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

<p>In the current educational reform wave, which prioritizes moral development as the fundamental goal and stresses the enhancement of core student competencies, aligning higher education management with student cultivation mechanisms is crucial to educational progress. The Lucas-Kanade (LK) optical flow algorithm is used in this paper to gather behavioral characteristics from student interactions within the cultivation mechanism. A multi-scale convolutional kernel approach is used to fuse these features both locally and globally. A multi-scale feature fusion module subsequently classifies and recognizes these features, with recognition accuracy optimized by a tailored loss function. This approach allows for the timely identification and analysis of students’ aberrant behaviors, which aids in prompt educational interventions. The multi-scale feature fusion model can effectively identify various types of aberrant student behaviors, which aids educators and institutional leaders in their management efforts, as revealed by experimental findings. Statistically, the model’s implementation led to significant improvements in classroom routine compliance, with pre-and post-test p-values in the experimental cohort showing a notable difference (p = 0.001; p &lt; 0.05). The proposed multi-scale feature fusion model promotes the integration of management and training mechanisms in higher education while also supporting the development of students’ learning capabilities. This innovation sets a solid foundation for future educational advancements.</p>
</abstract>

In the current educational reform wave, which prioritizes moral development as the fundamental goal and stresses the enhancement of core student competencies, aligning higher education management with student cultivation mechanisms is crucial to educational progress. The Lucas-Kanade (LK) optical flow algorithm is used in this paper to gather behavioral characteristics from student interactions within the cultivation mechanism. A multi-scale convolutional kernel approach is used to fuse these features both locally and globally. A multi-scale feature fusion module subsequently classifies and recognizes these features, with recognition accuracy optimized by a tailored loss function. This approach allows for the timely identification and analysis of students’ aberrant behaviors, which aids in prompt educational interventions. The multi-scale feature fusion model can effectively identify various types of aberrant student behaviors, which aids educators and institutional leaders in their management efforts, as revealed by experimental findings. Statistically, the model’s implementation led to significant improvements in classroom routine compliance, with pre-and post-test p-values in the experimental cohort showing a notable difference (p = 0.001; p < 0.05). The proposed multi-scale feature fusion model promotes the integration of management and training mechanisms in higher education while also supporting the development of students’ learning capabilities. This innovation sets a solid foundation for future educational advancements.

Integration and Innovation of Higher Education Management and Student Training Mechanisms Based on Multi-scale Feature Fusion

Applied Mathematics and Nonlinear Sciences

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

In the current educational reform wave, which prioritizes moral development as the fundamental goal and stresses the enhancement of core student competencies,...

Integration and Innovation of Higher Education Management and Student Training Mechanisms Based on Multi-scale Feature Fusion

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 29 mar 2024

Przyjęty: 27 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2027

Słowa kluczowe
Optical flow features, Multi-scale convolution kernel, Feature fusion, Higher education management, Student cultivation

© 2024 Yihui Hao., published by Sciendo

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

Integration and Innovation of Higher Education Management and Student Training Mechanisms Based on Multi-scale Feature Fusion

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 29 mar 2024

Przyjęty: 27 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2027

Słowa kluczoweOptical flow features, Multi-scale convolution kernel, Feature fusion, Higher education management, Student cultivation

© 2024 Yihui Hao., published by Sciendo

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

Słowa kluczowe
Optical flow features, Multi-scale convolution kernel, Feature fusion, Higher education management, Student cultivation