Abstract
Online teaching is developed on the basis of distance education. From the perspective of teacher-student relationship, online teaching should be the unity of five elements: teachers, students, technology, courses and activities. The level of interaction in the process of online teaching affects learners’ knowledge construction and learning quality. The existing network education platform focuses on the adaptive learning of knowledge content. Unable to give appropriate feedback based on current learning status. Convolution neural network can reduce the feature dimension, compress the amount of data, reduce the number of network parameters, and prevent over fitting. Therefore, this paper puts forward the design of online teaching method of subject knowledge for mathematics teachers in Higher Vocational Colleges Based on convolutional neural network. Based on the online teaching characteristics, the convolution neural network algorithm is improved, the convolution neural network algorithm structure is optimized, and the loss function is built. Based on convolution neural network algorithm, integrate online teaching materials and optimize the examination system of mathematics online teaching courses in higher vocational colleges. Finally, an example shows that the online teaching method of mathematics teachers’ subject knowledge in higher vocational colleges can effectively improve students’ performance.
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Funding
Jilin Province Vocational Education Research Project: Research on the Ways to Improve the Subject Knowledge and Teaching and Research Ability of Mathematics Teachers in Higher Vocational Colleges (2021XHZ043).
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Yu, C., Wang, J. (2022). Design of Online Teaching Method for Subject Knowledge of Mathematics Teachers in Higher Vocational Colleges Based on Convolutional Neural Network. 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 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_57
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DOI: https://doi.org/10.1007/978-3-031-21161-4_57
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