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Victoria Delaney; Victor R. Lee – Information and Learning Sciences, 2024
With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic…
Descriptors: High School Teachers, Data Use, Information Literacy, Aesthetics
Dogucu, Mine; Johnson, Alicia A.; Ott, Miles – Journal of Statistics and Data Science Education, 2023
Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical…
Descriptors: Access to Information, Inclusion, Instructional Materials, Statistics Education
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
Alderson, David L. – INFORMS Transactions on Education, 2022
This article describes the motivation and design for introductory coursework in computation aimed at midcareer professionals who desire to work in data science and analytics but who have little or no background in programming. In particular, we describe how we use modern interactive computing platforms to accelerate the learning of our students…
Descriptors: Curriculum Design, Introductory Courses, Computation, Data Science
Sanchez Reyes, Luna L.; McTavish, Emily Jane – Journal of Statistics and Data Science Education, 2022
Research reproducibility is essential for scientific development. Yet, rates of reproducibility are low. As increasingly more research relies on computers and software, efforts for improving reproducibility rates have focused on making research products digitally available, such as publishing analysis workflows as computer code, and raw and…
Descriptors: Case Studies, Replication (Evaluation), Data Science, Scientific Research
Dogucu, Mine; Çetinkaya-Rundel, Mine – Journal of Statistics and Data Science Education, 2022
It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science…
Descriptors: Statistics Education, Data Science, Teaching Methods, Instructional Materials
Jose L. Salas; Xinran Wang; Mary C. Tucker; Ji Y. Son – Online Learning, 2024
Students believe mathematics is best learned by memorization; however, endorsing memorization as a study strategy is associated with a decrease in learning (Schoenfeld, 1989). When the world changed with the onset of the COVID-19 global pandemic, instruction transitioned to fully remote instruction where many assignments and examinations became…
Descriptors: Distance Education, Memorization, Pandemics, COVID-19