Learning from the unexpected: Statistics and uncertainty in massively scalable online laboratories (MSOL)

摘要

Reasoning about unexpected outcomes is crucial to scientific exploration and STEM education. To stimulate conversations about these outcomes, we integrated noise characteristics and uncertainty into the framework of Massively Scalable Online Laboratories (MSOLs), which provide a collaborative and interactive online environment for education. The MSOL approach reduces a physical experiment to a set of states and displays it in an interactive environment. With the approach described here, those stages exhibit noise characteristics and nondeterministic outcomes. As a demonstration, we successfully recorded and displayed an example experiment, which contains quantum noise and limited repeatability. We thus display experiments more realistically and lead students towards a deeper understanding of the physical reality by evoking their curiosity about the variation of the displayed outcomes.