Tools and Recommendations for Reproducible Teaching

被引：4

作者：

Dogucu, Mine ^{[1
,2
]}

Cetinkaya-Rundel, Mine ^{[3
,4
]}

机构：

[1] UCL, Dept Stat Sci, London, England

[2] Univ Calif Irvine, Dept Stat, Irvine, CA USA

[3] Duke Univ, Dept Stat Sci, Durham, NC USA

[4] RStudio, Durham, NC USA

来源：

JOURNAL OF STATISTICS AND DATA SCIENCE EDUCATION | 2022年 / 30卷 / 03期

关键词：

Computational reproducibility; Data science education; Open education; Teaching materials; Workflows;

D O I：

10.1080/26939169.2022.2138645

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

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 instructors adopt reproducible workflows for their own teaching. We consider computational reproducibility, documentation, and openness as three pillars of reproducible teaching framework. We share tools, examples, and recommendations for the three pillars.

引用

页码：251 / 260

页数：10

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