Construction and Quantitative Evaluation of Basketball Smart Teaching Scenarios Integrating Ideological and Political Elements
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Abstract
Accurate perception and quantitative assessment of human activities in complex interactive environments require efficient sensing, reliable data fusion, and real-time contextual understanding. This study proposes an intelligent scenario construction and evaluation framework based on multimodal sensing and digital twin technologies. A heterogeneous sensing architecture integrating ultra-wideband (UWB) localization, inertial measurement units (IMUs), computer vision systems, and physiological sensor networks is developed to acquire synchronized spatiotemporal data from dynamic human activities. To achieve semantic understanding of complex behavioral interactions, a domain knowledge graph is introduced to establish contextual relationships among activity events, interaction patterns, and assessment objectives, enabling real-time generation of a digital twin environment. Furthermore, a data-driven evaluation model incorporating multi-head attention mechanisms is designed to perform feature fusion, representation learning, and quantitative assessment from multimodal sensor streams. Experimental results demonstrate that the proposed framework effectively improves activity recognition, interaction analysis, and real-time assessment performance. By integrating wireless sensing, multimodal information fusion, digital twin modeling, and intelligent data analytics, the proposed framework provides an engineering-oriented methodology for human-centric monitoring, distributed sensing environments, and intelligent perception systems.
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References
X. Cheng and M. Ye, “Evaluation of the Implementation Effect of Ideological and Political Education in Physical Education Curriculum Teaching under the Concept of,” Frontiers in Sport Research, vol. 7, no. 1, pp. 22-79, 2025, doi: 10.25236/FSR.2025.070104.
Z. Ma, H. Yang, X. Li, et al., “Research on the Organic Integration of Ideological and Political Elements and Teaching Content in Public Physical Education Courses: Taking Sports Dance as an Example,” International Journal of New Developments in Education, vol. 7, no. 9, pp. 47-53, 2025, doi: 10.25236/IJNDE.2025.070907 Xu X Z. Research on the path of curriculum ideological and political integration into physical education teaching.
Education Research and Practice, “2025; 1(2): 63-66,”, doi: 10.64216/3080-1494.25.03.006.
L. Li, “Study on the Application of Ideological and Political Education in Physical Education Courses,” Frontiers in Educational Research, vol. 7, no. 3, pp. 89-93, 2024, doi: 10.25236/FER.2024.070315.
F. Liu, “The connotation, difficulties and coping strategies of ideological and political construction of physical education curriculum in the new era,” Journal of Tianjin Institute of Physical Education, vol. 38, no. 2, pp. 136-142, 2023.
Y. Yang Z, L. Zhang, and F. Liu L, “Model design and implementation path for the value-added evaluation of the ideological and political effect of the course Sports Measurement and Evaluation empowered by artificial intelligence,” Journal of Modern Education, vol. 2, no. 1, pp. 67-73, 2026, doi: 10.70693/jyxb.v2i1.222.
F. Zulkifli A and A. Danis, “Technology in physical education: Using movement analysis application to improve feedback on sports skills among undergraduate physical education students,” Social Sciences & Humanities Open, vol. 6, no. 1, pp. 100350-100357, 2022, doi: 10.1016/j.ssaho.2022.100350.
Q. Zhong, J. Jiang, W. Bai, et al., “Application of digital-intelligent technologies in physical education: a systematic review,” Frontiers in public health, vol. 13, no. 1, pp. 1626603-1626608, 2025, doi: 10.3389/fpubh.2025.1626603.
X. Ye, “Research on the Path of Integrating Ideological and Political Education into Basketball Course Teaching in Universities,” Frontiers in Sport Research, vol. 5, no. 8, pp. 22-28, 2023, doi: 10.25236/FSR.2023.050805.
Y. Qin, “Research on the Dual-Track Integration Reform Mechanism of Ideological and Political Education-Competition and Training in Basketball Teaching in Private Colleges and Universities from the Perspective of National Fitness,” Contemporary Education Frontiers, vol. 3, no. 5, pp. 170-175, 2025, doi: 10.18063/cef.v3i5.1200.
C. Yang, R. Chen, X. Chen, et al., “The efficiency of cooperative learning in physical education on the learning of action skills and learning motivation,” Frontiers in psychology, vol. 12, no. 1, pp. 717528-717536, 2021, doi: 10.3389/fpsyg.2021.717528.
S. Luo, “Construction of situational teaching mode in ideological and political classroom based on digital twin technology,” Computers and Electrical Engineering, vol. 101, no. 1, pp. 108104-108114, 2022, doi: 10.1016/j.compeleceng.2022.108104.
M. Modinger, A. Woll, and I. Wagner, “Video-based visual feedback to enhance motor learning in physical educationa systematic review,” German journal of exercise and sport research, vol. 52, no. 3, pp. 447-460, 2022, doi: 10.1007/s12662-021-00782-y.
C. Seckin A, B. Ates, and M. Seckin, “Review on Wearable Technology in sports: Concepts, Challenges and opportunities,” Applied sciences, vol. 13, no. 18, pp. 10399-10406, 2023, doi: 10.3390/app131810399.
P. Cheng, “Evaluation method of ideological and political classroom teaching quality based on analytic hierarchy process,” Scientific Programming, vol. 2022, no. 1, pp. 6554084-6554189, 2022, doi: 10.1155/2022/6554084.
X. Chen and J. Yu, “Evaluation model of physical education integrated ideology and politics based on principal component analysis,” Mobile Networks and Applications, vol. 27, no. 3, pp. 1240-1251, 2022, doi: 10.1007/s11036-022-01944-4.
Y. Ma, M. Zuo, R. Gao, et al., “Interrelationships among college students perceptions of smart classroom environments, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement,” Behavioral Sciences, vol. 14, no. 7, pp. 565-571, 2024, doi: 10.3390/bs14070565.
I. Adler, S. Warren, C. Norris, et al., “Leveraging opportunities for self-regulated learning in smart learning environments,” Smart Learning Environments, vol. 12, no. 1, pp. 6-12, 2025, doi: 10.1186/s40561-024-00359-w.
F. Zhang, L. Yang, Y. Liu, et al., “Design and implementation of real-time localization system (RTLS) based on UWB and TDoA algorithm,” Sensors, vol. 22, no. 12, pp. -4358, 2022, doi: 10.3390/s22124353.
P. Huang S, B. Chen C, Z. Wei T, et al., “Range-extension algorithms and strategies for tdoa ultra-wideband positioning system,” Sensors, vol. 23, no. 6, pp. 3088-3092, 2023, doi: 10.3390/s23063088.
K. Host, M. Pobar, and M. Ivasic-Kos, “Analysis of movement and activities of handball players using deep neural networks,” Journal of imaging, vol. 9, no. 4, pp. 80-84, 2023, doi: 10.3390/jimaging9040080.
C. Duan, B. Hu, W. Liu, et al., “Motion capture for sporting events based on graph convolutional neural networks and single target pose estimation algorithms,” Applied Sciences, vol. 13, no. 13, pp. 7611-7615, 2023, doi: 10.3390/app13137611.
Y. Shi, Y. Zhang, Z. Li, et al., “IMU/UWB fusion method using a complementary filter and a Kalman filter for hybrid upper limb motion estimation,” Sensors, vol. 23, no. 15, pp. 6700-6709, 2023, doi: 10.3390/s23156700.
L. Chen Y, J. Yang I, C. Fu L, et al., “IMU-based estimation of lower limb motion trajectory with graph convolution network,” IEEE Sensors Journal, vol. 21, no. 21, pp. 24549-24557, 2021, doi: 10.1109/JSEN.2021.3115105.
Z. Li, Y. Zhao, X. Zhang, et al., “Word embedding factor based multi-head attention,” Artificial Intelligence Review, vol. 58, no. 4, pp. 115-119, 2025, doi: 10.1007/s10462-025-11115-y.
Y. Liu, H. Wu Y, G. Sun, et al., “Vision transformers with hierarchical attention,” Machine intelligence research, vol. 21, no. 4, pp. 670-683, 2024, doi: 10.1007/s11633-024-1393-8.
V. Shatravin, D. Shashev, and S. Shidlovskiy, “Sigmoid activation implementation for neural networks hardware accelerators based on reconfigurable computing environments for low-power intelligent systems,” Applied Sciences, vol. 12, no. 10, pp. 5216-5221, 2022, doi: 10.3390/app12105216.
M. Alzhrani K, “From Sigmoid to SoftProb: A novel output activation function for multi-label learning,” Alexandria Engineering Journal, vol. 129, no. 1, pp. 472-482, 2025, doi: 10.1016/j.aej.2025.06.013.
M. Xing J and Q. Jiang, “Factors influencing user experience in AI chat systems-a satisfaction study based on factor analysis and linear regression,” Kybernetes, vol. 54, no. 10, pp. 5441-5481, 2025, doi: 10.1108/K-10-2023-2237.
Wang Y,Qu W,Yuan Y.Study on the Construction and Verification of CDIO Teaching Model Based on AI Virtual Scene Data Feedback[J].Journal of Contemporary Educational Research,2025,9(10):164-173, doi: 10.26689/JCER.V9I10.12653.