Harnessing Data Science to Drive Student Learning in Patterns Physics

The Patterns Approach (Hill Citation2013) engages students with key characteristics of data science through its learning model. This model begins with real-world phenomena and engages students in data collection and analysis. This is followed by a data discussion that guides students in making sense of the data and developing a mathematical model of the system. This model can then be tested by using it to predict new scenarios within the system. Built on A Framework for K12 Science Education, the course has undergone multiple iterations to meet the NGSS and the needs of teachers and students in 3D learning. The approach has now been applied to chemistry and biology courses, with earth science and engineering design woven in, providing a 3-year open-source sequence available at hsscience4all.org. This article aims to share two anchoring experiments in Unit 1 of Patterns Physics (a pendulum and a rolling ball) that demonstrate how the Patterns Approach uses data science as a driver for student learning. The Patterns Sequence provides a learning progression that supports students’ sensemaking and the ability to apply patterns in data to explain phenomena and predict (calculate) future scenarios within the system of study.