Abstract
In view of the low effectiveness of traditional online teaching method based on principal component analysis and multilayer perceptron neural network, an online teaching method based on big data mining is proposed. Firstly, the conductive characteristics of graphene materials are extracted, and on this basis, the big data mining technology is used to construct the charge accumulation teaching model, and the online teaching of charge accumulation characteristics is completed through the teaching model. The experimental results show that the proposed method can effectively complete the online teaching of graphene material conductivity characteristics, and has high effectiveness.
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References
Tseng, S.S., Yeh, H.C.: Integrating reciprocal teaching in an online environment with an annotation feature to enhance low-achieving students English reading comprehension. Interact. Learn. Environ. 26(8), 789–802 (2018)
Khamparia, A., Pandey, B.: SVM and PCA based learning feature classification approaches for e-learning system. Int. J. Web Based Learn. Teach. Technol. 13(2), 32–45 (2018)
Wang, Y., Talim, J.: Online mobile teaching methods based on android in the 5G environment. Int. J. Continuing Eng. Educ. Life Long Learn. 30(2), 133 (2020)
Liu, Y.H., Yu, F.Y.: Supporting active learning and formative evaluation via teaching-by-questioning in classrooms: design, development, and preliminary evaluation of an online learning system. Interact. Learn. Environ. 27(8), 841–855 (2019)
Yu-Hsin, L., Fu-Yun, Y.: Supporting active learning and formative evaluation via teaching-by-questioning in classrooms: design, development, and preliminary evaluation of an online learning system. Interact. Learn. Environ. 25(3), 1–15 (2018)
Lobato, J., Walker, C.: How viewers orient toward student dialogue in online math videos. J. Comput. Math. Sci. Teach. 38(2), 177–200 (2019)
Moffitt, R.L., Padgett, C., Grieve, R.: Accessibility and emotionality of online assessment feedback: using emoticons to enhance student perceptions of marker competence and warmth. Comput. Educ. 143, 103654.1–103654.11 (2020)
Magnifico, A.M., Woodard, R., Mccarthey, S.: Teachers as co-authors of student writing: How teachers’ initiating texts influence response and revision in an online space. Comput. Compos. 52, 107–131 (2019)
Shu, L., Juzhi, S.: Current situation and future development of online learning platform video interactive environment. China Educ. Technol. (2019)
Wu, J., Chen, B.: English vocabulary online teaching based on machine learning recognition and target visual detection. J. Intell. Fuzzy Syst. 5, 1–12 (2020)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mobile Netw. Appl. 24(1), 1–4 (2019)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10(8), 120 (2019)
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Teaching Quality and Teaching Reform Project of Guangdong Undergraduate Colleges and Universities: Construction Project of Experiment Demonstration Center (2017002).
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Th., Li, A., Zhang, Z. (2021). Online Teaching Method of Conductive Characteristics of Graphene Materials Based on Big Data Mining. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-84383-0_13
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DOI: https://doi.org/10.1007/978-3-030-84383-0_13
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