Integrating Computing in the Statistics and Data Science Curriculum: Creative Structures, Novel Skills and Habits, and Ways to Teach Computational Thinking

Abstract

Nolan and Temple Lang (2010) argued for the fundamental role of computing in the statistics curriculum. In the intervening decade the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the Journal of Statistics and Data Science Education we have assembled a collection of papers that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these papers provide useful guidance for the community to move these efforts forward.

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

“…Another element of this research is on undergraduate course contexts and particular designs that engage students with the specific technologies data scientists use in practice; what Horton and Hardin (2021) refer to as creative structures. These include deliberate choices about aspects of the course beyond the programming language that is (often) used: the course website, nature of assignments and projects, and means for teachers and students of giving and receiving feedback, among others.…”

“…A final finding from research highlighting disciplinary agency concerns scaffolds for learning new and highly valued analytic and modeling techniques in data science disciplines (Horton & Hardin, 2021), such as machine learning. 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.…”

An educator’s perspective of the tidyverse

“…Computing has a fundamental and growing role in the statistics and data science curriculum (Horton & Hardin 2021, Nolan & Temple Lang 2010. The revised Guidelines for Assessment and Instruction in Statistics Education (GAISE) College report notes the importance of technology and states: "ideally, students should be given numerous opportunities to analyze data with the best available technology (preferably, statistical software)" (Carver et al (2016), page 11).…”

Integrating computational thinking into a longitudinal data analysis course for public health students

“…As a result, the traditional approach of bygone days that focused excessively on skills in the use of cut-and-dried statistical procedures has largely given way to a variety of new approaches that emphasized deep understanding of statistical concepts [1,25,34]. In the past few years, the pedagogical reform movement in the teaching of introductory statistics has accelerated considerably [4,14,31,38], partly due to the recent convergence of statistics and data science [20].…”