Computational Skills for Multivariable Thinking in Introductory Statistics

Abstract

Since the publishing of Nolan and Temple Lang's "Computing in the Statistics Curriculum" in 2010, the American Statistical Association issued new recommendations in the revised GAISE College report. To reflect modern practice and technologies, they emphasize giving students experience with multivariable thinking. Students develop multivariable thinking when they analyze real data in the context of investigating research questions of interest, which typically involve complex relationships between many variables. Proficiency in a statistical programming language facilitates the development of multivariable thinking by giving students tools to investigate complex data on their own. However, learning a programming language in an introductory course is difficult for many students. In this paper, we recommend a set of computational skills for introductory courses, demonstrate them using Rtidyverse , and describe a classroom activity to develop computational skills and multivariable thinking. We provide a tidyverse tutorial for introductory students, our course guide, and classroom activities.

Three principles for modernizing an undergraduate regression analysis course

“…Much of this philosophy has been incorporated in the introductory courses. In recent years there has been a wealth of literature on innovations in introductory statistics and data science courses (e.g, Adams et al 2021, C ¸etinkaya-Rundel & Ellison 2021, Farmus et al 2020, Baumer 2015, Hardin et al 2015. Many of these innovations, have been motivated by the need to help students gain the conceptual knowledge and computing skills required to analyze authentic complex and nonstandard data, such as analyzing text and spatial data.…”

“…Many of these innovations, have been motivated by the need to help students gain the conceptual knowledge and computing skills required to analyze authentic complex and nonstandard data, such as analyzing text and spatial data. Additionally these newly revised courses have put more emphasis on visualizing and interpreting multivariable relationships (Adams et al 2021) in line with GAISE recommendation to "give students experience with multivariable thinking." (pg.…”

A secret agent? K-12 data science learning through the lens of agency

“…For example, Fergusson and Pfannkuch (2022) show how the core tenets of machine learning can be taught to K-12 aged students when designed and taught in a particular manner; namely, using a particular ("informal") approach that emphasized visualizations, a potentially relevant data set (movie ratings), and a browser-based environment for students to run R code. Other papers emphasize machine learning (e.g., Zimmermann-Niefield et al, 2019) and even artificial intelligence (Druga & Ko, 2021) as well as modern approaches to inferential modeling , including Bayesian approaches (Erickson, 2017;Kazak, 2015;Warren, 2020), developing statistical software (Reinhart & Genovese, 2021), web scraping using social media data (Boehm & Hanlon, 2021;Dogucu & Çetinkaya-Rundel, 2021), and using git and GitHub (Adams et al, 2021;Beckman et al, 2021;Curtis et al, 2020;Kim & Henke, 2021;). This work shows that learners can develop the capacity to use new analytic and programming tools with deliberately-designed courses.…”