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Fagaras, S. P., Radu, L. E., & Vanvu, G. (2015). The level of physical activity of university students. Procedia-Social and Behavioral Sciences, 197, 1454-1457.FagarasS. P.RaduL. E.VanvuG. (2015). The level of physical activity of university students. Procedia-Social and Behavioral Sciences, 197, 1454-1457.Search in Google Scholar
Al-Tawel, A. M., & AlJa’afreh, I. A. (2017). A Study of Female Students’ Perceptions of the Barriers to Participate in Physical and Sports Activities at Al-Hussein Bin Talal University. Journal of Education and Practice, 8(11), 1-10.Al-TawelA. M.AlJa’afrehI. A. (2017). A Study of Female Students’ Perceptions of the Barriers to Participate in Physical and Sports Activities at Al-Hussein Bin Talal University. Journal of Education and Practice, 8(11), 1-10.Search in Google Scholar
Larsson, H., & Nyberg, G. (2017). ‘It doesn’t matter how they move really, as long as they move.’Physical education teachers on developing their students’ movement capabilities. Physical Education and Sport Pedagogy, 22(2), 137-149.LarssonH.NybergG. (2017). ‘It doesn’t matter how they move really, as long as they move.’Physical education teachers on developing their students’ movement capabilities. Physical Education and Sport Pedagogy, 22(2), 137-149.Search in Google Scholar
Iserbyt, P., Ward, P., & Li, W. (2017). Effects of improved content knowledge on pedagogical content knowledge and student performance in physical education. Physical Education and Sport Pedagogy, 22(1), 71-88.IserbytP.WardP.LiW. (2017). Effects of improved content knowledge on pedagogical content knowledge and student performance in physical education. Physical Education and Sport Pedagogy, 22(1), 71-88.Search in Google Scholar
Hollis, J. L., Sutherland, R., Williams, A. J., Campbell, E., Nathan, N., Wolfenden, L., ... & Wiggers, J. (2017). A systematic review and meta-analysis of moderate-to-vigorous physical activity levels in secondary school physical education lessons. International Journal of Behavioral Nutrition and Physical Activity, 14, 1-26.HollisJ. L.SutherlandR.WilliamsA. J.CampbellE.NathanN.WolfendenL.WiggersJ. (2017). A systematic review and meta-analysis of moderate-to-vigorous physical activity levels in secondary school physical education lessons. International Journal of Behavioral Nutrition and Physical Activity, 14, 1-26.Search in Google Scholar
Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. Journal of education policy, 31(2), 123-141.WilliamsonB. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. Journal of education policy, 31(2), 123-141.Search in Google Scholar
Lupton, D. (2021). ‘Honestly no, I’ve never looked at it’: teachers’ understandings and practices related to students’ personal data in digitised health and physical education. Learning, Media and Technology, 46(3), 281-293.LuptonD. (2021). ‘Honestly no, I’ve never looked at it’: teachers’ understandings and practices related to students’ personal data in digitised health and physical education. Learning, Media and Technology, 46(3), 281-293.Search in Google Scholar
Pang, B., Varea, V., Cavallin, S., & Cupac, A. (2019). Experiencing risk, surveillance, and prosumption: Health and physical education students’ perceptions of digitised health and physical activity data. Sport, education and society.PangB.VareaV.CavallinS.CupacA. (2019). Experiencing risk, surveillance, and prosumption: Health and physical education students’ perceptions of digitised health and physical activity data. Sport, education and society.Search in Google Scholar
Wang, L., & Huang, L. (2020, October). Analysis of the Causes and Prevention of Sports Injuries in School Physical Education and Training Based on Big Data Analysis. In 2020 International Conference on Computers, Information Processing and Advanced Education (CIPAE) (pp. 111-113). IEEE.WangL.HuangL. (2020, October). Analysis of the Causes and Prevention of Sports Injuries in School Physical Education and Training Based on Big Data Analysis. In 2020 International Conference on Computers, Information Processing and Advanced Education (CIPAE) (pp. 111-113). IEEE.Search in Google Scholar
Yu, L., Lu, Q., Yang, T., Wan, D., Xun, R., & Li, F. (2021, June). On the influence of big data era on physical education teaching research. In 2021 International Wireless Communications and Mobile Computing (IWCMC) (pp. 1604-1607). IEEE.YuL.LuQ.YangT.WanD.XunR.LiF. (2021, June). On the influence of big data era on physical education teaching research. In 2021 International Wireless Communications and Mobile Computing (IWCMC) (pp. 1604-1607). IEEE.Search in Google Scholar
Wang, H., Wang, N., Li, M., Mi, S., & Shi, Y. (2021). Student physical health information management model under big data environment. Scientific Programming, 2021(1), 5795884.WangH.WangN.LiM.MiS.ShiY. (2021). Student physical health information management model under big data environment. Scientific Programming, 2021(1), 5795884.Search in Google Scholar
Xu, H. (2021). Empirical study on theories and techniques of adolescent physical health promotion under the background of big data. Mobile Information Systems, 2021(1), 3113157.XuH. (2021). Empirical study on theories and techniques of adolescent physical health promotion under the background of big data. Mobile Information Systems, 2021(1), 3113157.Search in Google Scholar
Ai, L. (2021). Artificial Intelligence System for College Students’ Physical Fitness and Health Management Based on Physical Measurement Big Data. Wireless Communications and Mobile Computing, 2021(1), 4727340.AiL. (2021). Artificial Intelligence System for College Students’ Physical Fitness and Health Management Based on Physical Measurement Big Data. Wireless Communications and Mobile Computing, 2021(1), 4727340.Search in Google Scholar
Park, S. U., Ahn, H., Kim, D. K., & So, W. Y. (2020). Big data analysis of sports and physical activities among Korean adolescents. International Journal of Environmental Research and Public Health, 17(15), 5577.ParkS. U.AhnH.KimD. K.SoW. Y. (2020). Big data analysis of sports and physical activities among Korean adolescents. International Journal of Environmental Research and Public Health, 17(15), 5577.Search in Google Scholar
Li, X., Chen, X., Guo, L., & Rochester, C. A. (2022). Application of big data analysis techniques in sports training and physical fitness analysis. Wireless Communications and Mobile Computing, 2022(1), 3741087.LiX.ChenX.GuoL.RochesterC. A. (2022). Application of big data analysis techniques in sports training and physical fitness analysis. Wireless Communications and Mobile Computing, 2022(1), 3741087.Search in Google Scholar
Wang, J. (2019). The association between physical fitness and physical activity among Chinese college students. Journal of American College Health, 67(6), 602-609.WangJ. (2019). The association between physical fitness and physical activity among Chinese college students. Journal of American College Health, 67(6), 602-609.Search in Google Scholar
Bharti, J., & Singh, P. (2019). Students Relational World without Sports. Think India Journal, 22(8), 320-327.BhartiJ.SinghP. (2019). Students Relational World without Sports. Think India Journal, 22(8), 320-327.Search in Google Scholar
Yang, Y. (2022). Effect of functional training on adolescent health. Revista Brasileira de Medicina do Esporte, 29, e2022_0257.YangY. (2022). Effect of functional training on adolescent health. Revista Brasileira de Medicina do Esporte, 29, e2022_0257.Search in Google Scholar
Wang, J. (2022). Influence of physical training on the physical quality of university students. Revista Brasileira de Medicina do Esporte, 29(spe1), e2022_0184.WangJ. (2022). Influence of physical training on the physical quality of university students. Revista Brasileira de Medicina do Esporte, 29(spe1), e2022_0184.Search in Google Scholar
Wang, C. (2020). Analysis method of college student physical education quality based on big data analysis. In Cyber Security Intelligence and Analytics: Proceedings of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), Volume 1 (pp. 576-581). Springer International Publishing.WangC. (2020). Analysis method of college student physical education quality based on big data analysis. In Cyber Security Intelligence and Analytics: Proceedings of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), Volume 1 (pp. 576-581). Springer International Publishing.Search in Google Scholar
Sun, W. (2022). Predictive analysis and simulation of college sports performance fused with adaptive federated deep learning algorithm. Journal of Sensors, 2022(1), 1205622.SunW. (2022). Predictive analysis and simulation of college sports performance fused with adaptive federated deep learning algorithm. Journal of Sensors, 2022(1), 1205622.Search in Google Scholar
Dong, X. (2021). Physical training information system of college sports based on big data mobile terminal. Mobile Information Systems, 2021(1), 4109794.DongX. (2021). Physical training information system of college sports based on big data mobile terminal. Mobile Information Systems, 2021(1), 4109794.Search in Google Scholar
Zeng, W., Liu, Y., Li, R., & Zeng, Z. (2021, May). Research on the application of big data in the reform of college education mode—taking sports as an example. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 1344-1351).ZengW.LiuY.LiR.ZengZ. (2021, May). Research on the application of big data in the reform of college education mode—taking sports as an example. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 1344-1351).Search in Google Scholar
Yang, S., Jing, Y., & Chen, L. (2022, September). Sports health analysis and promotion countermeasures of weak constitution college students based on K-means clustering: take a university in Beijing as an Example. In Proceedings of the 4th World Symposium on Software Engineering (pp. 138-143).YangS.JingY.ChenL. (2022, September). Sports health analysis and promotion countermeasures of weak constitution college students based on K-means clustering: take a university in Beijing as an Example. In Proceedings of the 4th World Symposium on Software Engineering (pp. 138-143).Search in Google Scholar