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Activity Number: 72 - SPEED: Statistical Learning and Data Challenge Part 2
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 4:45 PM
Sponsor: Section on Statistics and Data Science Education
Abstract #323726
Title: Student Perceptions on Reproducible Research in Introductory and Advanced Statistics Courses
Author(s): Nicholas W Bussberg*
Companies: Elon University
Keywords: reproducibility; introductory statistics; accessible curriculum; student perceptions

Reproducibility is an increasingly common requirement for quantitative research, both within and outside of academia. However, there is comparatively little research on how to implement reproducibility into quantitative curriculum. Of the methods that have been published, most of them are discipline- or software-specific, making them less easily transferable to other areas. My research aims to provide instructors from many quantitative disciplines, teaching with different software and at different levels, accessible strategies to teach reproducibility in undergraduate programs. I will discuss IRB-approved data on student perceptions of reproducibility in my introductory and advanced statistics courses from Fall 2021 to Spring 2022. Particularly at the introductory level for which there are students from many disciplines and backgrounds, the student perception data provides insight into whether students were receptive to learning about reproducibility in the classroom and thought the techniques that were presented were useful in creating reproducible research.

Authors who are presenting talks have a * after their name.

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