Dogucu, Mine;
Çetinkaya-Rundel, Mine;
(2022)
Tools and Recommendations for Reproducible Teaching.
Journal of Statistics and Data Science Education
, 30
(3)
pp. 251-260.
10.1080/26939169.2022.2138645.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Tools and Recommendations for Reproducible Teaching |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/26939169.2022.2138645 |
| Publisher version: | https://doi.org/10.1080/26939169.2022.2138645 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published with license by Taylor and Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted. |
| Keywords: | Computational reproducibility, Data science education, Open education, Teaching materials, Workflows |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10159360 |
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