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

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.

BioSkills Guide: Development and National Validation of a Tool for Interpreting theVision and ChangeCore Competencies

“…BioSkills Guide responsible conduct of research (Diaz-Martinez et al, 2019), quantitative reasoning (Durán and Marshall, 2018;Stanhope et al, 2017), bioinformatics (Wilson Sayres et al, 2018), data science (Kjelvik and Schultheis, 2019), data communication (Angra and Gardner, 2016), modeling (Quillin and Thomas, 2015;Diaz Eaton et al, 2019), the interdisciplinary nature of science (Tripp and Shortlidge, 2019), and scientific writing (Timmerman et al, 2011). Efforts to define general or STEMwide educational goals for college graduates can also inform how we teach competencies in biology, such as the Association of American College and University VALUE rubrics (Rhodes, 2010) and more targeted work on information literacy (Association of College and Research Libraries, 2015), communication (Mercer-Mapstone and Kuchel, 2017), and process skills (Understanding Science, 2016;Cole et al, 2018).…”

Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science

“…This is due to the inherent qualities of messy data, and their ability to engender unique learning opportunities not found in other resources. However, messy data sets can be quite complex, creating a potential barrier for classroom use (Kjelvik & Schultheis, 2019). To break down this barrier, we highlight techniques to scaffold the use of messy data and propose an activity sequence that gives students repeated practice working with various types of messy data, with increasing complexity over time.…”

“…Throughout this article, we use the term messy data to represent a particular type of authentic data (Kjelvik & Schultheis, 2019). A key element in messy data sets is variability.…”

“…Data literacy is defined as "the ability to understand and use data to inform decisions" (Mandinach & Gummer, 2013) and is an interdisciplinary field lying at the intersection of data science, quantitative reasoning, and authentic context (Kjelvik & Schultheis, 2019). In addition to specific abilities, data literacy is characterized by habits of mind such as curiosity, resiliency, and ethical decision making (Box 1).…”

BioSkills Guide: Development and National Validation of a Tool for Interpreting the Vision and Change Core Competencies

“…Since then, two groups have unpacked the core concepts into more detailed frameworks Cary & Branchaw, 2017). For competencies, biology education researchers have enumerated a variety of specific scientific practices, including: science process skills (Coil, Wenderoth, Cunningham, & Dirks, 2010), experimentation (Pelaez et al, 2017), scientific literacy (Gormally, Brickman, & Lutz, 2012), responsible conduct of research (Diaz-Martinez et al, 2019), quantitative reasoning (Durán & Marshall, 2018;Stanhope et al, 2017), bioinformatics (Wilson Sayres et al, 2018), data science (Kjelvik & Schultheis, 2019), data communication (Angra & Gardner, 2016), modeling (Diaz Eaton et al, 2019;Quillin & Thomas, 2015), the interdisciplinary nature of science (Tripp & Shortlidge, 2019), and scientific writing (Timmerman, Strickland, Johnson, & Payne, 2011). Efforts to define general or STEM-wide education goals for college graduates can also inform how we teach competencies in biology, such as the Association of American College & University VALUE rubrics (Rhodes, 2010) and more targeted work on information literacy (Association of College and Research Libraries, 2015), communication (Mercer-Mapstone & Kuchel, 2017), and process skills (Cole, Lantz, Ruder, Reynders, & Stanford, 2018;Understanding Science, 2016).…”