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

<p>In this paper, we first study the multimodal interaction activities in higher education management from the two points of multimodal interaction types and multimodal interaction activity evaluation and propose the perfect way of digital construction of multimodal higher education management from the perspective of students, teachers and higher education institutions. Then, the higher education management system is constructed based on semi-supervised fusion feature learning and homogeneous multimodal features of multimodal machines, and the system architecture and database design are explained in detail. Next, the research literature statistics and system performance of ‘Digitalization of Higher Education Management’ are analyzed through experimental research. The results show that the periodicals’ literature statistics show a rapid increase in articles, mainly from 2003 to 2004 and 2006-2007. The higher education management system maintained an accuracy rate of 80.49% regarding system performance. It is confirmed that the management system constructed in this study can predict the learners’ status more accurately and predictably, contributing to the development of intelligent education management in higher education institutions.</p>
</abstract>

In this paper, we first study the multimodal interaction activities in higher education management from the two points of multimodal interaction types and multimodal interaction activity evaluation and propose the perfect way of digital construction of multimodal higher education management from the perspective of students, teachers and higher education institutions. Then, the higher education management system is constructed based on semi-supervised fusion feature learning and homogeneous multimodal features of multimodal machines, and the system architecture and database design are explained in detail. Next, the research literature statistics and system performance of ‘Digitalization of Higher Education Management’ are analyzed through experimental research. The results show that the periodicals’ literature statistics show a rapid increase in articles, mainly from 2003 to 2004 and 2006-2007. The higher education management system maintained an accuracy rate of 80.49% regarding system performance. It is confirmed that the management system constructed in this study can predict the learners’ status more accurately and predictably, contributing to the development of intelligent education management in higher education institutions.

Digital construction of higher education management based on multimodal machine database

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first study the multimodal interaction activities in higher education management from the two points of multimodal interaction types and...

Digital construction of higher education management based on multimodal machine database

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 10. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00862

Schlüsselwörter
Semi-supervised fusion features, Isomorphic multimodality, Database, Higher education, Management system

© 2023 Shen Liu, published by Sciendo

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

Digital construction of higher education management based on multimodal machine database

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 10. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00862

SchlüsselwörterSemi-supervised fusion features, Isomorphic multimodality, Database, Higher education, Management system

© 2023 Shen Liu, published by Sciendo

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

Schlüsselwörter
Semi-supervised fusion features, Isomorphic multimodality, Database, Higher education, Management system