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Online Course Model of Social and Political Education Using Deep Learning

This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built based on deep convolutional neural network (DCNN)....

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Detalles Bibliográficos
Autores principales: Zhang, Min, Gao, Qiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042604/
https://www.ncbi.nlm.nih.gov/pubmed/35498168
http://dx.doi.org/10.1155/2022/7653766
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author Zhang, Min
Gao, Qiong
author_facet Zhang, Min
Gao, Qiong
author_sort Zhang, Min
collection PubMed
description This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built based on deep convolutional neural network (DCNN). The sociopolitical situation of the study subjects is analyzed through a questionnaire. Secondly, a model of the learning process is constructed. Finally, the SPE online course learning platform is constructed. Empirical studies are divided into experimental and control groups. The findings show that all model assumptions are valid. There is a significant structural relationship between the influencing factors of the SPE learning process of college students in the study area. The students selected as research objects lack innovation and critical thinking in the learning process and have certain deficiencies in innovative thinking and critical thinking. The questionnaire has good reliability and validity. The predicted data of the designed platform are compared with the predicted data of the control group. Social science competencies by gender are compared. The results showed little difference in the effectiveness of students using other methods for sociopolitical learning. The data of the experimental group before and after the test are quite different, indicating that the designed experimental platform has played a certain positive role. There are significant differences in the posttest data between the experimental group and the control group, indicating that the constructed online course learning model has a positive impact on students' innovative thinking and critical thinking. Women's learning motivation and transfer learning ability are stronger than those of men. The constructed model has certain feasibility for the learning of SPE online courses with face recognition module. These contents provide a reference for the reform of social and political courses.
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spelling pubmed-90426042022-04-27 Online Course Model of Social and Political Education Using Deep Learning Zhang, Min Gao, Qiong Comput Intell Neurosci Research Article This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built based on deep convolutional neural network (DCNN). The sociopolitical situation of the study subjects is analyzed through a questionnaire. Secondly, a model of the learning process is constructed. Finally, the SPE online course learning platform is constructed. Empirical studies are divided into experimental and control groups. The findings show that all model assumptions are valid. There is a significant structural relationship between the influencing factors of the SPE learning process of college students in the study area. The students selected as research objects lack innovation and critical thinking in the learning process and have certain deficiencies in innovative thinking and critical thinking. The questionnaire has good reliability and validity. The predicted data of the designed platform are compared with the predicted data of the control group. Social science competencies by gender are compared. The results showed little difference in the effectiveness of students using other methods for sociopolitical learning. The data of the experimental group before and after the test are quite different, indicating that the designed experimental platform has played a certain positive role. There are significant differences in the posttest data between the experimental group and the control group, indicating that the constructed online course learning model has a positive impact on students' innovative thinking and critical thinking. Women's learning motivation and transfer learning ability are stronger than those of men. The constructed model has certain feasibility for the learning of SPE online courses with face recognition module. These contents provide a reference for the reform of social and political courses. Hindawi 2022-04-19 /pmc/articles/PMC9042604/ /pubmed/35498168 http://dx.doi.org/10.1155/2022/7653766 Text en Copyright © 2022 Min Zhang and Qiong Gao. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Min
Gao, Qiong
Online Course Model of Social and Political Education Using Deep Learning
title Online Course Model of Social and Political Education Using Deep Learning
title_full Online Course Model of Social and Political Education Using Deep Learning
title_fullStr Online Course Model of Social and Political Education Using Deep Learning
title_full_unstemmed Online Course Model of Social and Political Education Using Deep Learning
title_short Online Course Model of Social and Political Education Using Deep Learning
title_sort online course model of social and political education using deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042604/
https://www.ncbi.nlm.nih.gov/pubmed/35498168
http://dx.doi.org/10.1155/2022/7653766
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