<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates network ideological education using big data technology. Secondly, K-means clustering analysis is used to mine students’ campus behaviors, and the DPC algorithm combined with the improved AdaBoost algorithm is used to detect students’ academic anomalies so as to help schools better realize student management. Finally, performance testing and application analysis were conducted to verify the effectiveness of the student management platform that integrates online ideological education. The results show that when the number of concurrent users is 5000, the CPU occupancy rate and response time of this platform are 10.34% and 0.52 s. It is able to analyze and detect abnormalities in students’ campus behaviors and academic situations and encourage universities to analyze students’ situations through visual reports. This shows that the student management platform integrating network ideological education in the era of big data can promote the level of student management in colleges and universities.</p>
</abstract>

This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates network ideological education using big data technology. Secondly, K-means clustering analysis is used to mine students’ campus behaviors, and the DPC algorithm combined with the improved AdaBoost algorithm is used to detect students’ academic anomalies so as to help schools better realize student management. Finally, performance testing and application analysis were conducted to verify the effectiveness of the student management platform that integrates online ideological education. The results show that when the number of concurrent users is 5000, the CPU occupancy rate and response time of this platform are 10.34% and 0.52 s. It is able to analyze and detect abnormalities in students’ campus behaviors and academic situations and encourage universities to analyze students’ situations through visual reports. This shows that the student management platform integrating network ideological education in the era of big data can promote the level of student management in colleges and universities.

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first analyzes the path of network ideological education to boost student management and constructs a student management platform that integrates...

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Online veröffentlicht: 15. Nov. 2023

Seitenbereich: -

Eingereicht: 31. Jan. 2023

Akzeptiert: 26. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01162

Schlüsselwörter
Student management platform, Online civic education, K-means clustering, DP-2W-AdaBoost algorithm, Academic anomaly detection

© 2023 Hong Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Exploration of the Practical Path of Internet Civic Education in Student Management in Colleges and Universities Based on K-means Clustering

Online veröffentlicht: 15. Nov. 2023

Seitenbereich: -

Eingereicht: 31. Jan. 2023

Akzeptiert: 26. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01162

SchlüsselwörterStudent management platform, Online civic education, K-means clustering, DP-2W-AdaBoost algorithm, Academic anomaly detection

© 2023 Hong Sun, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Student management platform, Online civic education, K-means clustering, DP-2W-AdaBoost algorithm, Academic anomaly detection