Analyzing Education Data with Open Science Best Practices, R, and OSF

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Abstract

This workshop demonstrates how using R can advance open science practices in education. We focus on R and RStudio because it is an increasingly widely-used programming language and software environment for data analysis with a large supportive community. We present: a) general strategies for using R to analyze educational data and b) accessing and using data on the Open Science Framework (OSF) with R via the osfr package. This session is for those both new to R and those with R experience looking to learn more about strategies and workflows that can help to make it possible to analyze data in a more transparent, reliable, and trustworthy way. Access the workshop slides and supplemental information at https://osf.io/vtcak/‚Äč.

Resources:

  1. Download R: https://www.r-project.org/‚Äč
  2. Download RStudio (a tool that makes R easier to use): https://rstudio.com/products/rstudio/...‚Äč
  3. R for Data Science (a free, digital book about how to do data science with R): https://r4ds.had.co.nz/‚Äč
  4. Tidyverse R packages for data science: https://www.tidyverse.org/‚Äč
  5. RMarkdown from RStudio (including info about R Notebooks): https://rmarkdown.rstudio.com/‚Äč
  6. Data Science in Education Using R: https://datascienceineducation.com/‚Äč

Link to resource: https://www.youtube.com/watch?v=WxdWzTIzYmI&t=4s

Type of resources: Teaching/Learning Strategy

Education level(s): Graduate / Professional

Primary user(s):

Subject area(s): Computer Science, Education

Language(s): English