Integrating noise into PhET simulations to promote student learning of measurement uncertainty
Qiaoyi Liu, Matthew Blackman, Gayle Geschwind, Catherine Carter, Katherine K. Perkins, H. J. Lewandowski
公開日: 2025/9/30
Abstract
Understanding concepts and practices of measurement uncertainty is a core competency of physicists and engineers, and many physics lab courses aim to have students learn these ideas. However, there is strong evidence that these goals are often not met. To address the challenge of improving students' proficiency with measurement uncertainty concepts and practices, we designed and developed a new PhET simulation, Projectile Data Lab (PDL), featuring statistical noise and measurement tools in the context of projectile motion. We integrated this simulation into the Common Online Data Analysis Platform (CODAP), creating an instructional platform for collecting and analyzing data from the simulation, and designed three simulation-based instructional activities for instructors to use in their lab courses. We describe the pedagogical design of the new simulation, the PDL+CODAP instructional platform, and the associated instructional activities. We highlight how the targeted learning goals guided the pedagogical design, as well as how these three instructional tools (the simulation, the PDL+CODAP platform, and the lab activity) work together and leverage the affordances of each to scaffold learning. The goal of this work is to provide a model of how noise-enhanced simulations and activities can be designed to enhance student learning of measurement uncertainty.