Two-Dimensional Visualization of Solid Expansion Using Spyder as a Physics Learning Medium in Secondary Schools
DOI:
https://doi.org/10.37891/kpej.v9i1.1180Abstract
Physics education in secondary schools still faces several obstacles, particularly in understanding the concepts of temperature and heat, one of which is the expansion of solids. The main problem students face is difficulty visualizing how the size of solids changes as temperatures rise, so learning tends to consist only of memorizing formulas without a deep understanding. This study aims to explain the use of two-dimensional (2D) solid expansion graphs using Python-based Spyder software as a medium for physics learning. The method used is simulation and mathematical modeling of area expansion based on physical equations, with the results displayed as graphs. The simulation shows a linear relationship between temperature changes and the area of a solid object, in accordance with the area-expansion theory . Graph visualization helps students understand how temperature affects changes in object size and how expansion varies across materials, depending on their respective coefficients of thermal expansion. In addition, Spyder encourages active student participation, improves critical thinking skills, and provides a basic introduction to programming. Thus, the use of solid expansion graphs in Spyder can be an effective, interactive, and relevant alternative learning medium to improve the quality of physics learning at the secondary school level.
References
Afshin, S., & Yas, M. H. (2026). Study on Thermal, Mechanical, and Dynamic Properties of Epoxy Matrix with a Hybrid of Nanoclay/Carbon Nanotube. Mechanics of Advanced Composite Structures, 13(1), 37–56. https://doi.org/10.22075/MACS.2025.34769.1698
Al-Hazmi, G. H., Albedair, L. A., Alatawi, R. A. S., Alnawmasi, J. S., Alsuhaibani, A. M., & El-Desouky, M. G. (2024). Enhancing Trimethoprim Pollutant Removal from Wastewater Using Magnetic Metal-Organic Framework Encapsulated with Poly (Itaconic Acid)-Grafted Crosslinked Chitosan Composite Sponge: Optimization Through Box-Behnken Design and Thermodynamics of Adsorption. International Journal of Biological Macromolecules, 268(1), 189-197. https://doi.org/10.1016/j.ijbiomac.2024.131947
Anwar, A., Hasan, S., & Haerani, E. (2023). Increasing Student Learning Enthusiasm through Multimedia-Based Learning. AL-ISHLAH: Jurnal Pendidikan, 15(3), 3210–3217. https://doi.org/10.35445/alishlah.v15i3.3430
Az-zahra, P. L., Rohim, F., Amarulloh, R. R., Nanto, D., & Herpi, A. N. (2024). Comparative Study between the PhET Colorado Virtual Laboratory and the Applets on the Photoelectric Effect. Kasuari: Physics Education Journal (KPEJ), 7(2), 289–299. https://doi.org/10.37891/kpej.v7i2.644
Barak, M. (2010). Motivating Self-Regulated Learning in Technology Education. International Journal of Technology and Design Education, 20(4), 381–401. https://doi.org/10.1007/s10798-009-9092-x
Bates, K. E., Smith, M. L., Farran, E. K., & Machizawa, M. G. (2024). Behavioral and Neural Correlates of Visual Working Memory Reveal Metacognitive Aspects of Mental Imagery. Journal of Cognitive Neuroscience, 36(2), 272–289. https://doi.org/10.1162/jocn_a_02085
Binali, T., Chang, C., Chang, Y. J., & Chang, H. Y. (2024). High School and College Students’ Graph-Interpretation Competence in Scientific and Daily Contexts of Data Visualization. Science and Education, 33(3), 763–785. https://doi.org/10.1007/s11191-022-00406-3
Bungum, B., & Selstø, Sø. (2022). What Do Quantum Computing Students Need to Know about Quantum Physics? European Journal of Physics, 43(5), 89-93. https://doi.org/10.1088/1361-6404/ac7e8a
Byrne, S., Allen, A., Stavropoulos, V., & Kannis-Dymand, L. (2023). Problematic Gaming: The Role of Desire Thinking, Metacognition, and the Proteus Effect. Behaviour and Information Technology, 42(10), 1453–1465. https://doi.org/10.1080/0144929X.2022.2081092
Celestino-Salcedo, R. K. M., Malayao, S. O., Salic-Hairulla, M. A., Castro, E. J., & Mordeno, I. C. V. (2024). Vodcast Embedded with Physics Education Technology Simulation in Learning Projectile Motion. Journal of Education and Learning, 18(3), 1047–1055. https://doi.org/10.11591/edulearn.v18i3.21434
Dong, P., Teng, Z., Xie, J., Zhang, J., Xiong, D., & Chen, D. (2026). Effects of SiO2/Al2O3 Ratios on Microstructure, Properties, and Elastic Modulus of SiO2-Al2O3-CaO-MgO Alkali-Free Glass. Journal Wuhan University of Technology, Materials Science Edition, 41(1), 45–53. https://doi.org/10.1007/s11595-026-3223-z
Dove, M. T., & Fang, H. (2016). Negative Thermal Expansion and Associated Anomalous Physical Properties: Review of the Lattice Dynamics Theoretical Foundation. Reports on Progress in Physics, 79(6), 114-123. https://doi.org/10.1088/0034-4885/79/6/066503
Fauza, N., Nurvana, R., & Nasir, M. (2025). Transformation of Learning Kinematics of Rectilinear Motion: Essential Microcontroller-Based Experimental Tools to Improve High School Students’ Understanding of Concepts. Kasuari: Physics Education Journal (KPEJ), 8(1), 84–93. https://doi.org/10.37891/kpej.v8i1.790
García-Belmonte, G. (2017). Visualizing Time: How Linguistic Metaphors are Incorporated Into Displaying Instruments in the Process of Interpreting Time-Varying Signals. Cultural Studies of Science Education, 12(2), 369–385. https://doi.org/10.1007/s11422-015-9686-4
Gok, T. (2013). The Effects of Pen-Based Technology on Undergraduate Physics Students’ Motivation, Learning Strategies, and Achievement. Energy Education Science and Technology Part B: Social and Educational Studies, 5(1), 295–308. https://avesis.deu.edu.tr/yayin/1f44b1ae-ffcd-46bc-913d-01abfe41e993/the-effects-of-pen-based-technology-on-undergraduate-physics-students-motivation-learning-strategies-and-achievement
Haryandi, S., Misbah, M., Mastuang, M., Dewantara, D., & Mahtari, S. (2019). Analysis of Students' Critical Thinking Skills on Solid Material Elasticity. Kasuari: Physics Education Journal (KPEJ), 2(2), 89-94. https://doi.org/10.37891/kpej.v2i2.95
Hernández, C. A., Prada Núñez, R., & Gamboa, A. A. (2021). Gains in Active Learning of Physics: A Measurement Applying the Test of Understanding Graphs of Kinematics. Journal of Physics: Conference Series, 2073(1), 1-8. https://doi.org/10.1088/1742-6596/2073/1/012003
Hill, M., Sharma, M. D., & Johnston, H. M. (2015). How Online Learning Modules Can Improve the Representational Fluency and Conceptual Understanding of University Physics Students. European Journal of Physics, 36(4), 78-86. https://doi.org/10.1088/0143-0807/36/4/045019
Jebur, G., Al-Samarraie, H., & Alzahrani, A. I. (2022). An Adaptive Metalearner-Based Flow: A Tool for Reducing Anxiety and Increasing Self-Regulation. User Modeling and User-Adapted Interaction, 32(3), 469–501. https://doi.org/10.1007/s11257-022-09330-1
Jegede, O. J., & Okebukola, P. A. (1989). Some Socio-Cultural Factors Militating Against the Drift Towards Science and Technology in Secondary Schools. Research in Science and Technological Education, 7(2), 141–151. https://doi.org/10.1080/0263514890070203
Jhamb, S., Gupta, R., Shukla, V. K., Mearaj, I., & Agarwal, P. (2020). Understanding complexity in language learning through data visualization using Python. In 2020 International Conference on Computing, Communication and Knowledge Management (ICCCKM) (pp. 268–274). IEEE. https://doi.org/10.1109/ICCAKM46823.2020.9051512
Juneja, A., Kumar, V. R., Chandel, P., Kumari, P., Gola, K. K., & Painuli, D. (2026). Applications of Various Python Libraries for Data Visualization. In Lecture Notes in Networks and Systems (Vol. 1741). Springer. https://doi.org/10.1007/978-3-032-12827-0_22
Kadan-Tabaja, A., & Yerushalmy, M. (2024). Automated Assessment of Students’ Interactions with Visual Representations: A Metacognitive Perspective on Comparing Fractions. Computers in the Schools, 78(1), 567-574. https://doi.org/10.1080/07380569.2024.2390406
Kadiyala, A., & Kumar, A. (2017). Applications of Python to Evaluate Environmental Data Science Problems. Environmental Progress and Sustainable Energy, 36(6), 1580–1586. https://doi.org/10.1002/ep.12786
Kulindala, G., Srikanth, A., & Choppella, V. (2025). A Scaffolded Approach for Tracing Control-Flow in Simplified Python Programs. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1, 51–57. https://doi.org/10.1145/3724363.3729044
Lafuente, D., Cohen, B., Fiorini, G., García, A. A., Bringas, M., Morzán, E. M., & Onna, D. (2021). A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling. Journal of Chemical Education, 98(9), 2892–2898. https://doi.org/10.1021/acs.jchemed.1c00142
Li, K., Du, J. Q., Wang, C., Yang, G., Kang, J., & Chen, Y. (2026). A Zero-Dimensional Hybrid Manganese Bromide with Long Luminescence Lifetime, Temperature-Dependent Emissions, and Glass Transition Behavior. Journal of Molecular Structure, 1351(2), 56-67. https://doi.org/10.1016/j.molstruc.2025.144319
Liang, Z., Ga, R., Bai, H., Zhao, Q., Wang, G., Lai, Q., Chen, S., Yu, Q., & Zhou, Z. (2025). Teaching Expectancy Improves Video-Based Learning: Evidence from Eye-Movement Synchronization. British Journal of Educational Technology, 56(1), 231–249. https://doi.org/10.1111/bjet.13496
Liu, D., Oldenhof, H., Sieme, H., & Wolkers, W. F. (2026). Temperature-Scanning Infrared Spectroscopic Analysis of Sugar Glasses using Principal Component Analysis. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 349(4), 156-168. https://doi.org/10.1016/j.saa.2025.127346
Maharajan, S., Prabu Ram, G., Sakthi Priya, G., & Lingadurai, K. (2022). Prediction of Temperature Distribution in Three-Dimensional Solid Objects using Comsol and Python. Materials Today: Proceedings, 66(1), 1241–1246. https://doi.org/10.1016/j.matpr.2022.05.062
Manu, F. A., Ramaila, S., & Ramnarain, U. D. (2025). The Role of Arduino-Assisted Robotics Coding Application in Physics Learning: A Systematic Review. International Journal of Science, Mathematics and Technology Learning, 32(2), 189–211. https://doi.org/10.18848/2327-7971/CGP/v32i02/189-211
Martin, K., Lehmann, M., & Reese, S. (2026). Modeling of Polymer Seals at High Cryogenic Temperatures Across the Glass Transition. Journal of Applied Mechanics, 93(2), 45-56. https://doi.org/10.1115/1.4070498
Molin, F., Haelermans, C., Cabus, S., & Groot, W. (2020). The Effect of Feedback on Metacognition - A Randomized Experiment using Polling Technology. Computers and Education, 152(3), 67-78. https://doi.org/10.1016/j.compedu.2020.103885
Najafi, H. R., Abbasian, M., & Golrokh Sani, A. (2026). The Temperature Integral in Solid State Non-Isothermal Kinetics: A Comprehensive Review, Critical Evaluation, and Error Analysis of Approximation Methods. Journal of Analytical and Applied Pyrolysis, 193(3), 54-67. https://doi.org/10.1016/j.jaap.2025.107449
Nie, H. (2021). Using Spyder to Construct a Digit Recognition System. In 2021 International Conference on Computer Automation (pp. 79–83). IEEE. https://doi.org/10.1109/CompAuto54408.2021.00022
Norman, E., & Furnes, B. (2016). The Relationship between Metacognitive Experiences and Learning: Is There a Difference Between Digital and Non-Digital Study Media? Computers in Human Behavior, 54(2), 301–309. https://doi.org/10.1016/j.chb.2015.07.043
Perry, J. D., Erukhimova, T. L., & Bassichis, W. H. (2019). New Video Resource for Calculus-Based Introductory Physics, Design, and Assessment. I. Electricity and magnetism. American Journal of Physics, 87(5), 335–340. https://doi.org/10.1119/1.5095140
Rais, D., & Xuezhi, Z. (2024). Elevating Student Engagement and Academic Performance: A Quantitative Analysis of Python Programming Integration in the Merdeka Belajar Curriculum. Journal on Mathematics Education, 15(2), 495–516. https://doi.org/10.22342/jme.v15i2.pp495-516
Rizal, R., Rusdiana, D., Setiawan, W., & Siahaan, P. (2020). Students’ Perceptions of a Learning Management System-Supported Smartphone: Satisfaction Analysis in Online Physics Learning. Jurnal Pendidikan IPA Indonesia, 9(4), 600–610. https://doi.org/10.15294/jpii.v9i4.25363
Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and Texting Made Me Do It: Media-Induced Task-Switching while Studying. Computers in Human Behavior, 29(3), 948–958. https://doi.org/10.1016/j.chb.2012.12.001
Sari, D., & Madlazim. (2022). Student Learning Experience and Their Academic Performance in a Flipped Classroom: The Mediating Role of Self-Regulated Learning and Computer Simulation. Eurasian Journal of Educational Research, 2022(101), 204–221. https://doi.org/10.14689/ejer.2022.101.012
Schnieder, M., & Williams, S. R. (2023). Educational Mobile Apps for Programming in Python: Review and Analysis. Education Sciences, 13(1), 90-98. https://doi.org/10.3390/educsci13010066
She, H.-C., Cheng, M.-T., Li, T.-W., Wang, C.-Y., Chiu, H.-T., Lee, P.-Z., Chou, W.-C., & Chuang, M.-H. (2012). Web-Based Undergraduate Chemistry Problem-Solving: The Interplay of Task Performance, Domain Knowledge, and Web-Searching Strategies. Computers and Education, 59(2), 750–761. https://doi.org/10.1016/j.compedu.2012.02.005
Stanciulescu, A., Castronovo, F., & Oliver, J. (2024). Assessing the Impact of Visualization Media on Engagement in an Active Learning Environment. International Journal of Mathematical Education in Science and Technology, 55(5), 1150–1170. https://doi.org/10.1080/0020739X.2022.2044530
Su, X., Wang, J., Zheng, C., & Li, X. (2026). Fast UV-Curable Elastic Composite Films with Good Thermal, Mechanical, and Dielectric Performances for Flexible Electronics. Journal of Applied Polymer Science, 143(7), 143-158. https://doi.org/10.1002/app.70040
Sukarno, & Widdah, M. E. (2020). The Effects of Students’ Metacognition and Digital Literacy On Achievement in the “Methods and Strategies on Physics Learning” Course During the COVID-19 Pandemic. Jurnal Pendidikan IPA Indonesia, 9(4), 477–488. https://doi.org/10.15294/jpii.v9i4.25332
Swandi, A., Rahmadhanningsih, S., Yusuf, I., & Widyaningsih, S. W. (2021). Exploring the Compton Scattering Phenomenon with Virtual Learning Under a Project-Based Learning Model (PjBL). Kasuari: Physics Education Journal (KPEJ), 4(1), 1–12. https://doi.org/10.37891/kpej.v4i1.159
Veerasamy, B. D. (2024). Python Data Analysis and Visualization in Java GUI Applications through TCP Socket Programming. International Journal of Information Technology and Computer Science, 16(3), 72–92. https://doi.org/10.5815/ijitcs.2024.03.07
Viennot, L. (2020). Developing Critical Analysis of Explanations in Physics Teachers: Which Direction to Take? Physics Education, 55(1), 1-8. https://doi.org/10.1088/1361-6552/ab4f64
Wang, S., Liu, D., Wang, N., & Yuan, Y. (2020). Design and Implementation of an Online Python Teaching Case Library for the Training of Application-Oriented Talents. International Journal of Emerging Technologies in Learning, 15(21), 217–230. https://doi.org/10.3991/inet.v15i21.18191
Werth, A., West, C. G., & Lewandowski, H. J. (2022). Impacts on Student Learning, Confidence, and Affect in a Remote, Large-Enrollment, Course-Based Undergraduate Research Experience in Physics. Physical Review Physics Education Research, 18(1), 256-267. https://doi.org/10.1103/PhysRevPhysEducRes.18.010129
Yani, M., Mastuang, M., & Misbah, M. (2021). Development of Solid Elasticity Modules with a Guided Inquiry Model to Train Critical Thinking Skills. Kasuari: Physics Education Journal (KPEJ), 4(1), 44–56. https://doi.org/10.37891/kpej.v4i1.213
Yusuf, I., & Widyaningsih, S. W. (2020). Implementing E-Learning-Based Virtual Laboratory Media to Students’ Metacognitive Skills. International Journal of Emerging Technologies in Learning, 15(5), 63–74. https://doi.org/10.3991/ijet.v15i05.12029
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