Integrasi Pemodelan Komputasional Berbasis Python dalam Pembelajaran Difraksi Gelombang pada Mahasiswa Pendidikan Fisika
DOI:
https://doi.org/10.31538/adrg.v6i1.3110Keywords:
Pemodelan Komputasional, Difraksi Gelombang, Pembelajaran, Fisika, PythonAbstract
Pemodelan komputasional semakin penting dalam pembelajaran fisika modern karena mampu menghubungkan konsep abstrak dengan visualisasi fenomena fisika secara dinamis. Penelitian ini bertujuan untuk menganalisis perubahan pemahaman konseptual mahasiswa melalui integrasi pemodelan komputasional berbasis Python pada materi difraksi gelombang serta mengidentifikasi indikasi awal praktik berpikir komputasional dalam aktivitas pembelajaran. Penelitian ini menggunakan desain one-group pretest–posttest dengan melibatkan 15 mahasiswa pendidikan fisika yang mengikuti mata kuliah Gelombang dan Optik. Data dikumpulkan melalui tes pemahaman konseptual, lembar aktivitas pemodelan komputasional, dan refleksi mahasiswa. Analisis data dilakukan menggunakan statistik deskriptif, uji normalitas Shapiro–Wilk, paired sample t-test, serta N-gain. Hasil penelitian menunjukkan adanya peningkatan rata-rata skor dari 67,50 pada pretest menjadi 71,73 pada posttest dengan perbedaan yang signifikan secara statistik (p = 0,012). Nilai N-gain sebesar 0,127 menunjukkan bahwa peningkatan pemahaman konseptual berada pada kategori rendah. Analisis kualitatif menunjukkan bahwa 73% mahasiswa mengalami perubahan penalaran dari penjelasan deskriptif menuju penalaran berbasis parameter model. Penelitian ini menunjukkan bahwa pendekatan low-threshold computational modeling dapat menjadi langkah awal yang efektif untuk mengintegrasikan praktik komputasi dalam pembelajaran fisika bagi mahasiswa yang memiliki pengalaman pemrograman terbatas.
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References
Aho, A. V. (2012). Computation and Computational Thinking. The Computer Journal, 55(7), 832–835. https://doi.org/10.1093/comjnl/bxs074
Aisah, N., Yuliani, H., & Nasir, M. (2024). Meta Analisis: Pengaruh Multimedia Interaktif Terhadap Pemahaman Konsep IPA. Kappa Journal, 8(2), 249–254. https://doi.org/10.29408/kpj.v8i2.26294
Caballero, M. D., Kohlmyer, M. A., & Schatz, M. F. (2012). Implementing and assessing computational modeling in introductory mechanics. Physical Review Special Topics - Physics Education Research, 8(2), 020106. https://doi.org/10.1103/PhysRevSTPER.8.020106
Chabay, R., & Sherwood, B. (2008). Computational physics in the introductory calculus-based course. American Journal of Physics, 76(4), 307–313. https://doi.org/10.1119/1.2835054
Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., Reid, S., & LeMaster, R. (2005). When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physical Review Special Topics - Physics Education Research, 1(1), 010103. https://doi.org/10.1103/PhysRevSTPER.1.010103
Gambrell, J., & Brewe, E. (2024). Analyzing interviews on computational thinking for introductory physics students: Toward a generalized assessment. Physical Review Physics Education Research, 20(1), 010128. https://doi.org/10.1103/PhysRevPhysEducRes.20.010128
Ginting, F. W., Novita, N., & Rahmadhani, Y. (2020). PENERAPAN MODEL TGT MELALUI SIMULASI PhET TERHADAP PENINGKATAN PEMAHAMAN SISWA PADA ALAT-ALAT OPTIK. Relativitas: Jurnal Riset Inovasi Pembelajaran Fisika, 3(2), 1–9. https://doi.org/10.29103/relativitas.v3i2.3341
Hutchins, N. M., Biswas, G., Zhang, N., Snyder, C., Lédeczi, Á., & Maróti, M. (2020). Domain-Specific Modeling Languages in Computer-Based Learning Environments: A Systematic Approach to Support Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education, 30(4), 537–580. https://doi.org/10.1007/s40593-020-00209-z
Ilma, A. Z., Wilujeng, I., Nurtanto, M., & Kholifah, N. (2023). A Systematic Literature Review of STEM Education in Indonesia (2016-2021): Contribution to Improving Skills in 21st Century Learning. Pegem Journal of Education and Instruction, 13(2), 134–146. https://doi.org/10.47750/pegegog.13.02.17
Irvani, A. I., Rochintaniawati, D., Riandi, R., & Sinaga, P. (2024). Analyzing the Integration of Computational Thinking in Science and Physics Education within the Indonesian Curriculum. Kasuari: Physics Education Journal (KPEJ), 7(1), 182–194. https://doi.org/10.37891/kpej.v7i1.620
Koryataini, L., Sumo, M., Minnah, L., Solehah, S., & Khoiroh, A. R. A. (2024). Analisis Penggunaan Media Pembelajaran PhET pada Materi Gelombang Berjalan dan Stasioner: A Review Literatur. MUTIARA: Jurnal Ilmiah Multidisiplin Indonesia, 2(3), 120–138. https://doi.org/10.61404/jimi.v2i3.256
Lagubeau, G., Tecpan, S., & Hernández, C. (2020). Active learning reduces academic risk of students with nonformal reasoning skills: Evidence from an introductory physics massive course in a Chilean public university. Physical Review Physics Education Research, 16(2), 023101. https://doi.org/10.1103/PhysRevPhysEducRes.16.023101
Lutfia, W., & Putra, N. M. D. (2020). Analisis Profil Pemahaman Konsep dan Model Mental Siswa di SMA Kesatrian 2 Semarang pada Materi Interferensi dan Difraksi Cahaya. UPEJ Unnes Physics Education Journal, 9(1), 27–35. https://doi.org/10.15294/upej.v9i1.38278
Mešić, V., Neumann, K., Aviani, I., Hasović, E., Boone, W. J., Erceg, N., Grubelnik, V., Sušac, A., Glamočić, D. S., Karuza, M., Vidak, A., Alihodžić, A., & Repnik, R. (2019). Measuring students’ conceptual understanding of wave optics: A Rasch modeling approach. Physical Review Physics Education Research, 15(1), 010115. https://doi.org/10.1103/PhysRevPhysEducRes.15.010115
Odden, T. O. B., Lockwood, E., & Caballero, M. D. (2019). Physics computational literacy: An exploratory case study using computational essays. Physical Review Physics Education Research, 15(2). https://doi.org/10.1103/PhysRevPhysEducRes.15.020152
Phillips, A. M., Gouvea, E. J., Gravel, B. E., Beachemin, P.-H., & Atherton, T. J. (2023). Physicality, modeling, and agency in a computational physics class. Physical Review Physics Education Research, 19(1), 010121. https://doi.org/10.1103/PhysRevPhysEducRes.19.010121
Rabiudin, Afifi, E. H. N., Hastuti, T. W., & Nisa, D. C. (2023). Computational Thinking Skills to Solve Kinematics Problems at High Cognitive Level Cases. Jurnal Penelitian Pendidikan IPA, 9(12), 10955–10964. https://doi.org/10.29303/jppipa.v9i12.5775
Riazy, S., Weller, S., & Simbeck, K. (2020). Evaluation of Low-threshold Programming Learning Environments for the Blind and Partially Sighted: Proceedings of the 12th International Conference on Computer Supported Education, 366–373. https://doi.org/10.5220/0009448603660373
Ropinur, M., Anggraini, S., & Angin, S. (2025). Peran Simulasi PHET dalam Meningkatkan Pemahaman Mahasiswa Tentang Fenomena Dualisme Partikel-Gelombang. JGK (Jurnal Guru Kita), 9, 674–682. https://doi.org/10.24114/jgk.v9i3.64315
Saba, J., Hel-Or, H., & Levy, S. T. (2023). Much.Matter.in.Motion: Learning by modeling systems in chemistry and physics with a universal programing platform. Interactive Learning Environments, 31(5), 3128–3147. https://doi.org/10.1080/10494820.2021.1919905
Saridawati, S., Muinah, M., & Dinata, K. B. (2022). Pengembangan Media Pembelajaran Interaktif Menggunakan Aurora 3D Presentation 2012 terhadap Pemahaman Konsep Materi Bangun Ruang Siswa Kelas VIII SMP Negeri 10 Kotabumi. Eksponen, 12(2), 63–73. https://doi.org/10.47637/eksponen.v12i2.633
Vieyra, R. E., Megowan-Romanowicz, C., Fisler, K., Lerner, B. S., Politz, J. G., & Krishnamurthi, S. (2024). Expanding Models for Physics Teaching: A Framework for the Integration of Computational Modeling. Education Sciences, 14(8). https://doi.org/10.3390/educsci14080861
Weber, J., & Wilhelm, T. (2020). The benefit of computational modelling in physics teaching: A historical overview. European Journal of Physics, 41(3), 034003. https://doi.org/10.1088/1361-6404/ab7a7f
Weller, D. P., Bott, T. E., Caballero, M. D., & Irving, P. W. (2022). Development and illustration of a framework for computational thinking practices in introductory physics. Physical Review Physics Education Research, 18(2), 020106. https://doi.org/10.1103/PhysRevPhysEducRes.18.020106
Yeni, S., Nijenhuis-Voogt, J., Saeli, M., Barendsen, E., & Hermans, F. (2024). Computational thinking integrated in school subjects – A cross-case analysis of students’ experiences. International Journal of Child-Computer Interaction, 42, 100696. https://doi.org/10.1016/j.ijcci.2024.100696
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