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

<p>Improving the efficiency of curriculum education and student management according to the changing characteristics of the social environment is of great significance to the construction of a perfect social and cultural system. This paper constructs a fusion model of student management and curriculum education, taking into account the weaknesses and outstanding problems in both areas. In order to accurately control the behavior of university students, a student behavior prediction method based on the fusion model is proposed, which first uses the k-means clustering method to cluster the daily activity frequency sequences of all users and then realizes the prediction of the individual mobility of students through the LSTM model based on the historical records, and extracts the collective location preference of each user cluster. The effectiveness of the proposed fusion model is assessed using BJ University Daxing Campus as a case study. The results show that after applying the model proposed in this paper, students’ perceptual, language expression, language comprehension, cognitive, social, emotional, and behavioral abilities are improved by 0.941~1.256 points compared with those before the application of the model, with a significant difference of P&lt;0.01, which verifies that the fusion model has strong feasibility. It has positive significance for the work of student management and curriculum education. Research has a positive significance.</p>
</abstract>

Improving the efficiency of curriculum education and student management according to the changing characteristics of the social environment is of great significance to the construction of a perfect social and cultural system. This paper constructs a fusion model of student management and curriculum education, taking into account the weaknesses and outstanding problems in both areas. In order to accurately control the behavior of university students, a student behavior prediction method based on the fusion model is proposed, which first uses the k-means clustering method to cluster the daily activity frequency sequences of all users and then realizes the prediction of the individual mobility of students through the LSTM model based on the historical records, and extracts the collective location preference of each user cluster. The effectiveness of the proposed fusion model is assessed using BJ University Daxing Campus as a case study. The results show that after applying the model proposed in this paper, students’ perceptual, language expression, language comprehension, cognitive, social, emotional, and behavioral abilities are improved by 0.941~1.256 points compared with those before the application of the model, with a significant difference of P<0.01, which verifies that the fusion model has strong feasibility. It has positive significance for the work of student management and curriculum education. Research has a positive significance.

Research on the Integration Strategy of Student Management and Curriculum Education in Colleges and Universities Based on the Integration Model

Applied Mathematics and Nonlinear Sciences

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

Improving the efficiency of curriculum education and student management according to the changing characteristics of the social environment is of great...

Research on the Integration Strategy of Student Management and Curriculum Education in Colleges and Universities Based on the Integration Model

Pubblicato online: 02 lug 2024

Pagine: -

Ricevuto: 17 mar 2024

Accettato: 31 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1513

Parole chiave
Fusion model, K-means clustering, LSTM model, Student management, Curriculum education

© 2024 Xiaoxia Wang et al., published by Sciendo

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

Research on the Integration Strategy of Student Management and Curriculum Education in Colleges and Universities Based on the Integration Model

Pubblicato online: 02 lug 2024

Pagine: -

Ricevuto: 17 mar 2024

Accettato: 31 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1513

Parole chiaveFusion model, K-means clustering, LSTM model, Student management, Curriculum education

© 2024 Xiaoxia Wang et al., published by Sciendo

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

Parole chiave
Fusion model, K-means clustering, LSTM model, Student management, Curriculum education