Abstract
In order to solve the problem that the evaluation results of the ideological and political effect of the Internet of things network course are not accurate, this paper puts forward the research on the evaluation method of Ideological and political effect of online courses of Internet of things. The influencing factors of Ideological and political effect of online courses are deeply analyzed, and the evaluation indexes of Ideological and political effect of online courses are determined by KMO statistics and factor analysis; The analytic hierarchy process is applied to calculate the weight of evaluation indexes and construct the evaluation model of Ideological and political effect of online courses; Formulate the evaluation standard of Ideological and political effect of online courses, so as to realize the evaluation of Ideological and political effect of online courses. The experimental results show that compared with the existing methods, the minimum delay determined by the evaluation index obtained by the proposed method is 0.8s, the minimum delay calculated by the index weight is 2.3 s, the gap between the evaluation result and the actual evaluation value is less than 1 point, and the accuracy of the evaluation result is high. The above data fully confirm the feasibility and effectiveness of the proposed method.
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References
Gavronskaya, Y., Larchenkova, L., Kurilova, A., et al.: Virtual lab model for making online courses more inclusive for students with special educational needs. Int. J. Emerg. Technol. Learning (iJET) 16(2), 79 (2021)
Sharov, S., Pavlenko, A., Sharova, T., et al.: Analysis of developers of online courses on Ukrainian platforms of MOOC. Int. J. Emerg. Technol. Learn. (iJET) 16(5), 201 (2021)
Alturise, F.: Evaluation of blackboard learning management system for full online courses in Western Branch Colleges of Qassim University. Int. J. Emerg. Technol. Learn. (iJET) 15(15), 33 (2020)
Li, S., Chai, H.: Recognition of teaching features and behaviors in online open courses based on image processing. Traitement du Signal 38(1), 155–164 (2021)
Han, Z.M., Huang, C.Q., Yu, J.H., et al.: Identifying patterns of epistemic emotions with respect to interactions in massive online open courses using deep learning and social network analysis. Comput. Hum. Behav. 122(2), 106843 (2021)
Cunha, M.N., Chuchu, T., Maziriri, E.T.: Threats, challenges, and opportunities for open universities and massive online open courses in the digital revolution. Int. J. Emerg. Technol. Learn. (iJET) 15(12), 191–204 (2020)
Zhang, X.: Evaluating the quality of internet-based education in colleges using the regression algorithm. Mob. Inf. Syst. 2021(1), 1–9 (2021)
Yan, X.X., An, X.W., Dai, W.B., et al.: Image segmentation teaching system based on virtual scene fusion. Comp. Simul. 38(4), 331–337 (2021)
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Project supported by the Education Department of Hainan Province, project number: HnjgS2022-15.
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cai, J., Feng, J., Shi, J., Zhang, Y., Meng, S., Yao, J. (2022). Research on the Evaluation Method of Ideological and Political Effect of Online Courses in Internet of Things. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-21164-5_28
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DOI: https://doi.org/10.1007/978-3-031-21164-5_28
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