Difficulties in reproducing results from scientific studies have lately been referred to as ``reproducibility crisis". Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fields, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this manuscript, we review the existing literature on reproducibility education. We delve into the relationship between computing tools and reproducibility through visiting historical developments in this area. We share examples for teaching reproducibility and reproducible teaching while discussing the pedagogical opportunities created by these examples as well as challenges that the instructors should be aware of. We detail the use of teaching reproducibility and reproducible teaching practices in an introductory data science course. Lastly, we provide recommendations on reproducibility education for instructors, administrators, and other members of the scientific community.

As a result of the increased emphasis on mis- and over-use of p-values in scientific research and the rise in popularity of Bayesian statistics, Bayesian education is becoming more important at the undergraduate level. With the advances in computing tools, Bayesian statistics is also becoming more accessible for undergraduates. This study focuses on analyzing Bayesian courses for undergraduates. We explored whether an undergraduate Bayesian course is offered in our sample of 152 high-ranking research universities and liberal arts colleges. For each identified Bayesian course, we examined how it fits into the institution’s undergraduate curricula, such as majors and prerequisites. Through a series of course syllabi analyses, we explored the topics covered and their popularity in these courses, and the adopted teaching and learning tools, such as software. This article presents our findings on the current practices of teaching full Bayesian courses at the undergraduate level. Based on our findings, we provide recommendations for programs that may consider offering Bayesian courses to their students.

Introduction to Data Science (IDS) courses are being offered by many different departments either as a mandatory or an elective course. Because of the foundational nature of IDS courses to develop students’ understanding of data science, it is important to be aware of students’ potential learning difficulties. To that end, we conducted semi-structured interviews with 14 IDS instructors to study students’ difficulties. Qualitative content analysis was used to analyze the data. IDS instructors reported that students without prior coding experience encountered more syntactic difficulties than their peers. In terms of conceptual and strategic knowledge, students experienced difficulties in understanding principles of data visualization, the basics of coding, joining data sets, debugging and data wrangling. These findings suggest that IDS courses could be improved by addressing student difficulties and invite conducting future research with students to understand the dimensionality of student learning to improve capacity in data science (education).

UNLABELLED: Uncertainty is ubiquitous in science, but scientific knowledge is often represented to the public and in educational contexts as certain and immutable. This contrast can foster distrust when scientific knowledge develops in a way that people perceive as a reversals, as we have observed during the ongoing COVID-19 pandemic. Drawing on research in statistics, child development, and several studies in science education, we argue that a Bayesian approach can support science learners to make sense of uncertainty. We provide a brief primer on Bayes theorem and then describe three ways to make Bayesian reasoning practical in K-12 science education contexts. There are a) using principles informed by Bayes theorem that relate to the nature of knowing and knowledge, b) interacting with a web-based application (or widget-Confidence Updater) that makes the calculations needed to apply Bayes theorem more practical, and c) adopting strategies for supporting even young learners to engage in Bayesian reasoning. We conclude with directions for future research and sum up how viewing science and scientific knowledge from a Bayesian perspective can build trust in science. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11191-022-00341-3.