Finding the Acceleration Due to Gravity Using the Hydrostatic Pressure Simulation of PhET
DOI:
https://doi.org/10.47540/ijias.v5i1.1742Keywords:
Gravity Measurement, Hydrostatic Pressure, PhET SimulationsAbstract
This study explored the concept of hydrostatic pressure and its relationship with various heights of water using the PhET simulation platform. This research aims to determine the value acceleration due to gravity using the hydrostatic pressure simulation of PhET. The experiment's independent variable is the water column at various heights and the dependent variable is the pressure. The method used in this study is pure experimental wherein controlled variables like atmospheric pressure were kept constant. By systematically varying the water height above the ground, the experiment examined how pressure changes respond to these variations. A straight line of best fit was formed when pressure and height were plotted, which is consistent with the theory. This result indicated that as the height of the water column increases, the pressure increases proportionally, demonstrating the direct influence of gravity and water density on hydrostatic pressure. Also, the acceleration due to gravity was measured to be 9.82 ms-2. Therefore, the following were afforded by PhET simulation in this experiment: reliable data, convenient usage, eliminating the need for sophisticated equipment, and an intuitive interface for exploring physical phenomena. This study recommends PhET for teaching and learning processes. It engages the students and provides experiential learning to teachers and students.
References
Alam, A. (2022). Social robots in education for long-term human-robot interaction: socially supportive behaviour of robotic tutor for creating robo-tangible learning environment in a guided discovery learning interaction. ECS Transactions, 107(1), 12389.
Allan, B. (2016). Emerging strategies for supporting student learning: A practical guide for librarians and educators. Facet Publishing.
Asad, M. M., Naz, A., Churi, P., & Tahanzadeh, M. M. (2021). Virtual reality as pedagogical tool to enhance experiential learning: a systematic literature review. Education Research International, 2021(1), 7061623.
Azmandian, M., Yahata, R., Grechkin, T., Thomas, J., & Rosenberg, E. S. (2022). Validating simulation-based evaluation of redirected walking systems. IEEE Transactions on Visualization and Computer Graphics, 28(5), 2288-2298.
Bair, S. S. (2019). High pressure rheology for quantitative elastohydrodynamics. Elsevier.
Bratley, P., Fox, B. L., & Schrage, L. E. (2011). A guide to simulation. Springer Science & Business Media.Graver, D. K. (2016). Scuba Diving, 5E. Human Kinetics.
Carr, S., Fang, S., Jarillo-Herrero, P., & Kaxiras, E. (2018). Pressure dependence of the magic twist angle in graphene superlattices. Physical Review B, 98(8), 085144.
Cavusoglu, A. H., Chen, X., Gentine, P., & Sahin, O. (2017). Potential for natural evaporation as a reliable renewable energy resource. Nature communications, 8(1), 617.
Cui, P., Zeng, C., & Lei, Y. (2015). Experimental analysis on the impact force of viscous debris flow. Earth Surface Processes and Landforms, 40(12), 1644-1655.
Delgado, J. M. D., Oyedele, L., Demian, P., & Beach, T. (2020). A research agenda for augmented and virtual reality in architecture, engineering and construction. Advanced Engineering Informatics, 45, 101122.
Dinçer, I. (2020). Thermodynamics: a smart approach. John Wiley & Sons.
Dy, A. U., Lagura, J. C., & Baluyos, G. R. (2024). Using PhET Interactive Simulations to Improve the Learners’ Performance in Science. EduLine: Journal of Education and Learning Innovation, 4(4), 520-530.
Falloon, G. (2019). Using simulations to teach young students science concepts: An Experiential Learning theoretical analysis. Computers & Education, 135, 138-159.
Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022(1), 5215722.
Hughes, M. T., Kini, G., & Garimella, S. (2021). Status, challenges, and potential for machine learning in understanding and applying heat transfer phenomena. Journal of Heat Transfer, 143(12), 120802.
Jarvis, N. J. (2020). A review of non‐equilibrium water flow and solute transport in soil macropores: Principles, controlling factors and consequences for water quality. European Journal of Soil Science, 71(3), 279-302.
Kılınc, S. (2023). Embracing the future of distance science education: Opportunities and challenges of ChatGPT integration. Asian Journal of Distance Education, 18(1), 205-237.
Konrad, R., Cooper, E. A., & Wetzstein, G. (2016, May). Novel optical configurations for virtual reality: Evaluating user preference and performance with focus-tunable and monovision near-eye displays. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 1211-1220).
Learning, L. (2021). Variation of Pressure with Depth in a Fluid. Fundamentals of Heat, Light & Sound. https://pressbooks.nscc.ca/heatlightsound/chapter/11-4-variation-of-pressure-with-depth-in-a-fluid/
Li, W., Nee, A. Y., & Ong, S. K. (2017). A state-of-the-art review of augmented reality in engineering analysis and simulation. Multimodal Technologies and Interaction, 1(3), 17.
LibreTexts. (2024, October 1). 11.4: Variation of pressure with depth in a fluid. Physics LibreTexts. https://phys.libretexts.org/Bookshelves/College_Physics/College_Physics_1e_(OpenStax)/11%3A_Fluid_Statics/11.04%3A_Variation_of_Pressure_with_Depth_in_a_Fluid
Lindgren, R., Tscholl, M., Wang, S., & Johnson, E. (2016). Enhancing learning and engagement through embodied interaction within a mixed reality simulation. Computers & Education, 95, 174-187.
McEnaney, J. M., Singh, A. R., Schwalbe, J. A., Kibsgaard, J., Lin, J. C., Cargnello, M., ... & Nørskov, J. K. (2017). Ammonia synthesis from N 2 and H 2 O using a lithium cycling electrification strategy at atmospheric pressure. Energy & Environmental Science, 10(7), 1621-1630.
Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in medicine, 38(11), 2074-2102.
Newman, J. N. (2018). Marine hydrodynamics (p. 448). The MIT Press.
Ng, M. E., & Chua, K. H. (2023). The Effect of Using PhET in Changing Malaysian Students' Attitude to Learning Physics in a Full Virtual Environment. Pertanika Journal of Social Sciences & Humanities, 31(2).
Nihous, G. C. (2016). Notes on hydrostatic pressure. Journal of Ocean Engineering and Marine Energy, 2, 105-109.
Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2), 149-160.
OpenStax College. (2023). College physics. https://cnx.org/contents/031da8d3-b525-429c-80cf-6c8ed997733a/College_Physics. License: CC BY: Attribution.
Pacala, F. A. (2023). PhET’s Photoelectric Effect Simulation: Experiments and Analysis for Classroom Practice. Asian Journal of Science Education, 5(1), 70-82.
Pacala, F. A. A. (2023, October). Artificial intelligence in a modernizing science and technology education: a textual narrative synthesis in the COVID-19 era. In Journal of Physics: Conference Series, 2611(1), 012028.
Patrice Williams, W. (2023). Autobiography of a Sea Creature: Healing the Trauma of Infant Surgery.
PhET Interactive Simulations. (2024). University of Colorado Boulder. https://phet.colorado.edu/sims/html/underpressure/latest/under-pressure_all.html
Raju, K. S. (2011). Fluid mechanics, heat transfer, and mass transfer: chemical engineering practice. John Wiley & Sons.
Rane, N., Choudhary, S., & Rane, J. (2023). Education 4.0 and 5.0: Integrating artificial intelligence (AI) for personalized and adaptive learning. Available at SSRN 4638365.
Rayan, B., Daher, W., Diab, H., & Issa, N. (2023). Integrating PhET Simulations into Elementary Science Education: A Qualitative Analysis. Education Sciences, 13(9), 884.
Reyes, R., Pacala, F. A. A., Bengua, G., & Entac, E. (2024). The Correlation between Students’ Insights and Academic Achievement in Science: A Predictor for STEM Career Path. Formatif: Jurnal Ilmiah Pendidikan MIPA, 14(1).
Samsudin, A., Putra, G. D., Saepuzaman, D., Aminudin, A. H., & Rais, A. (2020). A Development of PhET based Mechanical Fluid Worksheet to identify changes in student conceptions. Test Engineering and Management, 83(15441), 15441-15451.
San Jose, A. E. (2019). We need your help: An evaluation of students’ tutorial experiences in mathematics and science. Journal of Humanities and Social Sciences Invention, 1(1), 1-7.
Schmidt, H., Seitz, S., Hassel, E., & Wolf, H. (2018). The density–salinity relation of standard seawater. Ocean Science, 14(1), 15-40.
Shi, C., Cui, X., Xie, L., Liu, Q., Chan, D. Y., Israelachvili, J. N., & Zeng, H. (2015). Measuring forces and spatiotemporal evolution of thin water films between an air bubble and solid surfaces of different hydrophobicity. ACS nano, 9(1), 95-104.
Tadmor, R., Das, R., Gulec, S., Liu, J., E. N’guessan, H., Shah, M., ... & Yadav, S. B. (2017). Solid-liquid work of adhesion. Langmuir, 33(15), 3594-3600.
Thiessen, D. B., & Man, K. F. (2023). Surface tension measurement. In Mechanical Variables Measurement-Solid, Fluid, and Thermal (pp. 12-1). CRC Press.
Trelles, J. P. (2018). Advances and challenges in computational fluid dynamics of atmospheric pressure plasmas. Plasma Sources Science and Technology, 27(9), 093001.
Yin, W., Hu, Q., Liu, W., Liu, J., He, P., Zhu, D., & Kornejady, A. (2024). Harnessing Game Engines and Digital Twins: Advancing Flood Education, Data Visualization, and Interactive Monitoring for Enhanced Hydrological Understanding. Water, 16(17), 2528.
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Copyright (c) 2025 Temur Ibotov, Mirkamol Murtazayev , Abdulfayz Allaqulov, Sevinch Sagdiyeva, Otabek Khushnazarov, Khadicha Mamatkulova, Akmal Addimuminov

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