Juana Sanchez, JSDSE Editor-in-Chief
The second issue of Volume 34 of the Journal of Statistics and Data Science Education features a panel interview conducted by Joshua Rosenberg with Matteo Bonvini, Andrew Bray, Ruobin Gong, and Bin Yu, who reflect on the role of statisticians in data science, the challenges of teaching data science to “Generation AI,” the data science life cycle, the forthcoming GAISE College Report, and teaching and research based on the veridical data science framework developed by Bin Yu and collaborators. The discussion about VDS highlights that many uncertainties introduced by the DSLC choices are not fully addressed by traditional statistical frameworks. These unaccounted-for uncertainties can lead to irreproducibility of results.
Two papers in this issue directly touch upon the many steps of the DSLC. The article “Gamifying Analytics Education: The Impact of the Craft Beer League on Student Engagement, Problem-Solving Skills, and Collaboration” describes how students of the authors—Silviya Valeva, Agustin Vallejo, Ronald Klimberg, Michael Marzano, Adison Geritz, and Michael Bruening—navigate the DSLC in their analytics course. Many of the themes discussed by the panel come to light in this paper and in the work of William Cipolli, Nicole M. Dalzell, Roy Bower, and Ciaran Evans—particularly the role of context, domain problems, and consequences.
In the second paper, “Battle of the Bands: Trying to Identify Contributions to a Collaboration,” the authors immerse readers in feature engineering that uses technology and data sets not traditionally discussed in textbooks. The objective of this activity is to help students discover how much each of several bands contributed to the whole collaborative track, “Allentown.”
The multifaceted nature of developing expertise in data science is also featured in the latest issue. Preparing sixth graders to be future data scientists is the subject of the paper “Seeing Our World Through Data: Sixth Graders Integrating Data Investigations in Collaborative Knowledge Building,” by Bodong Chen, Leanne Ma, and Vivian Yu Leung.
Similarly, Merve N. Kursav, Scott D. Pauls, Petra Bofert-Taylor, Lorie Loeb, and Laura Ray discuss professional development in data science for teachers in their article, “Accelerating Change of Practice Through Targeted Professional Development in Data Science.”
Modeling and software are two of the core pillars in the DSLC. Authored by Alex Reinhart, “The Regressinator: A Simulation Tool for Teaching Regression Assumptions and Diagnostics in R” describes the use of an R package that simulates regression problems to teach model-building.
Meanwhile, Sedigheh Abbasnasab Sardareh, Gavin T. L. Brown, and Paul Denny highlight how software design features can hinder or contribute to successful problem-solving by students. They compare SPSS and jamovi in “Statistical Software Usability for Novice Research Students in the Social Sciences: An Eye-Tracking Study.”
The last issue revisits best practices in teaching, highlighting the importance of group work and active learning in post-secondary education, while comparing traditional and web-enhanced courses.
JSDSE is an open-access journal with a wide and diverse audience. Volume 34, Issue 2, and all other issues can be accessed on the journal’s webpage. Feedback and questions may be emailed to Juana Sanchez.
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