Getting Messy With Databy: Joshua Rosenberg, Alex Edwards, and Bodong Chen

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Analyzing and interpreting data is essential to the practice of scientists and is also an essential science and engineering practice for science teaching and learning. Although working with data has benefits for student learning, it is also challenging, particularly with respect to aspects of work with data that are not yet very common in schools, such as developing quantitative models, understanding variation in data, and using larger, complex data sources. In this article, we aim to describe tools for engaging students to work with data in your class as well as three general strategies, including understanding how data is collected, experiencing the transformation of messy data sets in preparation for analysis, and modeling the data to answer a question. We show how these strategies can be employed using the freely-available, browser-based Common Online Data Analysis Platform, and outline connections to curricular standards.

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