Issue |
E3S Web Conf.
Volume 275, 2021
2021 International Conference on Economic Innovation and Low-carbon Development (EILCD 2021)
|
|
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Article Number | 03028 | |
Number of page(s) | 5 | |
Section | Environmental Protection and Governance Innovation Technology Research | |
DOI | https://doi.org/10.1051/e3sconf/202127503028 | |
Published online | 21 June 2021 |
Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning
Liaoning Finance Vocational College, Shenyang 100122, Liaoning, China
* Corresponding author e-mail: anchang@lnfvc.edu.cn
Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimental data show that there is a positive interaction between teachers and students. Most students use the interactive communication mode of machines, while a small number of students use real-time interactive discussions with teachers. The experimental results show that the excellent rate of ABC classes in the first semester is 80%, 82% and 90%, respectively, through the machine-supervised teaching mode. Therefore, supervised machine learning can greatly help students improve their academic performance. In the future, we should further explore the application of other personalized and extensible machine learning methods in quality education.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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