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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
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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
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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
Tanya Mae Lamar – ProQuest LLC, 2023
The divide between those who do and those who do not excel in mathematics is patterned in problematic ways. Women and people of color are typically underrepresented in Science, Technology, Engineering, and Math (STEM) and other quantitative fields (ex. Finance) where mathematics plays gatekeeper. However, mathematics is not a subject these groups…
Descriptors: Data Science, STEM Education, High School Students, Student Attitudes