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Direct linkAnna 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
Victoria Woodard – Journal of Statistics and Data Science Education, 2023
In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good…
Descriptors: Research Design, Data Collection, Statistics Education, Active Learning
Lu Ye; Yu Jin – Journal of Statistics and Data Science Education, 2024
Statistics is interdisciplinary and the practical application of statistical methods in various areas prompts undergraduates to learn more about statistics and better understand complex methods. This article presents a classroom teaching design that guides students in reading COVID-19 literature. The activities presented encourage peer-peer and…
Descriptors: Reading Instruction, Statistics Education, COVID-19, Pandemics
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
Esther Drill; Jessica A. Lavery; Stephanie Lobaugh; Jessica Flynn; Samantha Brown; Hannah Kalvin; Joanne F. Chou; David Nemirovsky; Zoe Guan; Sujata Patil; Kay See Tan – Journal of Statistics and Data Science Education, 2025
Persistent underrepresentation of Black and Hispanic Statistics degree holders relative to the U.S. population occurs at all levels in post-secondary education, contributing to the underrepresentation of Black and Hispanic Bio/Statisticians. Attempting to address this inequity before the undergraduate level, Memorial Sloan Kettering (MSK)'s Bridge…
Descriptors: Interaction, Electronic Learning, Statistics, Outreach Programs
Janet E. Rosenbaum; Lisa C. Dierker – Journal of Statistics and Data Science Education, 2024
Self-efficacy is associated with a range of educational outcomes, including science and math degree attainment. Project-based statistics courses have the potential to increase students' math self-efficacy because projects may represent a mastery experience, but students enter courses with preexisting math self-efficacy. This study explored…
Descriptors: Self Efficacy, Statistics Education, Introductory Courses, Self Esteem
Grace Pai – Journal of Statistics and Data Science Education, 2025
Instructors are increasingly using interactive student response systems (SRS) to foster active learning and deepen student understanding in statistics education. Yet most studies focus on either the benefits of SRS or on how "students" can receive and use feedback, rather than on how "instructors" can use formative assessment…
Descriptors: Community Colleges, Community College Students, Statistics Education, Active Learning
Starns, Jeffrey J.; Cohen, Andrew L.; Vargas, John M.; Lougee-Rodriguez, William F. – Journal of Statistics and Data Science Education, 2021
We developed and tested strategies for using spatial representations to help students understand core probability concepts, including the multiplication rule for computing a joint probability from a marginal and conditional probability, interpreting an odds value as the ratio of two probabilities, and Bayesian inference. The general goal of these…
Descriptors: Active Learning, Probability, Statistics Education, Concept Formation
Huang, Wen; London, Jeremi S.; Perry, Logan A. – Journal of Statistics and Data Science Education, 2023
Understanding statistics is essential for engineers. However, statistics courses remain challenging for many students, as they find them rigid, abstract, and demanding. Prior research has indicated that using project-based learning (PjBL) to demonstrate the relevance of statistics to students can have a significant effect on learning in these…
Descriptors: Student Projects, Active Learning, Student Attitudes, Relevance (Education)
Reyneke, Fransonet; Fletcher, Lizelle; Harding, Ansie – Journal of Statistics and Data Science Education, 2021
This article focuses on the unique contribution of the QT-clicker regarding formative and summative assessment in a large flipped first year statistics module. In this module, the flipped classroom as pedagogical model first substituted the traditional teaching model. QT-clickers were subsequently introduced to enable active and cooperative…
Descriptors: Audience Response Systems, Summative Evaluation, Flipped Classroom, Statistics
Vinje, Hilde; Brovold, Helge; Almøy, Trygve; Frøslie, Kathrine Frey; Saebø, Solve – Journal of Statistics and Data Science Education, 2021
Historically, the introductory course in statistics at the Norwegian University of Life Sciences (NMBU), has taken a traditional, lecture-based form. A previous study at the NMBU concluded that the course structure appeared to disfavor certain cognitive or personality types, extraverts in particular. Therefore, in 2016, as an experiment, the…
Descriptors: Foreign Countries, Undergraduate Students, Statistics Education, Active Learning
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
