From Puzzles to Discovery: Building Students’ Data Sensemaking Skills Through Authentic Science Investigations

This article explores how structured Data Puzzle lessons can build students’ data sensemaking skills and serve as a stepping-stone to more open-ended exploration of real-world datasets. The lessons use teacher strategies like the “slow-reveal” technique, which help students practice describing visual patterns and develop evidence-based explanations for investigative questions. Scaffolded prompts guide students in summarizing new insights and generating their own questions, some of which they then investigate in a related but larger dataset via CODAP in an open-ended Puzzle Piece activity. The article focuses on how Tina Grammel, a high school science teacher, used these resources to help her students connect with research by plant ecologist Dr. DeMarche. Tina selected The Tipping Point Data Puzzle lesson and the related Moss Campion Phenology in the Rocky Mountains Puzzle Piece to help her students understand that all living things, including the Alpine Tundra plant moss campion, respond to a range of environmental conditions in different, and sometimes unexpected ways. Building from structured to open-ended data investigations, Data Puzzle resources provide students the chance to explore authentic datasets, both small and large, much like professional scientists and make discoveries of their own.