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David Shilane; Nicole Di Crecchio; Nicole L. Lorenzetti – Teaching Statistics: An International Journal for Teachers, 2024
Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical…
Descriptors: Programming, Data Science, Programming Languages, Coding
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Paul Biberstein; Thomas Castleman; Luming Chen; Shriram Krishnamurthi – Informatics in Education, 2024
CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation-based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also…
Descriptors: Data Science, Secondary School Students, Programming, Open Source Technology
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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
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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