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Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

Melissa K. Kjelvik

*Address correspondence to: Melissa K. Kjelvik (E-mail Address: [email protected]).

BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824

W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060

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and

Elizabeth H. Schultheis

BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824

W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060

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Stephanie Gardner, Monitoring Editor

Published Online:10 May 2019https://doi.org/10.1187/cbe.18-02-0023

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Abstract

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

Vol. 18, No. 2

June 01, 2019

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Downloads: 2754

Citations: 54

History

Submitted: 21 February 2018

Revised: 11 March 2019

Accepted: 22 March 2019

Information

© 2019 M. K. Kjelvik and E. H. Schultheis. CBE—Life Sciences Education © 2019 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

ACKNOWLEDGMENTS

We give special thanks to Julie Morris, Ariel Cintron-Arias, Laurel Hartley, Kristin Jenkins, Robert Mayes, Louise Mead, Paul Strode, Molly Stuhlsatz, Gordon Uno, and Jeremy Wojdak for thought-provoking conversations at the Data Nuggets National Institute for Math and Biology Synthesis (NIMBioS) working group (National Science Foundation [NSF] DBI 1300426). We also thank the organizers of the 2017 Data Science Education Technology conference, along with all the participants for sharing great insights into the state of the field. Thank you to Cheryl Hach, May Lee, Paul Strode, and the anonymous reviewers who provided useful feedback on earlier versions of this article. Many ideas expressed in this article are related to our experiences developing Data Nuggets at Michigan State University. We received funding and support for Data Nuggets from the Kellogg Biological Station (KBS) GK–12 Project (NSF DGE 0947896), KBS Long-Term Ecological Research Program K–12 Partnership (NSF DEB 1027253), and BEACON Center for the Study of Evolution in Action (NSF DBI 0939454). Data Nuggets are currently funded by NSF DRK-12 1503211. We also thank teachers Marcia Angle, Cheryl Hach, Ellie Hodges, and Kristy Campbell for their valuable insights and work identifying key areas needed in curricular development. This is KBS Publication #2008.

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