IMPLEMENTATION OF JUPYTER APPLICATION TO IMPROVE PHYSICS LEARNING AT UNIVERSITAS BUDI DARMA
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Published:
Jul 12, 2025
DOI:
https://doi.org/10.54209/edumaniora.v4i01.93
Keywords:
Jupyter Notebook, Basic Physics, Interactive Learning, Simulation, Visualization
Section
Articles
Statistics Article
Article View : 34 Times
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Siti Nurhabibah Hutagalung
Universitas Budi Darma
Abstract
Physics learning often faces challenges in conveying abstract and complex concepts to students. To overcome this obstacle, the use of computing-based technology can be an effective solution. One potential technology is Jupyter Notebook, a web-based application that supports a variety of programming languages such as Python. This research aims to improve the effectiveness of Basic Physics learning by implementing Jupyter Notebook in the teaching process. The methods used include training, development of teaching materials, and learning evaluation. The results showed significant improvements in concept understanding, computational skills, and student involvement in the learning process.
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Castilla, Robert, and Marta Peña. 2023. “Jupyter Notebooks for the Study of Advanced Topics in Fluid Mechanics.” Computer Applications in Engineering Education 31(4): 1001–13.
Charles, Tessa, and Carl Gwilliam. 2023. “The Effect of Automated Error Message Feedback on Undergraduate Physics Students Learning Python: Reducing Anxiety and Building Confidence.” Journal for STEM Education Research 6(2): 326–57.
Du, Dou et al. 2024. “Jupyter Widgets and Extensions for Education and Research in Computational Physics and Chemistry.” Computer Physics Communications 305(December 2023): 109353. https://doi.org/10.1016/j.cpc.2024.109353.
González-Carrillo, Cristian D., Felipe Restrepo-Calle, Jhon J. Ramírez-Echeverry, and Fabio A. González. 2021. “Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses.” Sustainability (Switzerland) 13(21): 1–26.
Kim, Brian, and Graham Henke. 2021. “Easy-to-Use Cloud Computing for Teaching Data Science.” Journal of Statistics and Data Science Education 29(S1): S103–11. https://doi.org/10.1080/10691898.2020.1860726.
Laipaka, Robertus, Nova Mustika, and Oktivianus Rii Runda. 2021. "Application of Jupyter Notebook on Anaconda Navigator for Data Visualization (Case Study: Titanic)." Proceedings of the National Seminar on Community Service 1(1): 388–95. https://ejournal.raharja.ac.id/index.php/corisindo/article/view/2438.
Lane, W. Brian, Terrie M. Galanti, and X. L. Rozas. 2023. “Teacher Re-Novicing on the Path to Integrating Computational Thinking in High School Physics Instruction.” Journal for STEM Education Research 6(2): 302–25. https://doi.org/10.1007/s41979-023-00100-1.
Nikitin, Nikolay O. et al. 2022. “Hybrid and Automated Machine Learning Approaches for Oil Fields Development: The Case Study of Volve Field, North Sea.” Computers and Geosciences 161(December 2020): 105061. https://doi.org/10.1016/j.cageo.2022.105061.
Pimentel, João Felipe, Leonardo Murta, Vanessa Braganholo, and Juliana Freire. 2021. “Understanding and Improving the Quality and Reproducibility of Jupyter Notebooks.” Empirical Software Engineering 26(4).
Purnama, A. Yoga et al. 2023. “Particle Trajectory Simulation Using Python and Spreadsheet as an Online Learning Alternative.” Revista Mexicana de Fisica E 20(2): 1–6.
TOPSAKAL, Oguzhan. 2023. “Teaching Algorithms Design Approaches via Interactive Jupyter Notebooks.” European Journal of Technic 13(1): 1–6.
Tufino, Eugenio, Stefano Oss, and M Alemani. 2024a. “Using Jupyter Notebooks to Foster Computational Skills and Professional Practice in an Introductory Physics Lab Course.” 05(24): 1–10. http://arxiv.org/abs/2405.16675.
Tufino, Eugenio, Stefano Oss, and Micol Alemani. 2024b. “Integrating Python Data Analysis in an Existing Introductory Laboratory Course.” European Journal of Physics 45(4): 1–13.
Vallejo, William, Carlos Díaz-Uribe, and Catalina Fajardo. 2022. “Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students.” ACS Omega 7(8): 7421–29.