published by Wiley Periodicals, Inc. on behalf of American Society of Agronomy

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