Coverage of distributions, such as normal and t (among others), is prevalent in courses focused on probability and/or statistics irrespective of the discipline. In this article, we discuss existing software that can be used to create plots of univariate distributions and highlight several limitations of these tools. We then introduce a free, web-based, point-and-click application that we developed to address these limitations. The application is designed for both instructor and student use and supports interactive exploration of distributions. Visualizations of distributions can support conceptual understanding, and hands-on interactions with such tools can help students develop deeper insight into distributional behavior. To facilitate classroom implementation, we provide sample activities that can be incorporated at the introductory, intermediate, and advanced levels.
This scoping review explores the ways that R has been used in applied research college courses in social sciences and reports on the relevant studies. The included studies, meeting the eligibility criteria, comprise both quantitative and qualitative work in higher education, non-computer science (or related) disciplines, at both the undergraduate and graduate levels. Through a comprehensive and rigorous literature search and screening process, 21 articles are included in the review, covering outcomes on academic performance, course recommendations, and student perceptions of using R.
As generative AI becomes increasingly embedded in everyday life, the thoughtful and intentional integration of AI-based tools into statistics education has become essential. We address this need with a focus on homework assignments and we propose the use of LLMs as a companion to complete homework by developing an open-source tool named LLteacher. This LLM-based tool preserves learning processes and it guides students to engage with AI in ways that support their learning, while ensuring alignment with course content and equitable access. We illustrate LLteacher's design and functionality with examples from an undergraduate Statistical Computing course in R, showing how it supports two distinct pedagogical goals: recalling prior knowledge and discovering new concepts. While this is an initial version, LLteacher demonstrates one possible pathway for integrating generative AI into statistics courses, with strong potential for adaptation to other types of classes and assignments.
Programming or coding plays a crucial role in ensuring reproducibility in data analysis. Among the various programming languages in the field of data science, R stands out as a powerful tool capable of implementing a diverse range of statistical and graphical techniques. Unfortunately, in general education, lots of non-computing majors do not take a typical programming course due to their fear of the command line. A web app Rstats (http://esumath.shinyapps.io/rstats) was developed to promote students’ interest in R programming while learning introductory statistics, irrespective of their coding background. The web app provides a user-friendly point-and-click interface for data analysis, allowing students to explore statistical concepts without having to write code directly. But reproducible R code for the data analysis will be generated and displayed in the web app. Thus students can learn the programming language R during data analysis with the web app. By removing the intimidation associated with coding features, the web app aims to engage a broader audience in the realm of data and information. Source code of the app is provided for instructors who wish to deploy the web app to their own server. The features of the web app are described in detail and how the web app was applied to a GE statistical data analysis is discussed.
This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.
