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

Abstract

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.