JSDSE Editor Invites Submissions
Sharing Teaching Experiences
Juana Sanchez, JSDSE Editor
The editorial for the April issue of the Journal of Statistics and Data Science Education includes a call for papers that share experiences from teachers using artificial intelligence in their statistics or data science classrooms to achieve a specific learning objective. For example, Jacob Bien and Gourab Mukherjee—in their paper titled “Generative AI for Data Science 101: Coding Without Learning to Code” —highlight the challenges and benefits they faced using GitHub Copilot as an English-to-R-code translator to teach master’s business students how to engage with data.
Shonda Kuiper and coauthors share benefits and challenges of engaging students in data collection and resources for doing so in “The Greenhouse Effect: Using Student-Generated Agricultural Data to Warm Up Students for Data-Based Decision-Making.” Jessica Bates and coauthors offer a different perspective in “Cultivating Critical Thinking Skills: A Pedagogical Study in a Business Statistics Course,” as they propose a multi-faceted pedagogical course design intended to improve critical thinking skills.
Anelise Sabbag and coauthors highlight evidence-based teaching (also known as scientific or scholarly teaching) in “The Development of Collaborative Keys to Promote Engagement in Undergraduate Online Asynchronous Statistics Courses.” They shed light on certain limitations of online discussion forums and the advantages of adjusting collaborative keys to improve cooperative learning.
Mine Dogucu, Sibel Kazak, and Joshua Rosenberg recommend Bayesian data analysis in K–12 classes in “The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers.” Meanwhile, examples of tasks teacher educators can use to enhance teacher understanding of variability are the focus of “Teacher Preparation in Statistics: Focusing on Variability Through Attending to Precision,” by Anna Bargagliotti and Christina Eubanks-Turner.
Amy Nowacki and coauthors acknowledge there are four types of health science learners seeking statistical training and share the different levels at which they need to understand statistical topics in their paper, “Diagnosing Statistical Education Needs of Health Science Learners.” Quantitative literacy can be achieved with programs that include public health data, such as the one presented in “Increasing Interest in Data Literacy: The Quantitative Public Health Data Literacy Training Program,” by Jinal Shah and coauthors.
Christian Ritter, L. Allison Jones-Farmer, and Shine and Frederick W. Faltin discuss the advantages of experiential learning courses in their paper, “Expensive but Worth It: Live Projects in Statistics, Data Science, and Analytics Courses.”
Finally, Ciaran Evans and coauthors, in “Learning while Learning: Psychology Case Studies for Teaching Regression,” reproduce the results of two published research papers using R used to teach multiple regression and interaction of two continuous variables in multiple regression.
Feedback about the journal and questions about the call for papers can be emailed to Juana Sanchez at [email protected].