Juana Sanchez, JSDSE Editor-in-Chief
Volume 34, Issue 3 of the open-access Journal of Statistics and Data Science Education features a conversation between John Gabrosek and Michelle Everson on team-based learning, teaching with generative AI, the Guidelines for Assessment and Instruction in Statistics Education, and more. Both Gabrosek (2010–2012) and Everson (2013–2015) are former editors of the Journal of Statistics Education (the title of JSDSE until 2021). They recount for readers how the journal has changed alongside the evolving practices of statistics education.
This issue revisits, through a new lens, past concepts and a well-known dataset from traditional mathematical statistics courses. The paper, “Estimating Tanks-Communication in Mathematical Statistics,” authored by Amy Wagaman, shows readers how to use the German tank problem in a calculus-based mathematical statistics course to engage students in theoretical derivations, computation (via a simulation study), and writing to communicate in statistics.
Meanwhile, Mortaza Jamshidian and Parza Jamshidian critically evaluate noncalculus-based introductory statistics textbooks, noting the omission of the Neyman-Pearson hypothesis testing framework. In their paper, “Unraveling the Mystery of the Null Hypothesis: How Using Inequality Enhances Conceptual Understanding of Hypothesis Testing for Our Introductory Students,” they discuss how to implement that framework in the introductory statistics classroom.
A paper by Yikun Han, Michelle Krell Kydd, Joseph Ward, and Ambuj Tewari, “Teaching Machine Olfaction in an Undergraduate Deep Learning Course: A Pilot Integrating Chemistry, Machine Learning, and Sensory Evaluation,” explores an undergraduate deep-learning course that employs a multidisciplinary approach. The authors describe a hands-on activity that uses selective odorants to engage students in data collection and the fundamentals of machine olfaction. Through this process, students clearly see how physical odor molecules transform into a structured, rectangular dataset.
Evidence-based approaches to teaching collaboration in different statistics education contexts are the subject of the remaining papers. Mario Davidson and Regina Russell describe how they use case-based learning in a graduate statistical collaboration course. Figaro Loresto, Katherine Kissler, and Gail E. Armstrong focus on teaching quantitative research design through collaboration in doctoral nursing education. Jessica L. Alzen, Kimberly J. Cho, and Eric A. Vance discuss a community-of-practice approach to teaching interdisciplinary data science collaboration to advanced undergraduate and graduate students and researchers. Vance, Ilana M. Trumble, Jessica L. Alzen, and Leanna L. House provide a workflow for fostering interdisciplinary collaboration among academic fields, industry sectors, and organizations in a course designed for senior undergraduates and graduate students.
Collaboration takes a unique form when researchers and educators share goals and build a community of practice to drive educational innovation. In the paper, “Teachers Co-Designing and Enacting Elementary Data Science Curriculum through Connected Learning,” Danielle Herro, Ibrahim Oluwajoba Adisa, and Abimbade Oluwadara detail their experiences working with teachers to co-design data science units for elementary school students.
Data science professionals, as students in an online course on R programming, do better in peer reviewing when they are engaged in the course, according to Alon Friedman and Zachariah Beasley’s paper, “Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education.”
JSDSE is an open-access, peer-reviewed journal with a wide and diverse audience. Volume 34, Issue 3, and all past volumes of the journal can be accessed from the journal’s webpage. Feedback and questions about the journal can be emailed to Juana Sanchez.
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