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<p>The development of student subjectivity constitutes a crucial dimension in achieving the comprehensive growth of students. This study examines the role of constructing model branches within higher education institutions and explores their influence on fostering student subjectivity. To this end, an enhanced K-means clustering algorithm, augmented by a particle swarm optimization technique, is employed to process the data of college students, thereby facilitating the development of model branches in these institutions. Additionally, the initial warning feature matrix within the collaborative filtering recommendation algorithm is applied to quantitatively assess student characteristic factors in the establishment of model branches, enabling the creation of an early warning system. The effect of model branch construction on the evolution of student subjectivity is subsequently analyzed through Gaussian process regression and partial least squares regression methods. The findings indicate that the extent of model branch development significantly impacts student subjectivity. Specifically, the average development level of student subjectivity in institutions with high-level model branches surpasses that in institutions lacking such structures by 17.255 points. Thus, the establishment of high-level model branches in colleges and universities emerges as a vital strategy to enhance student subjectivity and promote balanced and holistic student development.</p>
</abstract>

The development of student subjectivity constitutes a crucial dimension in achieving the comprehensive growth of students. This study examines the role of constructing model branches within higher education institutions and explores their influence on fostering student subjectivity. To this end, an enhanced K-means clustering algorithm, augmented by a particle swarm optimization technique, is employed to process the data of college students, thereby facilitating the development of model branches in these institutions. Additionally, the initial warning feature matrix within the collaborative filtering recommendation algorithm is applied to quantitatively assess student characteristic factors in the establishment of model branches, enabling the creation of an early warning system. The effect of model branch construction on the evolution of student subjectivity is subsequently analyzed through Gaussian process regression and partial least squares regression methods. The findings indicate that the extent of model branch development significantly impacts student subjectivity. Specifically, the average development level of student subjectivity in institutions with high-level model branches surpasses that in institutions lacking such structures by 17.255 points. Thus, the establishment of high-level model branches in colleges and universities emerges as a vital strategy to enhance student subjectivity and promote balanced and holistic student development.

An empirical study on the construction of sample branches in colleges and universities in promoting the development of students’ subjectivity

Applied Mathematics and Nonlinear Sciences

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

The development of student subjectivity constitutes a crucial dimension in achieving the comprehensive growth of students. This study examines the role of...

An empirical study on the construction of sample branches in colleges and universities in promoting the development of students’ subjectivity

Publié en ligne: 09 juil. 2024

Pages: -

Reçu: 15 mars 2024

Accepté: 01 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1804

Mots clés
Student subjectivity, K-means clustering, Collaborative filtering, Gaussian process regression

© 2024 Chu Liu., published by Sciendo

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

An empirical study on the construction of sample branches in colleges and universities in promoting the development of students’ subjectivity

Publié en ligne: 09 juil. 2024

Pages: -

Reçu: 15 mars 2024

Accepté: 01 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1804

Mots clésStudent subjectivity, K-means clustering, Collaborative filtering, Gaussian process regression

© 2024 Chu Liu., published by Sciendo

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

Mots clés
Student subjectivity, K-means clustering, Collaborative filtering, Gaussian process regression