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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
Stoudt, Sara – Journal of Statistics and Data Science Education, 2022
To paraphrase John Tukey, the beauty of working with data is that you get to "play in everyone's backyard." A corollary to this statement is that working with data necessitates collaboration. Although students often learn technical workflows to wrangle and analyze data, these workflows may break down or require adjustment to accommodate…
Descriptors: Collaborative Writing, Communication Skills, Writing Strategies, Writing Processes
Anthony Underwood; Aidan Sichel; Emily C. Marshall – Journal of Statistics and Data Science Education, 2024
Economics has become increasingly empirical and, alongside this shift, has come more demand for improved transparency and reproducibility in empirical economic research. In this article, we distribute a survey to almost 1500 economics faculty from the top 161 liberal arts colleges with an economics major (according to U.S. News & World Report)…
Descriptors: Teaching Methods, Economics Education, Undergraduate Students, Liberal Arts
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
Hoffman, Heather J.; Elmi, Angelo F. – Journal of Statistics and Data Science Education, 2021
Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students' experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures…
Descriptors: Statistics Education, Programming Languages, Troubleshooting, Coding
Sutter, Claudia C.; Givvin, Karen B.; Tucker, Mary C.; Givvin, Kathryn A.; Leandro-Ramos, Ana; Solomon, Paige L. – Journal of Statistics and Data Science Education, 2023
The COVID-19 pandemic and shift to remote instruction disrupted students' learning and well-being. This study explored undergraduates' incoming course concerns and later perceived challenges in an introductory statistics course. We explored how the frequency of concerns changed with the onset of COVID-19 (N=1417) and, during COVID-19, how incoming…
Descriptors: Student Attitudes, Barriers, Introductory Courses, Statistics Education
Liao, Shu-Min – Journal of Statistics and Data Science Education, 2023
SCRATCH, developed by the Media Lab at MIT, is a kid-friendly visual programming language, designed to introduce programming to children and teens in a "more thinkable, more meaningful, and more social" way. Although it was initially intended for K-12 students, educators have used it for higher education as well, and found it…
Descriptors: Teaching Methods, Coding, Programming Languages, Computer Science Education