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Culturally Relevant Data in Teaching Statistics and Data Science Courses
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Travis Weiland; Immanuel Williams – Journal of Statistics and Data Science Education, 2024
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…
Descriptors: Undergraduate Students, Predominantly White Institutions, Statistics Education, Culturally Relevant Education
Can You Trust Your Memory?
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Jeff Witmer – Journal of Statistics and Data Science Education, 2024
Data reported from memory can be unreliable. A simple activity lets students experience this firsthand.
Descriptors: Memory, Trust (Psychology), Reliability, Class Activities
Constructing a Course on Classification Methods for Undergraduate Non-Stem Students: Striving to Reach Knowledge Discovery
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Anna Khalemsky; Roy Gelbard; Yelena Stukalin – Journal of Statistics and Data Science Education, 2025
Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…
Descriptors: Classification, Undergraduate Students, Undergraduate Study, Data Science
What Is Missing in Missing Data Handling? An Evaluation of Missingness in and Potential Remedies for Doctoral Dissertations and Subsequent Publications That Use NHANES Data
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Hairui Yu; Suzanne E. Perumean-Chaney; Kathryn A. Kaiser – Journal of Statistics and Data Science Education, 2024
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…
Descriptors: Statistics Education, Data Science, Data Use, Research Problems
Obtaining and Applying Public Data for Training Students in Technical Statistical Writing: Case Studies with Data from U.S. Geological Survey and General Ecological Literature
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Barb Bennie; Richard A. Erickson – Journal of Statistics and Data Science Education, 2024
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…
Descriptors: Undergraduate Students, Statistics Education, Data Science, Earth Science
Philosophy within Data Science Ethics Courses
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Sara Colando; Johanna Hardin – Journal of Statistics and Data Science Education, 2024
There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, we…
Descriptors: Philosophy, Data Science, Ethical Instruction, Ethics
StatTeacherAssistant: An Application for Creating, Adjusting, and Checking the Suitability of Data Sets for Courses That Incorporate Introductory Statistical Methods
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Christopher J. Casement; Laura A. McSweeney – Journal of Statistics and Data Science Education, 2024
As the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there…
Descriptors: Statistics Education, Data Science, Educational Technology, Computer Software
Coding Code: Qualitative Methods for Investigating Data Science Skills
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Allison S. Theobold; Megan H. Wickstrom; Stacey A. Hancock – Journal of Statistics and Data Science Education, 2024
Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these…
Descriptors: Computer Science Education, Coding, Data Science, Statistics Education
Open Case Studies: Statistics and Data Science Education through Real-World Applications
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Carrie Wright; Qier Meng; Michael R. Breshock; Lyla Atta; Margaret A. Taub; Leah R. Jager; John Muschelli; Stephanie C. Hicks – Journal of Statistics and Data Science Education, 2024
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…
Descriptors: Statistics, Statistics Education, Open Educational Resources, Case Method (Teaching Technique)
Neglected, Acknowledged, or Targeted: A Conceptual Framing of Variability, Data Analysis, and Domain Consequences
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Zachary del Rosario – Journal of Statistics and Data Science Education, 2024
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,…
Descriptors: Foreign Countries, Engineering Education, Engineering, Technical Occupations
Taking the Next Step in Exploring the "Literary Digest" 1936 Poll
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Beth Chance; Andrew Kerr; Jett Palmer – Journal of Statistics and Data Science Education, 2024
While many instructors are aware of the "Literary Digest" 1936 poll as an example of biased sampling methods, this article details potential further explorations for the "Digest's" 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to…
Descriptors: Publications, Public Opinion, Surveys, Bias
Studying the Opportunities Provided by an Applied High School Mathematics Course: Explorations in Data Science
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Jo Boaler; Kira Conte; Ken Cor; Jack A. Dieckmann; Tanya LaMar; Jesse Ramirez; Megan Selbach-Allen – Journal of Statistics and Data Science Education, 2025
This article reports on a multi-method study of a high school course in data science, finding that students who take data science take more mathematics courses than those who do not, there are more under-represented students in data science than is typical for other advanced mathematics courses; that the students who take data science are more…
Descriptors: Mathematics Instruction, Opportunities, High School Students, Data Science
Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers
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Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
Personalized Education through Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE): Proof-of-Concept Studies for Designing and Evaluating Personalized Education
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Sy-Miin Chow; Jungmin Lee; Jonathan Park; Prabhani Kuruppumullage Don; Tracey Hammel; Michael N. Hallquist; Eric A. Nord; Zita Oravecz; Heather L. Perry; Lawrence M. Lesser; Dennis K. Pearl – Journal of Statistics and Data Science Education, 2024
Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden…
Descriptors: Individualized Instruction, Instructional Design, Science Education, Higher Education
Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps
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Nathan A. Quarderer; Leah Wasser; Anne U. Gold; Patricia Montaño; Lauren Herwehe; Katherine Halama; Emily Biggane; Jessica Logan; David Parr; Sylvia Brady; James Sanovia; Charles Jason Tinant; Elisha Yellow Thunder; Justina White Eyes; LaShell Poor Bear/Bagola; Madison Phelps; Trey Orion Phelps; Brett Alberts; Michela Johnson; Nathan Korinek; William Travis; Naomi Jacquez; Kaiea Rohlehr; Emily Ward; Elsa Culler; R. Chelsea Nagy; Jennifer Balch – Journal of Statistics and Data Science Education, 2025
Today's data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and…
Descriptors: Electronic Learning, Earth Science, Environmental Education, Science Education