Gamified Teaching Scenario Simulation System Built Based on Unity Engine and Particle System
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Abstract
Real-time semantic visualization plays an increasingly important role in immersive training environments and interactive engineering simulations, where stable dynamic rendering is also valuable for the visualization of electromagnetic field evolution and propagation processes. This study presents a gamified teaching scenario simulation system based on the Unity engine and a particle system, integrating learner behavior modeling, exponentially weighted moving average (EWMA) smoothing, semantic parameter mapping, and GPU-driven parallel rendering into a closed-loop visual feedback framework. A ScriptableObject-based semantic mapping strategy dynamically associates learning states with particle attributes, while ComputeBuffer and Visual Effect Graph enable efficient real-time visualization with consistent temporal behavior. Experimental results demonstrate that the proposed system maintains frame rates between 56.3 and 59.8 fps with an average feedback latency of 45.57 ms, significantly improves semantic mapping consistency, and enhances user immersion and interaction satisfaction across multiple teaching scenarios. By providing temporally coherent and semantically adaptive visual feedback, the proposed framework offers a scalable solution for intelligent visualization systems and provides methodological support for interactive representation and analysis of complex electromagnetic phenomena, virtual engineering environments, and digital training platforms.
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References
F. Amlasri, “Simulations to teach science subjects: Connections among students; engagement, self-confidence, satisfaction, and learning styles,” Education and Information Technologies, vol. 27, no. 5, pp. 7161-7181, 2022, doi: 10.1007/s10639-022-10940-w.
K. Yue, “Design and Application of Virtual Simulation Teaching System under Artificial Intelligence Orientation,” Applied Mathematics and Nonlinear Sciences. Erdos Publish House S.L, vol. 9, no. 1, pp. 1-15, 2024, doi: 10.2478/amns-2024-1151.
S. Hong, J. Moon, T. Eom, I. D. Awoyemi, and J. Hwang, “Generative AI-Enhanced Virtual Reality Simulation for Pre-Service Teacher Education: A Mixed-Methods Analysis of Usability and Instructional Utility for Course Integration,” Education Sciences, vol. 15, no. 8, pp. 997, 2025, doi: 10.3390/educsci15080997.
I. M. Álvarez, B. Manero, A. Romero-Hernández, M. Cárdenas, and I. Masó, “Virtual reality platform for teacher training on classroom climate management: evaluating user acceptance,” Virtual Reality, vol. 28, no. 2, pp. 78-95, 2024, doi: 10.1007/s10055-024-00973-6.
A. Li, J. Li, and L. Nong, “Research on Improving Preschool Teachers’ Educational Research Ability Supported by Big Data Technology,” International Journal of E-Collaboration (IJeC), vol. 21, no. 1, pp. 1-16, 2025, doi: 10.4018/IJeC.368067.
J. Schoenherr and S. Schukajlow, “Characterizing external visualization in mathematics education research: a scoping review,” ZDM-Mathematics Education, vol. 56, no. 1, pp. 73-85, 2024, doi: 10.1007/s11858-023-01494-3.
D. M. Onufriienko, “Using a compute shader for an adaptive particle system,” Mathematical Modeling and Computing, vol. 11, no. 1, pp. 1-18, 2024, doi: 10.23939/mmc2024.01.001.
T. Kawamura, Y. Hasegawa, and Y. Idomura, “Interactive steering on in situ particle-based volume rendering framework,” Journal of Visualization, vol. 27, no. 1, pp. 89-107, 2024, doi: 10.1007/s12650-023-00945-z.
L. Chu, B. Ai, Y. Wen, Q. Shi, H. Ma, and W. Feng, “A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System,” ISPRS International Journal of Geo-Information, vol. 12, no. 4, pp. 146, 2023, doi: 10.3390/ijgi12040146.
K. Jaber, E. E. Essel, and P. E. Sullivan, “GPU-native adaptive mesh refinement with application to lattice Boltzmann simulations,” Computer Physics Communications, vol. 311, Art. no. 109543, 2025, doi: 10.1016/j.cpc.2025.109543.
M. Faltýnková, O. Meca, T. Brzobohatý, L. Říha, M. Jaroš, and P. Strakoš, “Workflow for high-quality visualisation of large-scale CFD simulations by volume rendering,” Advances in Engineering Software, vol. 200, Art. no. 103822, 2025, doi: 10.1016/j.advengsoft.2024.103822.
Y. Zhang, Y. Xu, Y. Xu, Y. Hou, X. Wang, Y. Guo, et al., “Real-time screen space rendering method for particle-based multiphase fluid simulation,” Simulation Modelling Practice and Theory, vol. 136, Art. no. 103008, 2024, doi: 10.1016/j.simpat.2024.103008.
V. Cesari, S. D’Aversa, A. Piarulli, F. Melfi, A. Gemignani, and D. Menicucci, “Sense of agency and skills learning in virtual-mediated environment: a systematic review,” Brain Sciences, vol. 14, no. 4, pp. 350, 2024, doi: 10.3390/brainsci14040350.
K. Aoyagi, W. Wen, Q. An, S. Hamasaki, H. Yamakawa, Y. Tamura, et al., “Modified sensory feedback enhances the sense of agency during continuous body movements in virtual reality,” Scientific Reports, vol. 11, no. 1, pp. 2553, 2021, doi: 10.1038/s41598-021-82154-y.
Z. Alsulaimawi, “Feedback-Enhanced Hallucination-Resistant Vision-Language Model for Real-Time Scene Understanding,” 2025. arXiv preprint arXiv:2504.04772.
M. Waseem and M. Hong, “Moving Towards Large-Scale Particle Based Fluid Simulation in Unity 3D,” Applied Sciences, vol. 15, no. 17, pp. 9706, 2025, doi: 10.3390/app15179706.
X. Wang, Y. Xu, S. Liu, B. Ren, J. Kosinka, A. C. Telea, et al., “Physics-based fluid simulation in computer graphics: Survey, research trends, and challenges,” Computational Visual Media, vol. 10, no. 5, pp. 803-858, 2024, doi: 10.1007/s41095-023-0368-y.
C. Rao, H. Yu, H. Wan, J. Zhou, Y. Zheng, M. Wu, et al., “Icarus: A specialized architecture for neural radiance fields rendering,” ACM Transactions on Graphics (TOG), vol. 41, no. 6, pp. 1-14, 2022, doi: 10.1145/3550454.3555505.
L. Franke, L. Fink, and M. Stamminger, “VR-splatting: Foveated radiance field rendering via 3D Gaussian splatting and neural points,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, vol. 8, no. 1, pp. 1-21, 2025, doi: 10.1145/3728302.
P. Jagtap, R. Nasre, V. S. Sanapala, and B. S. V. Patnaik, “Efficient parallelization of SPH algorithm on modern multicore CPUs and massively parallel GPUs,” International Journal of Modeling, Simulation, and Scientific Computing, vol. 12, no. 06, Art. no. 2150054, 2021, doi: 10.1142/S1793962321500549.
K. Zheng, Y. Ci, H. Liu, and J. Zhang, “A spatio-temporal process visualization approach for wind features,” Computational Geosciences, vol. 25, no. 6, pp. 2055-2067, 2021, doi: 10.1007/s10596-021-10080-z.
L. Chen, Z. Ling, Y. Gao, R. Sun, and S. Jin, “A real-time semantic visual SLAM for dynamic environment based on deep learning and dynamic probabilistic propagation,” Complex & Intelligent Systems, vol. 9, no. 5, pp. 5653-5677, 2023, doi: 10.1007/s40747-023-01031-5.
P. Fanta-Jende, D. Steininger, A. Kern, V. Widhalm, J. G. Apud Baca, M. Hofstätter, et al., “Semantic real-time mapping with UAVs,” PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 91, no. 3, pp. 157-170, 2023, doi: 10.1007/s41064-023-00242-2.
X. Liu, Y. He, J. Li, R. Yan, X. Li, and H. Huang, “A comparative review on enhancing visual simultaneous localization and mapping with deep semantic segmentation,” Sensors, vol. 24, no. 11, pp. 3388, 2024, doi: 10.3390/s24113388.
S. Wang and B. Xue, “State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory,” Advances in Neural Information Processing Systems, vol. 36, pp. 74021-74038, 2023.
H. Chaudhry, J. Zavatone-Veth, D. Krotov, and C. Pehlevan, “Long sequence hopfield memory,” Advances in Neural Information Processing Systems, vol. 36, pp. 54300-54340, 2023.
J. Li and G. Sun, “Design of a virtual simulation interaction system based on enhanced reality,” Electronic Research Archive, vol. 31, no. 10, pp. 1-14, 2023, doi: 10.3934/era.2023317.
F. Liarokapis, V. Milata, and F. Skola, “Extended reality educational system with virtual teacher interaction for enhanced learning,” Multimodal Technologies and Interaction, vol. 8, no. 9, pp. 83, 2024, doi: 10.3390/mti8090083.
L. Yao, A. Bezerianos, R. Vuillemot, and P. Isenberg, “Visualization in motion: A research agenda and two evaluations,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 10, pp. 3546-3562, 2022, doi: 10.1109/TVCG.2022.3184993.
Y. Su, C. Lin, and T. Liu, “Real-Time Trajectory Smoothing and Obstacle Avoidance: A Method Based on Virtual Force Guidance,” Sensors, vol. 24, no. 12, pp. 3935, 2024, doi: 10.3390/s24123935.
E. Livieris I, S. Stavroyiannis, and L. Iliadis, “Smoothing and stationarity enforcement framework for deep learning time-series forecasting,” Neural Computing and Applications, vol. 33, no. 20, pp. 14021-14035, 2021, doi: 10.1007/s00521-021-06043-1.
T. T. Goh, “Learning management system log analytics: the role of persistence and consistency of engagement behaviour on academic success,” Journal of Computers in Education, vol. 1, no. 1, pp. 1-24, 2025, doi: 10.1007/s40692-025-00358-x.
H. Va, M. H. Choi, and M. Hong, “Efficient simulation of volumetric deformable objects in Unity3D: GPU-accelerated position-based dynamics,” Electronics, vol. 12, no. 10, pp. 2229, 2023, doi: 10.3390/electronics12102229.
R. Reutov and T. Prikhodko, “Modeling a fluid using a particle system,” International Journal of Open Information Technologies, vol. 12, no. 1, pp. 76-82, 2024.
R. A. Girgidov, “Swarm Simulation Modeling Using the Hadamard Product,” Moscow University Computational Mathematics and Cybernetics, vol. 47, no. 3, pp. 122-134, 2023, doi: 10.3103/S0278641923030032.