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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
Schneiter, Kady; Hadfield, KimberLeigh Felix; Clements, Jenny Lee – Journal of Statistics and Data Science Education, 2023
Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students' names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size…
Descriptors: Large Group Instruction, Statistics Education, Introductory Courses, Lecture Method
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
Pyott, Laura – Journal of Statistics and Data Science Education, 2021
Understanding the abstract principles of statistical experimental design can challenge undergraduate students, especially when learned in a lecture setting. This article presents a concrete and easily replicated example of experimental design principles in action through a hands-on learning activity for students enrolled in an experimental design…
Descriptors: Statistics Education, Research Design, Undergraduate Students, Active Learning
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
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
Petersen, Ashley – Journal of Statistics and Data Science Education, 2022
While correlated data methods (like random effect models and generalized estimating equations) are commonly applied in practice, students may struggle with understanding the reasons that standard regression techniques fail if applied to correlated outcomes. To this end, this article presents an in-class activity using results from Monte Carlo…
Descriptors: Intuition, Skill Development, Correlation, Graduate Students
George, Brandon J.; Leon, Juan – Journal of Statistics and Data Science Education, 2023
The recent rise in online learning in statistics has made it essential for instructors to teach effectively in that modality. In this retrospective, we reflect on how online course content was added to an introductory statistics course in a graduate public health program and how it was updated over time based on student feedback and instructor…
Descriptors: Online Courses, Flipped Classroom, Public Health, Statistics
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
Reinhart, Alex; Genovese, Christopher R. – Journal of Statistics and Data Science Education, 2021
Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since Nolan and Temple Lang's seminal paper, we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with…
Descriptors: Computer Software, Graduate Students, Computer Science Education, Statistics Education
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