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Evaluation Model of Physical Education Integrated Ideology and Politics Based on Principal Component Analysis

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Abstract

In order to obtain scientific quantitative decision-making of physical education integrated ideological and political teaching in college, and improve the teaching effect of college physical education integrated ideology and politics, a teaching effect evaluation model of physical education integrated ideology and politics based on principal component analysis is proposed. Clear teaching contents, activities planning and organization, sports teams, such as evaluation index, according to each evaluation index, data collection and initial evaluation, in combination with principal component analysis (pca), assimilation process evaluation index, and correlation matrix eigenvalue and characteristic vector and determine the number of principal components, fuse the sports education teaching effect evaluation model of the building. Experiments show that the model of the linear correlation coefficient and the rank correlation coefficient of average 97.3% and 96.7%, respectively, were higher than other methods, fusion and its various professional sports education teaching effect are present must rise, the score of one of the English major students score increased 0.75, the lowest building professional and a higher level of ascension 0.12. The accuracy of the evaluation results of the model in this paper is higher, which can effectively improve the effect of college sports integrated ideological and political teaching.

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References

  1. Shuai L, Dongye L, Gautam S et al (2021) Overview and methods of correlation filter algorithms in object tracking. Complex Intell Syst 7:1895–1917

    Article  Google Scholar 

  2. Papaioannou A, Milosis D, Gotzaridis C (2019) Interdisciplinary teaching of physics in physical education: effects on students’ autonomous motivation and satisfaction. J Teach Phys Educ 39(2):1–9

    Google Scholar 

  3. Su L, Xiao L, Wang J (2021) A case study of the ideological and political education of college english translation course driven by words. Creat Educ 12(2):317–328

    Article  Google Scholar 

  4. Yu RR (2020) Research on the reform model of mathematics teaching integration into ideological and political education based on school cloud platform. Creat Educ Stud 08(6):1126–1129

    Article  Google Scholar 

  5. Hao T (2020) The status quo and countermeasures of the construction of primary school teachers under the concept of “curriculum thinking.” Adv Educ 10(4):443–446

    Article  Google Scholar 

  6. Wang Y, Luo Y (2021) Research on the integration of ideological and political elements in the teaching of professional courses—taking the course of “automobile consumption psychology” as an example. Open Access Libr J 08(2):1–5

    Google Scholar 

  7. Liu S, He T, Dai J (2021) A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese. Mob Netw Appl 26(5):1891–1903

    Article  Google Scholar 

  8. Gao P, Li J, Liu S (2021) An introduction to key technology in artificial intelligence and big data driven e-learning and e-education. Mob Netw Appl 26(5):2123–2126

    Article  Google Scholar 

  9. Shuai L (2019) Introduction of key problems in long-distance learning and training. Mob Netw Appl 24(1):1–4

    Article  MathSciNet  Google Scholar 

  10. Li Q (2020) Evaluation method of English and American literature classroom teaching quality based on fuzzy analytic hierarchy process. Dyn Syst Appl 29(4):21–26

    Google Scholar 

  11. Wang Y (2020) Ideological and political teaching model using fuzzy analytic hierarchy process based on machine learning and artificial intelligence. J Intell Fuz Syst 40(6):1–13

    Google Scholar 

  12. Yang QK, Zhao SP, Liang CY (2020) The construction of quality evaluation system of “big data + ideological and political education” based on “second classroom achievement report.” Heilongjiang Res Higher Educ 38(2):6

    Google Scholar 

  13. Wang S, Liu X, Liu S, et al. Human short-long term cognitive memory mechanism for visual monitoring in IoT-assisted smart cities. IEEE Int Things J. https://doi.org/10.1109/JIOT.2021.3077600

  14. Zhang YL (2020) Simulation of network delay feature extraction based on principal component analysis. Comput Simul 37(3):301–304

    Google Scholar 

  15. Olasege BS, Zhang S, Zhao Q et al (2019) Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis. J Dairy Sci 45(02):145–152

    Google Scholar 

  16. Ait-Sahalia Y, Xiu D (2019) Principal component analysis of high-frequency data. J Am Stat Assoc 114(525):287–303

    Article  MathSciNet  Google Scholar 

  17. Schipper N, Deun KV (2020) Model selection techniques for sparse weight-based principal component analysis. J Chemometr 45(529):1145–1152

    Google Scholar 

  18. Roberge JB, Harnois-Leblanc S, Mcnealis V et al (2021) Body mass index Z-score vs weight-for-length Z-score in infancy and cardiometabolic outcomes at age 8–10 years. J Pediatr 15(2):15–25

    Google Scholar 

  19. Simian LI (2020) Comprehensive evaluation on the level of agricultural economic development in Hubei province based on principal component analysis. Asian Agric Res 12(08):34-35+43

    Google Scholar 

  20. Franklin JB, Sathish T, Vinithkumar NV et al (2020) A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores. Mar Pollut Bull 152(1):110902

    Article  Google Scholar 

  21. Shuai L, Shuai W, Xinyu L et al (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimed 23:2188–2198

    Article  Google Scholar 

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Correspondence to Jianbo Yu.

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The authors have no relevant financial or non-financial interests to disclose. Xuekai Chen provided the algorithm and experimental results, wrote the manuscript, Jianbo Yu revised the paper, supervised and analyzed the experiment. We also declare that data availability and ethics approval is not applicable in this paper.

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Chen, Xk., Yu, J. Evaluation Model of Physical Education Integrated Ideology and Politics Based on Principal Component Analysis. Mobile Netw Appl 27, 1240–1251 (2022). https://doi.org/10.1007/s11036-022-01944-4

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  • DOI: https://doi.org/10.1007/s11036-022-01944-4

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