International Journal of Sports Technology, 2021, 2(2); doi: 10.38007/IJST.2021.020201.
Qiang Chen and Yanhong Zhang
Nanchang Institute of Science and Technology, Jiangxi 330108, China
With the continuous development and application of big data, the current teaching model has undergone great changes, and the drawbacks of traditional teaching methods have gradually been exposed. In terms of physical education, teaching reform in the era of big data is imperative. In the era of big data, how to seize the opportunity and effectively use big data technology and computer information technology to carry out teaching is a question that every teaching worker must think about. The problem is that online teaching has a positive effect and influence on cultivating students’ sports awareness and raising interest in classroom teaching. At the same time, in the context of big data, the widespread popularization of sports applications has brought huge opportunities to improve the effect of university network teaching, and discussed the factors and significance that affect the effect of university network teaching, so as to promote the innovative and differentiated development of university sports work. And comprehensively analyze the strategy of improving the effect of colleges and universities network teaching under the background of big data, and then promote the development of college physical education to effectively carry out college sports work, and continuously cultivate students' concept and ability of lifelong sports. To develop high-quality college students’ physical education curriculum, it is necessary to identify the key directions of teaching reform and carry out work in a planned way. Combining the advantages of big data, in the process of physical education curriculum reform, combined with the collected data on the overall physical fitness of college students, feedback on the effects of teaching activities, and students’ personal interest in physical exercise, etc., an intuitive analysis can be made through the comparison of comprehensive data: Whether the teaching work has achieved the expected teaching effect, whether the students’ physical literacy and physical fitness have been improved, etc., and through the lack of teaching derived from the data, the bottleneck facing the physical education curriculum is analyzed. According to the needs of current teaching development, explore A scientific teaching method suitable for the individual needs of students and the direction of teaching reform. This article analyzes and discusses the reform measures of physical education in the era of big data, and learns from the analysis of experts to study the application of big data in the cultivation of college physical education and training interest.
Big Data, Physical Training, High-Efficiency Physical Education, Interest Training Model
Qiang Chen, Yanhong Zhang. Interest Cultivation Mode in College Physical Education Training Based on Big Data. International Journal of Sports Technology (2021), Vol. 2, Issue 2: 1-8. https://doi.org/10.38007/IJST.2021.020201.
 Mohan S. Energy aware task scheduling using hybrid firefly - GA in big data. International Journal of Advanced Intelligence Paradigms, 2020, 16(2):99-112. https://doi.org/10.1504/IJAIP. 2020.107008
 Long Y, Zhao J. What Makes a City Bikeable? A Study of Intercity and Intracity Patterns of Bicycle Ridership using Mobike Big Data Records. Built environment, 2020, 46(1):55-75. https://doi.org/10.2148/benv.46.1.55
 Guan H, Zhao X. Study on the Prediction System of Shrimp Field Distribution in the East China Sea Based on Big Data Analysis of Fishing Trajectories. Journal of Ocean University of China, 2021, 20(1):228-234.https://doi.org/10.1007/s11802-021-4518-5
 Евгений Анатольевич Чумила, Игорь Иванович Маркач, Алексей Александрович Гурин. Theoretical aspects of professionally applied physical training of rescuers of the Ministry of Emergencies of Belarus. Journal of Civil Protection, 2020, 4(4):442-449. https://doi.org/ 10.33408/ 2519-237X.2020.4-4.442
 Juraevich M B. Scientific Basis of the Concept of Strategic Marketing for the Development of Physical Training and Sports in Uzbekistan. International Journal of Psychosocial Rehabilitation, 2020, 24(5):6579-6585.https://doi.org/10.37200/IJPR/V24I5/PR2020645
 Mikicin M, Anna Mróz, Karczewska-Lindinger M, et al. Correction to: Effect of the Neurofeedback-EEG Training During Physical Exercise on the Range of Mental Work Performance and Individual Physiological Parameters in Swimmers. Applied Psychophysiology and Biofeedback, 2020, 45(2):57-57. https://doi.org/10.1007/s10484-020-09463-2
 Park M, Lee D W, Jeong M B. The Effect of Coordinative Locomotor Training on Physical Factors for Falls in the Elderly with Mild Cognitive Impairment. Journal of the Korean Society of Physical Medicine, 2020, 15(2):65-73. https://doi.org/10.13066/kspm.2020.15.2.65
 Patricios J. Diversity among our disciplines: let's provide differing perspectives in the interest of athlete care. British Journal of Sports Medicine, 2020, 54(10):561-562. https://doi.org/ 10.1136/bjsports-2020-102305
 Yarmani Y, Defliyanto D. Petanque Sports Training and Socialization for Pjok Teachers in Mgmp Sukaraja, Kab. Seluma. Dharma Raflesia Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS, 2020, 18(1):12-14. https://doi.org/10.33369/dr.v18i1.11109
 Klimov V M, Roman Idelevich Аizman. Effect of Different Physical and Sports Specializations on Psychophysiological Status of Students. Психология Психофизиология, 2020, 12(4):83-92. https://doi.org/10.14529/jpps190409