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Application of Campus Happy Running APP in the Reform of University Physical Education Integration

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1283))

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Abstract

Objective: In view of the current decline in college students’ physical fitness and the Ministry of Education’s basic requirement of “effectively ensuring that students exercise for one hour a day”, the reform of universities is carried out by using the campus trail Lepao APP software to promote the integration of physical education in and out of universities. Methods: Literature review, questionnaire survey and logical analysis were used. Results: The introduction and APPlication of footpath running app incorporated extra-curricular running exercise into the evaluation system of physical education curriculum, realized the diversified evaluation of college physical education, and promoted the integration of inside and outside physical education classes. Conclusion: The campus trail running APP effectively supervises college students’ after-school exercise, realizes the integration of college physical education inside and outside the class and the diversification of performance evaluation, which is of great significance to mobilize the enthusiasm of students’ after-school exercise, cultivate lifelong physical education awareness and habits, and perfect the school physical education work. At the same time, it has important reference value for the construction of the integrated evaluation mode of physical education inside and outside the class.

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Acknowledgments

This research was supported by Teaching Research Project of Hubei Institute of Science and Technology (2018-XB-016).

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Correspondence to Yuanhai Liu .

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Liu, Y., Liu, Y. (2021). Application of Campus Happy Running APP in the Reform of University Physical Education Integration. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-62746-1_77

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