INNOVATIVE MODELS OF VISUAL LEARNING IN PHYSICS EDUCATION DURING DIGITAL TRANSFORMATION: A CASE STUDY OF SPECIALIZED MILITARY SCHOOLS
DOI:
https://doi.org/10.66345/stj.v4i5/2.6140Keywords:
visual learning, physics education, digital transformation, specialized military schools, computational modeling, LMS, interactive simulationsAbstract
This article develops and substantiates an innovative visual-learning framework for teaching physics in specialized military schools during digital transformation. The study follows a theoretical-analytical and design-based case-study approach grounded in national policy documents on educational digitalization, research on interactive simulations, computational modeling, multimedia learning, cognitive load, and LMS-based analytics. The central argument is that, in a military-academic context, physics should be taught not as a static body of formulas but as a model-based language for describing motion, constraints, uncertainty, and decision-making. The paper proposes three interrelated visual-learning models: (1) dynamic simulation-based conceptualization, (2) multi-representational computational modeling, and (3) LMS-embedded interactive visual assessment. Mechanics and introductory ballistic trajectory tasks are used as demonstrative cases because they require synchronized qualitative reasoning, mathematical formalization, and algorithmic verification. The analysis shows that visual models become pedagogically productive when they reduce invisible processes to inspectable structures, support guided inquiry, and provide immediate feedback through dashboards and interactive content. The resulting framework is suitable for Temurbek specialized military schools and similar institutions seeking to align subject teaching with engineering reasoning, digital didactics, and evidence-based instructional design.
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References
1. Adams, W. K. (2010). Student engagement and learning with PhET interactive simulations. Nuovo Cimento C, 33(3), 21–32. https://doi.org/10.1393/ncc/i2010-10623-0
2. 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
3. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179
4. 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
5. Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
6. Kefalis, C., Skordoulis, C., & Drigas, A. (2025). Digital simulations in STEM education: Insights from recent empirical studies, a systematic review. Encyclopedia, 5(1), 10. https://doi.org/10.3390/encyclopedia5010010
7. Mayer, R. E. (2020). Multimedia Learning (3rd ed.). Cambridge University Press.
8. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2
9. President of the Republic of Uzbekistan. (2019). On approval of the concept for development of the public education system of the Republic of Uzbekistan until 2030 (Presidential Decree PF-5712).
10. President of the Republic of Uzbekistan. (2020). On approval of the Digital Uzbekistan–2030 strategy and measures for its effective implementation (Presidential Decree PF-6079).
11. Rahmi, U., Fajri, I., & Azrul. (2024). Effectiveness of interactive content with H5P for Moodle-LMS in blended learning. Journal of Learning for Development, 11(1), 66–81.
12. Solvang, L., & Haglund, J. (2021). How can GeoGebra support physics education in upper-secondary school—a review. Physics Education, 56, 055011. https://doi.org/10.1088/1361-6552/ac03fb
13. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
14. 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), 861. https://doi.org/10.3390/educsci14080861
