Over the last decade, reform in science education has placed an emphasis on the science practices as a way to engage students in the process of science and improve scientific literacy. A critical component of developing scientific literacy is learning to apply quantitative reasoning to authentic scientific phenomena and problems. Students need practice moving fluidly (or fluently) between math and science to develop a habit of mind that encourages the application of quantitative reasoning to real-world scenarios. Here we present a student-facing model that challenges students to think across these two fields. The model brings together math and science with a goal to increase scientific literacy by engaging students in quantitative reasoning within the context of scientific questions and phenomena. In the classroom, the model serves to help students visualize the logical and necessary moves they make as they use quantitative reasoning to connect science practices with mathematical thinking. 
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                            Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy
                        
                    
    
            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. 
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                            - PAR ID:
- 10112628
- Date Published:
- Journal Name:
- CBE—Life Sciences Education
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1931-7913
- Page Range / eLocation ID:
- es2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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