Algorithmic Optimization-Based Skill Development Strategies for Vocational Education Students and Their Employability Enhancement

This paper adopts the questionnaire survey method to issue and collect formal questionnaires to vocational education students. Then, the quantitative analysis method was used to process the 200 recovered valid data by using SPSS software to verify the role of each factor in influencing the skill development of vocational education students through the Logistic binary model regression analysis established in this paper, followed by inputting the identified salient factors of employability skills into the ISM model to explain the logical relationship and hierarchical structure among the factors. Finally, a correlation analysis was conducted regarding the factors that affect students’ employability skills. The results showed that the mean values of general competence, professional competence, learning attitude, and career planning and confidence were 4.1895, 4.3231, 4.2255, and 4.5053 points, respectively, and it was clear that the factors had a significant effect on students’ employability skills training. In addition, the Pearson coefficients of the four dimensions related to the improvement of employment level ranged from 0.1924 to 0.2638, and their p-values were less than 0.05, which shows that there is a significant positive correlation between the improvement of vocational education students’ employability skills and the general competence, professional competence, attitude towards employment, and career planning and confidence.