Generative AI’s Role, Gaps in Ecosystem
for Data Science Education in Newest Issue

Juana Sanchez, Journal of Statistics and Data Science Education Editor-in-Chief

The first issue of Volume 34 of the Journal of Statistics and Data Science Education features semi-structured, task-based interviews with undergraduate students that suggest Rtutor.AI can facilitate statistical thinking, but more so if the students have strong conceptual understanding. Details can be found in “Students’ Statistical Thinking When Using Generative AI: A Descriptive Case Study,” by Vimal Rao, Amos Jeng, Julianna Drew, Bhuvan Kala, and Sanjana Gongati.

This issue also features an evaluation of data science education curricula and programs that originate from statistically trained teachers’ efforts. Zofia Bednarowska-Michaiel and Emma Uprichard, in their article titled “Bridging Interdisciplinary Data Science Education Challenges in the Classroom,” observed that throughout Europe and the United States, the teaching and learning of data science has centered on learning statistics and computational skills—neglecting multidisciplinarity, skills differences, decolonization, and ethics—in some programs.

Readers will also find activities and assessment methods that can be directly integrated into a course. For those teaching in the life sciences disciplines, the article by Chad Curtis on engaging undergraduate students in collaborative and active learning via historical role playing is an example.

Another example with direct actionable insights is the article by Sarah Samorodnitsky, Maria Masotti, Rachel Zilinskas, Aidan Neher, Ann Gliddon, Luke Gliddon, Marta Shore, Anne Eaton, Ann Brearley, and Laura Le, titled “Leveraging a Community Partnership to Provide Statistical Consulting Experience to Graduate Student Trainees.” Volunteer graduate students act as consultants for high school students (the clients), who are preparing a research proposal for their senior capstone project.

Marla L. Sole had community college social science students taking their first statistics course criticize an article, which led to engaging in a full investigative statistical project to find out about students’ sleeping habits. The article is titled “An Investigation Designed to Teach Statistical Thinking in the Midst of the COVID-19 Pandemic: Are Teens Living Like Vampires?”

Pre-K–12 is the time to plant the seeds of statistics and data science education. The education of the teachers is discussed in an article by Randall E. Groth, and the feasibility of teaching time series plots interpretation to middle school students is featured in an article by Jan Mokros, Jacob Sagrans, and Pendred Noyce. For those looking for alternative ways to assess students, Michael J. von Maltitz describes how portfolios of learning and interviews of understanding complement each other and foster student engagement and active learning in a mathematical statistics course.

The editorial note asks readers to pay attention to the context in which the papers originated and the methods used to provide evidence, as those help determine for whom a paper has most value.

Feedback about the journal and questions are welcome and can be emailed to Juana Sanchez .