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:

• “Teaching Statistics: A Technology-Enhanced Supportive Instruction (TSI) Model During the COVID-19 Pandemic and Beyond,” by Serina Al-Haddad, Nancy Chick, and Farshid Safi
• “Teaching Students to Read COVID-19 Journal Articles in Statistics Courses,” by Lu Ye and Yu Jin
• “Challenges and Successes of Emergency Online Teaching in Statistics Courses,” by Analisa Flores, Lauren Parker Cappiello, and Isaac Quintanilla Salinas

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