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Table. 1.

Results of Computational Physics Curriculum Analysis.

University A B C D E F G H I J K L M N total %
Category
Contents Digital literacy Programming skill 5 9 3 0 6 0 2 1 4 6 6 0 2 8 52 30.8
Data analysis 0 0 0 0 0 0 0 0 0 1 0 0 1 0 2 1.2
Understanding AI 2 2 0 0 0 0 0 0 0 1 0 0 4 2 11 6.5
Physics understanding Physics 4 0 0 0 0 0 4 0 0 0 0 15 2 0 25 14.8
Numerical analysis 1 0 8 0 0 0 4 2 0 4 6 0 4 0 29 17.2
Physics education Use of digital technology in educational contexts 0 0 0 10 2 2 0 6 5 0 0 0 0 0 25 14.8
Data visualization and simulation 2 4 2 0 0 3 0 0 4 0 0 0 0 0 15 8.9
Physical computing 0 0 0 0 5 3 0 0 0 0 0 0 0 2 10 5.9
Sub total 14 15 13 10 13 8 10 9 13 12 12 15 13 12 169 100.0
Level Secondary school 2 4 0 0 0 6 4 0 1 0 0 0 0 0 17 32.1
General physics 4 0 0 0 0 0 0 0 3 0 0 0 0 0 7 13.2
Undergraduate Year 2–4 0 1 5 0 0 0 0 0 0 4 1 15 3 0 29 54.7
Graduate school or higher 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00.0
Sub total 6 5 5 0 0 6 4 0 4 4 1 15 3 0 53 100.0
New Phys.: Sae Mulli 2023;73:437~448 https://doi.org/10.3938/NPSM.73.437
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