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Design, Implementation, and Analysis of an Algebra-based Treatment of Measurement Uncertainty

Abstract

Learning to measure and propagate uncertainty is a necessity for understanding experimental data. But because statistics and calculus are required for a rigorous analysis of uncertainty, students in algebra-based physics classes are rarely able to determine the meaning of their experimental results. This thesis describes the design and implementation of an algebra-based method for calculating, propagating, and determining agreement with measurement uncertainty that was taught at scale to students of introductory physics for biologists. Changes to the previous lab implementation are described, including the additional of learning goals, in-class checkpoint questions, lab write-up prompts designed around uncertainty, and TA training. Student attitudes towards physics are measured in different instructional circumstances using the Colorado Learning About Science Survey. Several different methods of measuring how well the students learned the technique and the meaning of uncertainty are analyzed. Students showed more comfort and ability in using estimation and spread to determine ruler lengths after instruction. Students were more likely to use spread-based approaches in computer collected data. Students who performed the uncertainty propagation procedure were more likely (97% to 28%) use their calculated uncertainty in data comparisons. Different framings of uncertainty are compared. Since biology students also take lower division lab classes in chemistry, the complementary approach that each science takes in lab instruction is explored from a course design perspective and via student interviews.

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