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Activity Number: 4 - Theoretical and Empirical Contributions to Data Analytic Practice
Type: Invited
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: ENAR
Abstract #316707
Title: Examining the Impact of Software Instruction on Completion of Data Analysis Tasks
Author(s): Lucy D'Agostino McGowan*
Companies: Wake Forest University
Keywords: education; softward; R; randomized trial; shiny; tidyverse
Abstract:

We are interested in studying best practices for introducing students in statistics or data science to the programming language R. The “tidyverse” is a suite of R packages created to help with common statistics and data science tasks that follow a consistent philosophy. We have created two sets of online learning modules, one that introduces tidyverse concepts first and then dives into idiosyncrasies of R as a programming language, the second that takes a more traditional approach, first introducing R broadly and then following with an introduction to a particular suite of packages, the tidyverse. We have created a randomized study to examine whether the order certain concepts are introduced impacts whether learning objectives are met and/or how engaged students are with the material. This talk will focus on the mechanics of this study: how it was designed, how we enrolled participants, and how we evaluated outcomes.


Authors who are presenting talks have a * after their name.

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