Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
The main problems of the traditional software talent training are that the knowledge learned is out of touch of the industry,weak ability to solve complex engineering problems and the comprehensive quality cannot meet...The main problems of the traditional software talent training are that the knowledge learned is out of touch of the industry,weak ability to solve complex engineering problems and the comprehensive quality cannot meet the requirements of enterprises.Taking the course of digital image processing as an example,this paper analyzes the key steps in the training process in details,and proposes a multiple mode collaborative based software engineering talent training mechanism,which brings the key subjects of teaching materials,teachers,students,enterprises,subjects into a unified training mechanism,so as to provide a new ideas for software talent teaching.展开更多
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
文摘The main problems of the traditional software talent training are that the knowledge learned is out of touch of the industry,weak ability to solve complex engineering problems and the comprehensive quality cannot meet the requirements of enterprises.Taking the course of digital image processing as an example,this paper analyzes the key steps in the training process in details,and proposes a multiple mode collaborative based software engineering talent training mechanism,which brings the key subjects of teaching materials,teachers,students,enterprises,subjects into a unified training mechanism,so as to provide a new ideas for software talent teaching.
