An active learning tool for quantitative genetics instruction using R and shiny. Issue 1 (19th August 2020)
- Record Type:
- Journal Article
- Title:
- An active learning tool for quantitative genetics instruction using R and shiny. Issue 1 (19th August 2020)
- Main Title:
- An active learning tool for quantitative genetics instruction using R and shiny
- Authors:
- Neyhart, Jeffrey L.
Watkins, Eric - Abstract:
- 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 (q uantitative g enetics 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 platformAbstract: 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 (q uantitative g enetics 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 ). … (more)
- Is Part Of:
- Natural sciences education. Volume 49:Issue 1(2020)
- Journal:
- Natural sciences education
- Issue:
- Volume 49:Issue 1(2020)
- Issue Display:
- Volume 49, Issue 1 (2020)
- Year:
- 2020
- Volume:
- 49
- Issue:
- 1
- Issue Sort Value:
- 2020-0049-0001-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2020-08-19
- Subjects:
- Agricultural education -- Periodicals
Life sciences -- Study and teaching -- Periodicals
Natural resources -- Study and teaching -- Periodicals
507.1 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
https://acsess.onlinelibrary.wiley.com/journal/21688281 ↗ - DOI:
- 10.1002/nse2.20026 ↗
- Languages:
- English
- ISSNs:
- 2168-8281
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17022.xml