The COVID-19 Pandemic and Data Science and Statistics Education

The global pandemic beginning in 2020 caused by the spread of the SARS-CoV-2 virus (more commonly described as COVID-19) led to hundreds of millions of cases, millions of deaths, and dramatic economic and social disruptions. The onset of COVID-19 had a direct and continuing impact on the educational sector, with institutions, instructors, students, and parents scrambling to adapt to a variety of online or hybrid educational models.

While those who taught or studied during this time are unlikely to forget their experiences, it’s important that the responses and approaches that were adopted be documented, assessed, and reviewed in light of what we have learned about what works (and what doesn’t).

We have seen shifts in terms of remote and hybrid learning, flexible scheduling, and the increased use of technology. The pandemic highlighted the need for additional professional development, modifications to assessment methods, improved understanding of social-emotional learning, and acknowledgement of historic and current disparities in our educational systems. Many of the practices that were adopted over the past four years are likely to remain in place due to larger societal shifts.

Fifteen papers to date have been published in the Journal of Statistics and Data Science Education (JSDSE) that have described approaches to dealing with the challenges of the pandemic, often framed by a goal of improving inclusive and flexible learning environments in a sustainable way (see also Dana Center 2021; Dogucu, Johnson, and Ott 2023). Taylor & Francis has recently made a new collection of papers available that focus on teaching statistics and data science and the COVID-19 pandemic (Horton, 2024). The papers, all of which are open access, are available at https://www.tandfonline.com/journals/ujse21/collections/Teaching-and-COVID. If additional papers are published in this area, they will be added to the collection. I can imagine many ways that this collection may help the community learn from this painful and challenging time.

The current issue of the Journal of Statistics and Data Science Education features ten papers on a variety of topics. The first three papers, part of the new COVID-19 collection, describe the challenges and successes of emergency online teaching, a technology enhanced supportive instruction model, and a way to help students engage with COVID-19 journal articles.

Other papers published in the issue explore a number of timely topics including:

• how causal inference is not just a statistics problem,

• what we should do differently in STAT 101,

• a qualitative analysis of student code,

• developing personalized education through individualized pathways,

• a review of investigative projects,

• an active learning activity for support vector classifiers, and

• case studies using data from the U.S. Geological Survey.

As always, the journal wouldn’t exist without the voluntary efforts of the editorial board members and reviewers. I’m thankful for their work on behalf of the profession.

Nicholas J. Horton http://orcid.org/0000-0003-3332-4311
[email protected]

Disclosure Statement

The author has no conflicts of interest to declare.