Research on the optimization strategy and best practice of college career guidance courses from the perspective of big data analysis
Year: 2025
Type: conference-paper
Abstract: In recent years, career guidance courses have gradually become the focus of attention in the field of higher education as a key initiative for colleges and universities to cultivate students’ employability. Relying on the big data analysis method, this study systematically explores the existing status and characteristics of career guidance courses in local colleges and universities. Firstly, the study collects basic data of career guidance courses in many colleges and universities in China, covering multiple dimensions such as course preparation, class time allocation plan, teaching content structure, etc., and carries out classification and standardisation. Secondly, text mining, cluster analysis and other data processing techniques are comprehensively used to analyse the collected data in depth, and to find out the problems and room for improvement of the current vocational guidance courses in terms of the positioning of the teaching objectives, the construction of the content system, and the innovation of the teaching methods. Based on the results of the data analysis, the study proposes targeted curriculum improvement strategies from the three dimensions of optimising the design of the curriculum system, innovating the teaching implementation path, and strengthening the practical teaching links, aiming to provide an operable reference plan for the reform of the career guidance curriculum in colleges and universities, so as to effectively enhance the competitiveness of students’ employability and competitiveness. The big data analysis framework constructed in this study provides a new perspective for the systematic study of career guidance courses in colleges and universities, and the conclusions of the study have both theoretical innovation and practical guidance value.
Authors: Yu Cai, Yaoxuan Liang
Institution: East University Of Heilongjiang
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4413985829