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

Author:

Schultheis Elizabeth H.1,Kjelvik Melissa K.2

Affiliation:

1. ELIZABETH H. SCHULTHEIS is a Postdoctoral Researcher, at W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060; e-mail: eschultheis@gmail.com.

2. MELISSA K. KJELVIK is a Postdoctoral Researcher, at W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060; e-mail: kjelvikm@gmail.com.

Abstract

Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.

Publisher

University of California Press

Subject

General Agricultural and Biological Sciences,Agricultural and Biological Sciences (miscellaneous),Education

Reference57 articles.

1. ACT, Inc. (2014). ACT College and Career Readiness Standards: Science. https://www.act.org/standard/planact/science.

2. First-year physics students' perceptions of the quality of experimental measurements;International Journal of Science Education,1998

3. American Statistical Association (2014). Curriculum Guidelines for Undergraduate Programs in Statistical Science. http://www.amstat.org/education/curriculumguidelines.cfm.

4. Development of a framework for graph choice and construction;Advances in Physiology Education,2016

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