Technology Innovations in Statistics Education

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. We propose the use of LLMs as an opportunity for instructors to integrate more pedagogical approaches into homework design, 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 suitable to 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 is an example of one possible pathway for integrating generative AI into statistics courses, with strong potential for adaptation to other types of classes and assignments.

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.