1887

With Big Data Comes Big Responsibilities for Science Equity Research

    Authors: Cissy J. Ballen1,*, Henriette Tolstrup Holmegaard2
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    Affiliations: 1: Department of Biological Sciences, Auburn University, Auburn, AL 36849, United States; 2: Department of Science Education, University of Copenhagen, Copenhagen, Denmark
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Received 19 June 2018 Accepted 17 September 2018 Published 26 April 2019
    • ©2019 Author(s). Published by the American Society for Microbiology
    • [open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.

    • *Corresponding author. Mailing address: Department of Biological Sciences, Auburn University, Auburn, AL 36849. Phone: 334-844-4830. Fax: 334-844-1645. E-mail: [email protected]
    Source: J. Microbiol. Biol. Educ. April 2019 vol. 20 no. 1 doi:10.1128/jmbe.v20i1.1643
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    Abstract:

    Our ability to collect and access large quantities of data over the last decade has been revolutionary for many social sciences. Suddenly, it is possible to measure human behavior, performance, and activity on an unprecedented scale, opening the door to fundamental advances in discovery and understanding. Yet such access to data has limitations that, if not sufficiently addressed and explored, can result in significant oversights. Here we discuss recent research that used data from a large global sample of high school students to demonstrate, paradoxically, that in nations with higher gender equality, fewer women pursued science, technology, engineering, and mathematics (STEM) degrees than would be expected based on aptitude in those subjects. The for observed patterns is central to current debates, with frequent disagreement about the nature and magnitude of problems posed by the lack of female representation in STEM and the best ways to deal with them. In our international efforts to use big data in education research, it is necessary to critically consider its limitations and biases.

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2019-04-26
2019-08-23

Abstract:

Our ability to collect and access large quantities of data over the last decade has been revolutionary for many social sciences. Suddenly, it is possible to measure human behavior, performance, and activity on an unprecedented scale, opening the door to fundamental advances in discovery and understanding. Yet such access to data has limitations that, if not sufficiently addressed and explored, can result in significant oversights. Here we discuss recent research that used data from a large global sample of high school students to demonstrate, paradoxically, that in nations with higher gender equality, fewer women pursued science, technology, engineering, and mathematics (STEM) degrees than would be expected based on aptitude in those subjects. The for observed patterns is central to current debates, with frequent disagreement about the nature and magnitude of problems posed by the lack of female representation in STEM and the best ways to deal with them. In our international efforts to use big data in education research, it is necessary to critically consider its limitations and biases.

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