Optimization of Ideological and Political Education under the Epidemic via Mobile Learning Auxiliary Platform in the Era of Digitization

In the digitized era, life has become simpler with the increased information technology. The Education Department in the whole world is facing a tremendous revolution with the development. The traditional classroom study is converted to a modernized and digitized classroom with visualization. This m...

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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2022; no. 1
Main Authors: Zhang, Lizhe, He, Juan
Format: Journal Article
Language:English
Published: Oxford Hindawi 2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial intelligence
Classrooms
Cluster analysis
Clustering
College students
Colleges & universities
Data mining
Design
Digitization
Distance learning
Educational materials
Educational technology
Higher education
Higher education institutions
Ideology
Internet
Machine learning
Modernization
Online instruction
Optimization
Pneumonia
Politics
Students
Teaching
Vector quantization
Wireless networks
China
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary:In the digitized era, life has become simpler with the increased information technology. The Education Department in the whole world is facing a tremendous revolution with the development. The traditional classroom study is converted to a modernized and digitized classroom with visualization. This modernization has increased the learning capability of the students with an increase in student and teacher interaction. From this teaching and learning process, most colleges and universities have improved performance in preparing course materials, effective teaching, and independent learning among the students in the theoretical courses. Ideological and political education (IPE) is a theoretical subject that is taught and understood at higher education institutions, such as colleges and universities. A hybrid hierarchical K-means clustering for optimizing clustering with unsupervised machine learning is proposed to analyze the student performance and concluded that the proposed algorithm shows improved performance than the K-means algorithm.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2022/6149995