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Reflections on the Future of Statistics Education in a Technological Era

Craig Alexander, Jennifer Gaskell, Vinny Davies

TL;DR

Technological developments including R widely used and Python increasingly incorporated into statistics and data analytics programmes are explored and strategies for their integration into contemporary statistics education are discussed.

Abstract

Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technologies into the statistical curriculum where possible. University-level statistics education has experienced substantial technological change, particularly in the tools and practices that underpin teaching and learning. Statistical programming has become central to many courses, with R widely used and Python increasingly incorporated into statistics and data analytics programmes. Additionally, coding practices, database management, and machine learning now feature within some statistics curricula. Looking ahead, we anticipate a growing emphasis on artificial intelligence (AI), particularly the pedagogical implications of generative AI tools such as ChatGPT. In this article, we explore these technological developments and discuss strategies for their integration into contemporary statistics education.

Reflections on the Future of Statistics Education in a Technological Era

TL;DR

Technological developments including R widely used and Python increasingly incorporated into statistics and data analytics programmes are explored and strategies for their integration into contemporary statistics education are discussed.

Abstract

Keeping pace with rapidly evolving technology is a key challenge in teaching statistics. To equip students with essential skills for the modern workplace, educators must integrate relevant technologies into the statistical curriculum where possible. University-level statistics education has experienced substantial technological change, particularly in the tools and practices that underpin teaching and learning. Statistical programming has become central to many courses, with R widely used and Python increasingly incorporated into statistics and data analytics programmes. Additionally, coding practices, database management, and machine learning now feature within some statistics curricula. Looking ahead, we anticipate a growing emphasis on artificial intelligence (AI), particularly the pedagogical implications of generative AI tools such as ChatGPT. In this article, we explore these technological developments and discuss strategies for their integration into contemporary statistics education.
Paper Structure (21 sections, 1 figure)

This paper contains 21 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Programming within the Statistics Curriculum
  3. R in the Statistics Curriculum
  4. Tidyverse
  5. Shiny
  6. RMarkdown & Quarto
  7. Polyglot Programming
  8. Introducing Python
  9. Teaching both R and Python
  10. The Influence of Data and Coding Practices
  11. Modern data sources & structures
  12. How can we improve the way we teach modern data
  13. Changes in code-based good practice and transparency
  14. Incorporating ML and AI into Statistical Education
  15. Teaching ML
  16. ...and 6 more sections

Figures (1)

  • Figure 1: An example of the language switcher, shown in the top right of each image. (a) R code enabled, allowing students to view the notes as if they were available only in R. (b) Equivalent view with Python enabled.