Evaluating a Computer Vision Enhancement of the Millikan Oil-Drop Experiment

Caleb Christian, Ethan Seefried, Nathaniel Blanchard

Abstract: The Millikan oil-drop experiment has served as a foundational element in physics education, effectively demonstrating the quantization of electric charge through meticulous measurement techniques. Despite its significant pedagogical contributions, the conventional implementation of this experiment remains suboptimal, predominantly relying on outdated software, manual tracking, and physical hardware. These limitations reduce usability, increase the risk of errors, consume significant time, and require monetary investment. This paper introduces a contemporary approach by presenting the Millikan Automated System for Education (M.A.S.E.), an application that incorporates Human-Computer Interaction (HCI) principles and Computer Vision (CV), alongside the MillikanCV dataset. A comparative user study between traditional methodologies and M.A.S.E. evaluated usability using the Usability Metric for User Experience (UMUX) scale, accuracy based on the error rate relative to the accepted value of the electron (e), and efficiency measured by the time required to analyze a single particle. The study, using Wilcoxon Signed-Rank Tests, revealed substantial enhancements in all three areas, with effect sizes of \(r = 0.86\) for usability, \(r = -0.84\) for accuracy, and \(r = -0.88\) for efficiency. The M.A.S.E. application and the MillikanCV dataset are publicly accessible at https://github.com/Blanchard-lab/MillikanCV/tree/main.

External IDs:doi:10.1007/978-3-031-93965-5_11