In recent years, big data has become ubiquitous in our day-to-day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and…

Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical…

This study explores the use of generative AI, specifically ChatGPT, in statistical data analysis and its implications for statistics education at universities of applied sciences. This paper begins with first discussing the future division of labor between humans and machines in the context of statistical data analyses following the widespread…

Providing a rich context has become a sine qua non of principled teaching of applied statistical thinking. With increasing opportunities to access secondary data, there should be increasing opportunity to work with rich context. We review the contextual information provided in 41 data sets suitable for introductory tertiary statistics teaching,…

In statistics classes, the central limit theorem has been demonstrated using simulation-based illustrations. Known population distributions such as a uniform or exponential distribution are often used to consider the behavior of the sample mean in simulated samples. Unlike such simulations, a number of real-data-based simulations are here…

In the context of an afterschool program in which students explore relatively large authentic datasets, we investigated how 11- to 14-year old students worked with categorical variables. During the program, students learned to use the Common Online Data Analysis Platform (CODAP), a statistical analysis platform specifically designed for middle and…

In the contemporary workplace, data scientists who are capable of interdisciplinary collaboration are in high demand. Universities need to provide data science students with a plethora of learning opportunities that involve collaboration in interdisciplinary contexts and engagement with industry partners. Curtin University and Lab Tests Online…

In this paper, we propose that informal aspects of data science could be introduced in primary school. The International Data Science in Schools Project (IDSSP) framework for data science curriculum provides a guide for a data science curriculum aimed at upper secondary level. We analyzed synergies between the IDSSP framework and the current…

As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re-conceptualizing how we teach undergraduate statistics and data science courses for majors and non-majors alike. In this paper, we focus on three crucial components for this re-conceptualization: Developing research…

In the 1990s, educators advocated for projects in statistical courses to enrich student learning. Prior research showcases the positive impact of Project-Based Learning (PBL), where students complete course-driven projects. In agreement with this perspective, we implemented PBL methodologies within two statistical courses at a North American…

Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep…

College-level statistics courses emphasize the use of the coefficient of determination, R-squared, in evaluating a linear regression model: higher R-squared is better. This often gives students an impression that higher R-squared implies better predictability since textbooks tend to use sample data to support the theory and students rarely have an…

Our complex world requires multivariate reasoning to make sense of reality. Within this paper, we offer a sequence of activities designed to develop multivariate reasoning by explicitly connecting data and visualization. The activities were designed based on a hypothetical learning trajectory we conjectured for students with limited experience…

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order…

The ubiquitous acquisition and generation of data require a reworking of curricula in introductory statistics in tertiary education. We present a renewed curriculum that focuses on scientific thinking, modeling, and simulation-based inference, utilizing R and various R tools such as shiny and learnr apps. We teach statistics from a data-centric…