In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics…

Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…

In this dissertation, I engage in three analytic cuts to think about/with a relational ontological orientation to data and data literacies/science education. The analysis focuses on the following question: What possibilities for teaching and learning about data are made possible when we attune to the relational, noisy, liminal, and material…

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…

The objectives of this study were to identify gaps in educational training for undergraduate and graduate students in agricultural data science, propose paths for filling these gaps, and provide an annotated list of resources currently available to different training levels. Data in this study was collected through three voluntary surveys catered…

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…

This study examined the data science career preference, data science skills, and core competencies of 416 students from fourteen Nigerian universities using a Google Forms-created structured online questionnaire. A convenience sampling technique was adopted to select the participants. Data were analysed using both descriptive and inferential…

With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice "statistical thinking," as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing…

Academic and practitioner interest in data science has increased considerably. Yet scholarly understanding of what motivates students to learn data science is still limited. Drawing on the theory of planned behavior, we propose a research model to examine the determinants of behavioral intentions to learn data science. In the proposed research…

Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics…

Variability is underemphasized in domains such as engineering. Statistics and data science education research offers a variety of frameworks for understanding variability, but new frameworks for domain applications are necessary. This study investigated the professional practices of working engineers to develop such a framework. The Neglected,…

Statistics & data science are growing, rapidly evolving, and increasingly important for an informed citizenry in a data-saturated world. In this dissertation, I address two central questions: (1) who is taking statistics? and (2) what are statistics courses teaching? I estimate that 920,000 US students take statistics in high school each year,…

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…

Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of…

This dissertation explores technological affordances in blended learning, their influence on the flexibility of statistics and data science curricula, and students' satisfaction with learning. While blended learning is often perceived as a flexible learning approach, its correlation with flexibility lacks substantial evidence in existing…