This study pioneers an intelligent teaching system for physical education, utilizing data mining (DM) and virtual simulation technologies, focusing on a basketball course at a university in Shaanxi Province. Involving 250 students randomly assigned to either an intelligent or conventional class, the research examines the impact on learning outcomes, basketball skills, and physical fitness. A survey of 125 students from the intelligent classes assesses the perceived effects of DM and virtual simulation integration. Findings show that students in the intelligent classes significantly outperform their counterparts in all measured areas. The application of DM and virtual simulation technologies facilitates personalized training strategies and techniques tailored to individual student needs. Moreover, 54.12% of students report enhanced engagement and motivation in basketball learning due to these technologies, while 43.82% attribute increased self-confidence in basketball to the use of DM and virtual simulation. The significance of this research lies in establishing a novel intelligent teaching framework for physical education, driven by data mining (DM) and virtual simulation technologies. This innovative approach charts a new course for future sports education methodologies, offering advanced and effective ways to enhance physical education.
BindhuVRanganathanG. Hyperspectral image processing in internet of things model using clustering algorithm. IEEE Internet Things J2021; 3(2): 163–175.
2.
MaLZhangYLeivaV, et al.A new clustering algorithm based on a radar scanning strategy with applications to machine learning data. Expert Syst Appl2022; 191(34): 116143.
3.
HassanBARashidTAMirjaliliS. Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star. Complex Intell Syst2021; 7(5): 2383–2398.
4.
AgbehadjiIEMillhamRCAbayomiA, et al.Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network. Appl Soft Comput2021; 104(86): 107171.
5.
HuaLGuYGuX, et al.A novel brain MRI image segmentation method using an improved multi-view fuzzy c-means clustering algorithm. Front Neurosci2021; 15(89): 245.
6.
MaYLinHWangY, et al.A multi-stage hierarchical clustering algorithm based on centroid of tree and cut edge constraint. Inf Sci2021; 557(70): 194–219.
7.
SabaCSNgepahN. Convergence in renewable energy sources and the dynamics of their determinants: an insight from a club clustering algorithm. Energy Rep2022; 8(64): 3483–3506.
8.
ShiCWeiBWeiS, et al.A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP J Wirel Commun Netw2021; 2021(1): 1–16.
9.
ZhouZSiGSunH, et al.A robust clustering algorithm based on the identification of core points and KNN kernel density estimation. Expert Syst Appl2022; 195(72): 116573.
10.
LeeYKimS. The impact of virtual reality on physical training motivation: a meta-analysis. J Sports Sci Med2023; 22(2): 45–58.
11.
GarciaMAlvarezR. Virtual simulations in sports training: enhancing performance and safety. Inter J Sport Tec2023; 14(1): 100–115.
12.
ChenLZhaoQ. The effects of VR-based training on athlete performance: a longitudinal study. J Virt Sport Tra2024; 15(1): 78–92.
13.
TanakaHNakamuraY. Enhancing physical education through virtual reality: insights and future directions. Internat J Edu Tec Phy Edu2024; 29(3): 120–135.
14.
WanLZhangGLiH, et al.A novel bearing fault diagnosis method using spark-based parallel ACO-K-Means clustering algorithm. IEEE Access2021; 9(02): 28753–28768.
15.
Orlando MoraisFAndrianiKFDa SilvaJL. Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k-means clustering algorithm. J Chem Inf Model2021; 61(7): 3411–3420.
16.
PuQXuCWangH, et al.A novel artificial bee colony clustering algorithm with comprehensive improvement. Vis Comput2022; 38(4): 1395–1410.
17.
SeongCLeeWJeonD. A multi-channel spike sorting processor with accurate clustering algorithm using convolutional autoencoder. IEEE Trans Biomed Circuits Syst2021; 15(6): 1441–1453.
18.
LiuQChenHCrabbeM. Interactive study of multimedia and virtual technology in art education. Int J Emerg Technol Learn2021; 16(1): 80–93.
19.
KhanmohammadiEBarekatainBQuintanaAA. An enhanced AHP-TOPSIS-based clustering algorithm for high-quality live video streaming in flying ad hoc networks. J Supercomput2021; 77(9): 10664–10698.
20.
SharmaRVashishtVSinghU. eeFFA/DE-a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks. Int J Adv Intell Paradigms2022; 21(1-2): 129–157.
21.
SunHFanMSharmaA. Design and implementation of construction prediction and management platform based on building information modelling and three-dimensional simulation technology in industry 4.0. IET Collab Intel Manufact2021; 3(3): 224–232.
22.
DongBWuTYaoZ, et al.Teaching reform and practice of bioengineering comprehensive experiment based on virtual simulation technology. Sheng wu Gong Cheng xue bao= Chinese J Biotech2022; 38(4): 1671–1684.
23.
XieJLiuBHeY, et al.Teaching reform of electronic courses based on virtual technology. International Journal of Social Science and Education Research2021; 4(7): 446–452.
24.
ChangHYWuHFChangYC, et al.The effects of a virtual simulation-based, mobile technology application on nursing students’ learning achievement and cognitive load: randomized controlled trial. Int J Nurs Stud2021; 120(42): 103948.
25.
YangYLiuJGaoQ, et al.Data simulation in machine olfaction with the R package chemosensors. PLoS One2021; 2021(27): 533–536.
26.
LaiZCuiYZhaoT, et al.Design of three-dimensional virtual simulation experiment platform for integrated circuit course. Electronics2022; 11(9): 1437.
27.
LiuQWuHXuZ. Consensus model based on probability K-means clustering algorithm for large scale group decision making. Int J Mach Learn Cybern2021; 12(6): 1609–1626.
28.
ZhangLWangXZhangZ, et al.An adaptative control strategy for interfacing converter of hybrid microgrid based on improved virtual synchronous generator. IET Renewable Power Gen2022; 16(2): 261–273.
29.
LiuW. The effects of virtual simulation on undergraduate nursing students’ beliefs about prognosis and outcomes for people with mental disorders. Clinic Sim Nur2021; 50(49): 1–9.
30.
LeungDKWYuenSKKLoKL, et al.eLearning and transurethral prostate surgery: virtual tools for a real training. Curr Opin Urol2021; 31(5): 456–460.
31.
MoussaRAlghazalyAAlthagafiN, et al.Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent2022; 16(01): 14–31.
32.
ZhangJXingJZhengM, et al.Effectiveness of virtual simulation and jaw model for undergraduate periodontal teaching. BMC Med Educ2021; 21(1): 1–10.
33.
CoyneECallejaPForsterE, et al.A review of virtual-simulation for assessing healthcare students' clinical competency. Nurse Educ Today2021; 96(83): 104623.
34.
JohnsonAEBarrackJFitzgeraldJM, et al.Integration of a virtual dispensing simulator “MyDispense” in an experiential education program to prepare students for community introductory pharmacy practice experience. Pharmacy2021; 9(1): 48.
35.
JoshiSHamiltonMWarrenR, et al.Implementing Virtual Reality technology for safety training in the precast/prestressed concrete industry. Appl Ergon2021; 90(97): 103286.
36.
XuXLiLSharmaA. Controlling messy errors in virtual reconstruction of random sports image capture points for complex systems. Int J Syst Assur Eng Manag2021; 31(1): 1–8.
