Journal of Applied Data Sciences
Vol 6, No 1: JANUARY 2025

Applying Factor Analysis to Assess Employment Competitiveness Strategies: A Data Science Perspective

Wang, Yang (Unknown)
Sangsawang, Thosporn (Unknown)
Vipahasna, Piyanan Pannim (Unknown)
Vipahasna, Kitipoom (Unknown)
Watkraw, Wasan (Unknown)



Article Info

Publish Date
30 Jan 2025

Abstract

This study aims to identify and analyze the factors influencing the employment competitiveness of graduates from higher vocational colleges in China and evaluate the impact of targeted programs designed to enhance these factors on graduates' employability. The research involved 17 experts and 100 instructors from Sichuan University of Science and Engineering, utilizing purposive sampling to explore effective career guidance models for improving employment ability. The Delphi technique was applied to synthesize expert opinions on key factors affecting graduate employment competitiveness. Additionally, a sample of undergraduate students participated in the study, with data collected through questionnaires. The findings demonstrate the transformative potential of focused career guidance programs, showing a significant improvement in students' employability post-intervention. These results emphasize the importance of targeted initiatives that equip students with the necessary skills, resources, and career insights to succeed in the job market. By bridging the gap between academia and industry expectations, such programs play a crucial role in preparing students for a smooth transition from university to the professional world, helping them secure meaningful employment opportunities.

Copyrights © 2025






Journal Info

Abbrev

JADS

Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...