To learn with data, students need "data" to explore. This can be deceptive--data-rich experiences typically involve much more than a straightforward science lab. Solving real problems with data means identifying authentic questions that are meaningful to students and provide a foundation for deep inquiry. Such situations often lend…

Data-driven inquiry as a science practice and instructional approach can provide a powerful context for project-based learning (PBL). Students engage in data-driven inquiry when they explore a rich data set and observe patterns, ask questions suggested by the data, and pursue answers about underlying phenomena. Krajcik (2015) describes five…

Data literacy is complex. When students investigate the natural world, they must be able to gather data, organize it in tables and spreadsheets, analyze it in context, and describe and interpret it--usually as evidence to support a scientific argument. These skills are echoed in the science and engineering practices of the "Next Generation…

When students understand content deeply, they recognize main concepts and understand the relationships among ideas. But the typical processes students use to study are generally not designed to generate conceptual understandings. Instead, they tend to become passive learners. In this article, the authors present three commonly used classroom…

Phase changes and intermolecular forces are important physical science concepts but are not always easy to present in an active learning format. This article presents several interactive activities in which students plot the melting points of some fatty acids and explore the effect that the nanoscale size and shape of molecules have on the…