Teaching During COVID Leads April JSDSE
Nick Horton, Journal of Statistics and Data Science Education Editor
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. The April issue of the open-access Journal of Statistics and Data Science Education (JSDSE) leads off with the following three papers that describe approaches to teaching during the pandemic:
The issue also includes an editorial that addresses ways educators have grappled with the pandemic and an announcement of a new Taylor & Francis collection of open-access articles titled “Teaching Data Science and Statistics and the COVID-19 Pandemic.”
The following papers published in the issue explore other timely topics:
- “Causal Inference Is Not Just a Statistics Problem,” by Lucy D’Agostino McGowan, Travis Gerke, and Malcolm Barrett
- “What Should We Do Differently in STAT 101?” by Jeff Witmer
- “Coding Code: Qualitative Methods for Investigating Data Science Skills,” by Allison S. Theobold, Megan H. Wickstrom, and Stacey A. Hancock
- “Personalized Education Through Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE): Proof-of-Concept Studies for Designing and Evaluating Personalized Education,” by Sy-Miin Chow, Jungmin Lee, Jonathan Park, Prabhani Kuruppumullage Don, Tracey Hammel, Michael N. Hallquist, Eric A. Nord, Zita Oravecz, Heather L. Perry, Lawrence M. Lesser, and Dennis K. Pearl
- “A Review of the Use of Investigative Projects in Statistics and Data Science Courses,” by Allison Davidson
- “Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers,” by Qing Wang and Xizhen Cai
- “Obtaining and Applying Public Data for Training Students in Technical Statistical Writing: Case Studies with Data from U.S. Geological Survey and General Ecological Literature,” by Barb Bennie and Richard A. Erickson