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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
Phillips, A. M.; Gouvea, E. J.; Gravel, B. E.; Beachemin, P. -H.; Atherton, T. J. – Physical Review Physics Education Research, 2023
Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists' careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics…
Descriptors: Physics, Science Instruction, Teaching Methods, Models
Mashood, K. K.; Khosla, Kamakshi; Prasad, Arjun; V., Sasidevan; Ashefas CH, Muhammed; Jose, Charles; Chandrasekharan, Sanjay – Physical Review Physics Education Research, 2022
Recent educational policies advocate a radical revision of science curricula and pedagogy, to support interdisciplinary practices, a distinguishing feature of contemporary science. Computational modeling (CM) is a core methodology of interdisciplinary science, as such models allow intertwining of data and theoretical perspectives from multiple…
Descriptors: Teaching Methods, Undergraduate Students, Science Instruction, Science Curriculum

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