Optimizing the Professional Talent Training System for Civil Aviation Airport Construction Using Factor Analysis Method
Optimizing the talent training system is a basic requirement for improving the level and quality of civil aviation airport construction.The current talent cultivation system lacks comprehensiveness and is disconnected from industry demands.In order to improve the quality of talent cultivation and meet the development needs of the civil aviation industry,this article combines factor analysis to study the optimization of the professional talent cultivation system for civil aviation airport construction.This article first constructs an evaluation index system for talent cultivation quality from the perspective of talent cultivation needs,based on the princi-ple of scientific feasibility.Then,a questionnaire survey was conducted on the 2023 graduates of a civil aviation vocational and techni-cal college,school leaders,and civil aviation unit employees with more than 5 years of work experience.Exploratory factor analysis was conducted using SPSS 16.0 to extract three common factors that can reflect the quality of talent cultivation:professional knowl-edge and language ability,public management ability,and soft skills.Finally,the factor scores and rankings were calculated using re-gression estimation method.The results showed that the common factor and the common measurement value of each variable were all greater than 0.5.Targeted training of students'professional knowledge and language ability can better meet the development needs of the civil aviation industry,but there is still room for improvement in public management ability and soft skills training.The conclusion indicates that factor analysis can provide new ideas and methods for the cultivation of professional talents in civil aviation airport con-struction,and promote the comprehensive upgrading of the talent cultivation system.
talent cultivation systemfactor analysisconstruction of civil aviation airportsprofessional education
NETL
NSTL
万方数据
