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Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
Ethan C. Campbell; Katy M. Christensen; Mikelle Nuwer; Amrita Ahuja; Owen Boram; Junzhe Liu; Reese Miller; Isabelle Osuna; Stephen C. Riser – Journal of Geoscience Education, 2025
Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here…
Descriptors: Earth Science, Undergraduate Students, Programming Languages, Computer Software

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