Abstract
At present, there are some problems in online physical education micro course system, such as the teaching effect is not ideal and students’ performance is low, so it is necessary to design a new online physical education micro course system based on improved machine learning. The hardware of the system consists of three interactive modules: information display unit, information processing unit and terminal interactive unit. The system software is composed of system user module, course management module, independent learning module, database module and online examination module. In the database module, six groups of information parameters, such as administrator information, course information and announcement information, are set. Using the improved machine learning method to design the online examination module. Through the combination of hardware and software, the online physical education micro course system is designed. The comparative experiment shows that the teaching effect of the system is better than that of the traditional system, and the sports performance is improved significantly.
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References
Demir, M.: Using online peer assessment in an Instructional Technology and Material Design course through social media. High. Educ. 75(3), 399–414 (2018). https://doi.org/10.1007/s10734-017-0146-9
Veiga, N., Luzardo, F., Irving, K., et al.: Online pre-laboratory tools for first-year undergraduate chemistry course in Uruguay: student preferences and implications on student performance. Chem. Educ. Res. Pract. 20(1), 229–245 (2019)
Park, H.S., Cheong, Y.F.: Correlates of monotonic response patterns in online ratings of a university course. High. Educ. 76(1), 101–113 (2018). https://doi.org/10.1007/s10734-017-0199-9
Goodfellow, I., McDaniel, P., Papernot, N.: Making machine learning robust against adversarial inputs. Commun. ACM 61(7), 56–66 (2018)
Muggleton, S.H., Schmid, U., Zeller, C., et al.: Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Mach. Learn. 107(7), 1119–1140 (2018). https://doi.org/10.1007/s10994-018-5707-3
Nalmpantis, C., Vrakas, D.: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif. Intell. Rev. 52(1), 217–243 (2019). https://doi.org/10.1007/s10462-018-9613-7
Lei, Z., Zhou, H., Hu, W., et al.: Modular web-based interactive hybrid laboratory framework for research and education. IEEE Access 20(6), 152–163 (2018)
Jin, D., Shi, S., Zhang, Y., et al.: A complex event processing framework for an adaptive language learning system. Future Gener. Comput. Syst. 92(10), 857–867 (2019)
Merayo, N., RuĂz, I., Debrán, J., et al.: AIM-Mobile Learning Platform to enhance the teaching-learning process using smartphones. Comput. Appl. Eng. Educ. 26(5), 1753–1768 (2018)
Wei, X., Gu, Q., Luo, Y., et al.: The reform of computer experiment teaching based on O2O model. Comput. Appl. Eng. Educ. 27(1), 102–111 (2019)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lv, Cm., Zhang, Xp., Ji, Jp. (2020). Research on Online Physical Education Micro Course System Based on Improved Machine Learning. In: Liu, S., Sun, G., Fu, W. (eds) e-Learning, e-Education, and Online Training. eLEOT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-030-63952-5_20
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DOI: https://doi.org/10.1007/978-3-030-63952-5_20
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