Generative artificial intelligence (AI) technologies are transforming the world of education, but their impact on students’ thinking and learning remains unclear. In this study, we investigated AI’s potential role in supporting statistical thinking by interviewing undergraduate students ( N = 5 ) as they completed a graphing task using Rtutor.AI, an AI-powered tool that integrates ChatGPT with R. The analysis yielded five key themes that describe students’ statistical thinking while using Rtutor.AI. The first theme demonstrated how the iterative and intuitive nature of prompting within Rtutor.AI shaped participants’ approaches to problem-solving. The next three themes—“Building statistical understanding through a step-by-step process,” “Identifying key elements of a problem to create specific prompts,” and “Lowering barriers to completion of statistical tasks”—illustrated how Rtutor.AI facilitated statistical thinking in various ways. The fifth theme showed that students’ prior statistical knowledge influenced their ability to interpret and contextualize Rtutor.AI’s output, and that Rtutor.AI did not fully absolve students of the need to think statistically. Overall, these results highlight both the potential benefits and risks of incorporating AI into statistics classrooms and can serve as an empirical basis for future scholarship aimed at creating scaffolding around AI technologies to support statistics instruction.

Statistics anxiety is recognised as a major challenge facing students at all academic levels. It particularly affects their performance in statistics and research methods courses. This study investigates changes in students’ statistics anxiety based on the perceived utility of learning resources and class attendance within an introductory statistics course. Through the application of social cognition theory, we also examine the role of previous performance in mathematics, perceived competence in mathematics, and statistical self-efficacy as contributors to statistics anxiety. A quasi-experimental approach was used with undergraduate students completing a survey at the start and end of an introductory statistics course. Paired sample t-tests indicated a significant reduction in statistics anxiety. Additionally, Pearson correlation points to negative correlations between posttest statistics anxiety scores, perceived competence in mathematics, and statistical self-efficacy. Using hierarchical regression, class attendance was found to predict posttest statistics anxiety, with the smaller classes having a greater impact on statistics anxiety. These findings highlight the need for further interrogation of the role of class size and classroom activities with explorations of how these may reduce students’ statistics anxieties.

The importance of data science skills for modern scientific research cannot be understated. Although policy documents increasingly recommend what skills should be included in undergraduate statistics and data science curricula, little is known about how students actually develop and apply these skills. This paper addresses this gap through an in-depth case study tracing one student’s learning progressions throughout her master’s program. Using a qualitative method for analyzing student code that has seen little use in Statistics Education research, I examined how Alicia transferred the data science skills from her applied statistics course into authentic research settings. The analysis shows that, while Alicia successfully navigated new challenges, she encountered persistent hurdles when extending bivariate techniques into multivariate contexts, particularly with visualizations and summary statistics. These findings highlight the obstacles students may face when applying classroom knowledge to real-world data problems. The results carry implications for instructors designing curricula, researchers studying how students learn data science, and policymakers shaping educational standards, underscoring the need to pair policy recommendations with research on the realities of student learning.

This systematic review examines data science education in U.S. informal learning environments through analysis of 20 studies. Our analysis reveals the landscape of this emerging field. Our findings highlight three critical dimensions shaping informal DSE: the methodological and theoretical diversity of the field; the interplay of people, practices, and places; and emerging design principles and pedagogical approaches. Effective programs integrate technical skills with critical perspectives, connect to personally meaningful contexts, and position learners as knowledge producers rather than consumers. We identify several promising approaches, including critical data literacies, personal data exploration, data storytelling, embodied learning, and youth positioning as data agents. Yet, implementation challenges persist: literacy barriers, data complexity, equity gaps between intentions and practice, and limited assessment frameworks constrain the field's ability to scale these innovations. Despite intentional efforts, notable gaps remain in rural, early childhood, and disability contexts. Critical approaches examining power and representation show promise for marginalized communities. Beyond technical recommendations, we argue for reconceptualizing data literacy toward collective sovereignty and assessment frameworks valuing transformative outcomes. Informal learning environments can serve not merely as preparation for existing data systems but as spaces for imagining and enacting more just alternatives that challenge power structures in data science education.

This paper recommends a refinement in teaching hypothesis testing in introductory, non-calculus-based statistics courses, focusing on how the null hypothesis is formulated in mean and proportion inference. The proposal is grounded in classroom experience at CSU Fullerton, where general education courses enroll liberal arts and natural science students in lecture–lab formats. Most mainstream textbooks at this level (e.g., Peck, Gould, De Veaux) present the null as an equality for one-sided tests. Instead, we suggest adopting the Neyman–Pearson framework, commonly used in mathematical statistics textbooks, which treats the null and alternative as complementary hypotheses, leading to an inequality in the null in one-sided tests. Framing the hypotheses in this way is more logical to students, allows fundamental hypothesis testing concepts such as Type I error and significance level to be taught more precisely, and helps students recognize the uncertainties inherent in hypothesis testing rather than viewing it as a purely algorithmic process. To support instructor implementation, we provide a classroom activity accompanied by an interactive Shiny app that allows students to explore Type I error probabilities empirically and visually. This pedagogical framework is consistent with GAISE recommendations to teach statistical thinking, focus on conceptual understanding, and use technology to explore ideas.

Due to workforce demands and changing societal needs, ideally all university students, regardless of major, should have the opportunity to gain some level of data acumen in their university career. This paper provides thoughtful methods of analysis for measuring how a higher education institution serves its students in providing opportunities to achieve three different levels of data acumen. Using one institution as a case study, the results show that overall students need more exposure to data analysis skills. The methods presented can be generalized and applied to other institutions worldwide thus contributing to the literature on how to assess students’ attainment of data acumen in their undergraduate experiences.

The proliferation of data in all aspects of our lives positions statistical literacy as a critical skill in the 21st century. As teachers’ attitudes toward statistics have far-reaching implications for their future learning and that of their students, there is an impetus to identify the types of experiences that support the development of positive attitudes toward statistics. Using Japanese Lesson Study as a guiding framework, this study examined the influence of an 11-week, practice-based mathematics elective on 28 pre-service primary teachers’ attitudes toward statistics. Using Shapiro’s framework to inform our approach, we report on quantitative and qualitative data to reveal the nature and trajectory of pre-service teacher attitudes across the duration of the elective and particular experiences they attribute to these changes. Our findings indicate that the pre-service teachers credited the exploration of locally generated, societally relevant data and the use of math action technology to support data analysis with positive changes in attitudes toward statistics. Furthermore, opportunities to collaboratively design, deliver and extend rich learning experiences in the classroom were valued by participants.

With a proliferation of Doctor of Nursing Practice (DNP) programs, there is a growing need to support statistical literacy of DNP and Ph.D.-prepared nurses and the collaboration between them. Nurses with advanced degrees (DNPs and Ph.D.s) are increasingly required to deeply engage in evaluating and applying quantitative research with some, namely Ph.D.s, developing quantitative research. These activities require statistical literacy. However, recent literature has identified low statistical literacy among nurses with advanced degrees. To address these needs, we developed a collaborative advanced quantitative research design and statistics course for DNP and Ph.D. students guided by adult learning theory and the Guidelines for Assessment and Instruction in Statistics Education. In this course, intraprofessional collaboration between DNP and Ph.D. students is facilitated through partnered projects. Statistical literacy is emphasized through critical evaluation of statistical literature and manuscript-style written assignments. The collaborative quantitative methods course is delivered in a hybrid format with an in-person week of class and 16 online modules. The course consists of three overlapping threads with emphasis on (a) reading and evaluation of statistical literature, (b) conducting and communicating statistical analysis, and (c) design of a quantitative research study. Forty seven students have completed the newly designed course over three cohorts. Students and faculty have reported increased confidence with statistical writing and thoughtful engagement across programs following the introduction of the collaborative quantitative methods course.

Reacting to the Past is a game-based pedagogy in which students take on roles in a broader historical conflict with elements of game fiction, competition, and collaboration. This paper introduces two Reacting to the Past games developed for introductory statistics courses: “The Cigarette Century”: Tobacco and Lung Cancer, 1964-1965 and Cholera! at the Pump: Contagionism, Miasma Theory and Sanitation, London 1854. Both games are used to teach specific statistical content including measures of risk, data visualization, and hypothesis testing while also using historical context and real datasets to emphasize statistical thinking and provide relevance. Reacting to the Past games as high impact practices are strongly in alignment with the 2016 GAISE recommendations including conceptual understanding, use of real data, and active learning. Both games were implemented in an “Applied Statistics for the Biological Sciences” course at a public four-year university. Students enrolled in the course (N = 115 over five semesters from Spring 2023 to Spring 2025) were primarily juniors or seniors (63%) and Biology or Human Health Science majors (75%). Assessment of performance in the games is based on portfolios that include scaffolded background research, posters presentations, labs, and reflections. Course evaluations show students generally found the games to be fun, interactive, and effective at teaching statistical thinking, although some students struggled with historical language, workload, and complexity of the projects. Both the Cigarette Century and Cholera 1854 games and additional games are available through the Reacting Consortium by instructor subscription ($25-$150 annually, income-based). The student-facing game books discussed in this paper are reproduced in the Supplementary Materials with permission from the authors and publisher. In addition, full game materials for Cigarette Century and Cholera 1854 are available at no cost on the Reacting website for one month following the publication of this article in a Journal’s Issue.

Given the growing importance of data literacy in a digitalized society, this paper presents a transdisciplinary teaching concept based on the Data Investigation Process Framework. The evaluation of a project week entitled “On the road – Adolescents develop concepts for cycling in their own city using digital geomedia” demonstrated that the concept effectively enhances secondary school students’ data literacy, fostering critical thinking and improving their ability to evaluate data. In particular, project-based and real-world learning experiences- especially those involving the collection of students’ own datasets - proved to be highly beneficial. The teaching concept was evaluated through an educational intervention study conducted at five schools in Germany, involving a total of 121 students. The study focused on learners’ self-efficacy, measured using the standardized test instrument SESSKO, as well as the influence of personal data collection on students’ data analysis skills.