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Article: Toward Training and Assessing Reproducible Data Analysis in Data Science Education

TitleToward Training and Assessing Reproducible Data Analysis in Data Science Education
Authors
KeywordsData science education
Reproducibility
Reproducible data analysis
Communication
Action research
Issue Date2019
PublisherMIT Press: Data Intelligence. The Journal's web site is located at https://www.mitpressjournals.org/loi/dint
Citation
Data Intelligence, 2019, v. 1 n. 4, p. 381-392 How to Cite?
AbstractReproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports, only one-third of reports provided all necessary information for replication. The actual replication results also include conflicting claims; some lacked comparisons of original and replication results, indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results. The findings suggest that more training is needed to help data science students communicating reproducible data analysis.
Persistent Identifierhttp://hdl.handle.net/10722/294172

 

DC FieldValueLanguage
dc.contributor.authorYu, B-
dc.contributor.authorHu, X-
dc.date.accessioned2020-11-23T08:27:24Z-
dc.date.available2020-11-23T08:27:24Z-
dc.date.issued2019-
dc.identifier.citationData Intelligence, 2019, v. 1 n. 4, p. 381-392-
dc.identifier.urihttp://hdl.handle.net/10722/294172-
dc.description.abstractReproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports, only one-third of reports provided all necessary information for replication. The actual replication results also include conflicting claims; some lacked comparisons of original and replication results, indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results. The findings suggest that more training is needed to help data science students communicating reproducible data analysis.-
dc.languageeng-
dc.publisherMIT Press: Data Intelligence. The Journal's web site is located at https://www.mitpressjournals.org/loi/dint-
dc.relation.ispartofData Intelligence-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectData science education-
dc.subjectReproducibility-
dc.subjectReproducible data analysis-
dc.subjectCommunication-
dc.subjectAction research-
dc.titleToward Training and Assessing Reproducible Data Analysis in Data Science Education-
dc.typeArticle-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.authorityHu, X=rp01711-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1162/dint_a_00053-
dc.identifier.hkuros318960-
dc.identifier.volume1-
dc.identifier.issue4-
dc.identifier.spage381-
dc.identifier.epage392-
dc.identifier.eissn2641-435X-
dc.publisher.placeUnited States-
dc.identifier.issnl2641-435X-

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