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.
CBE Life Sci Educ. 2019 Jun;18(2):es2. doi: 10.1187/cbe.18-02-0023.
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.
数据在科学和社会中变得越来越重要，因此数据素养对于学生来说是一项至关重要的资产，无论是在科学、技术、工程和数学领域内还是外，学生都需要具备数据素养，以便在未来能够过上富有成效的生活。在本文中，我们讨论了为什么当学生有机会使用来自科学研究的真实数据时，他们可能会获得围绕数据素养的最强学习体验。首先，我们探讨了定量推理、数据科学和数据素养这三个领域之间的重叠，特别关注数据素养如何源自于在真实数据背景下进行定量推理和数据科学实践。接下来，我们确定并描述了影响真实数据集复杂性的特征（选择、策展、范围、大小和混乱程度），以及这些特征对数据素养教学的影响。最后，我们讨论了未来的研究领域，旨在确定真实数据可能对学生学习产生的影响。这些领域包括定义课堂上围绕数据使用的预期学习成果，以及确定在课堂上使用数据来培养学生数据素养能力时的教学最佳实践。
