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physics.ed-ph2023

A method to assess trustworthiness of machine coding at scale

Rebeckah K. Fussell, Emily M. Stump, N. G. Holmes

Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data,…

physics.ed-ph2023

New perspectives on student reasoning about measurement uncertainty: More or better data

Andy Schang, Matthew Dew, Emily M. Stump +2

Uncertainty is an important and fundamental concept in physics education. Students are often first exposed to uncertainty in introductory labs, expand their knowledge across lab co…

physics.ed-ph2023

Comparing introductory and beyond-introductory students' reasoning about uncertainty

Emily M. Stump, Mark Hughes, Gina Passante +1

Uncertainty is an important concept in physics laboratory instruction. However, little work has examined how students reason about uncertainty beyond the introductory (intro) level…

physics.ed-ph2023

Context affects student thinking about sources of uncertainty in classical and quantum mechanics

Emily M. Stump, Matthew Dew, Gina Passante +1

Measurement uncertainty is an important topic in the undergraduate laboratory curriculum. Previous research on student thinking about experimental measurement uncertainty has focus…

physics.ed-ph2020

Student evaluation of more or better experimental data in classical and quantum mechanics

Courtney L. While, Emily M. Stump, N. G. Holmes +1

Prior research has shown that physics students often think about experimental procedures and data analysis very differently from experts. One key framework for analyzing student th…