IoT-Based Model for Student Ability Segmentation in Higher Education: A Technology and Innovation Management Approach

Abstract

Recent surveys indicate a growing gap between college students' comprehensive abilities and the workforce's actual demands. Addressing this issue, our study innovatively applies Internet of Things (IoT) technology to analyze and evaluate the comprehensive abilities of college students from a management and innovation perspective. We scrutinize the essential elements of a comprehensive ability index system across multiple levels, incorporating views from teachers, students, and biological standpoints. Leveraging IoT technology, we have constructed a sophisticated topological model encompassing 22 secondary ability indicators. This model represents a breakthrough in education management, enabling a more nuanced understanding of student abilities in various disciplines. We employ principal component analysis and feature cluster analysis to establish an integrated evaluation method. This method provides a detailed assessment of student capabilities across six majors: English Translation, Mathematics and Application, Hotel Management, Advertising, Business Administration, and Educational Technology. The results, with scores ranging from 0.0992 to 2.0254, surpass the average baseline of 0, highlighting the model's efficacy in identifying specific areas of improvement. This research is of substantial theoretical and practical importance, particularly for the enhancement of Chinese college medical education. By pinpointing the shortcomings in each major, our IoT-based model facilitates a more scientifically informed approach to student development. It underscores the potential of merging technology and innovation management in higher education, offering a strategic framework to bridge the gap between academic training and professional requirements.