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

General Analysis

Students’ satisfaction with their own skills training is shown in Figure 1. In the content can be seen, the national key vocational colleges and universities and general colleges based on the comparative analysis can be seen, the students of the two schools are more satisfied with their employment skills. For the students of national key vocational colleges and universities, 67.03% of the respondents chose “very satisfied” and “more satisfied”, while the total proportion of general colleges and universities is 61.83%, which can be seen through the specific analysis, both of which exceed 60%. More than 60%. That is to say, in the actual survey process, the proportion of very satisfied and relatively satisfied people is relatively large, and the final results of the options can also maintain a certain degree of consistency. At this stage, more than half of the students are satisfied with the process of their own employment skills training, so on this basis, there is still a lot of room to stimulate the potential of students. Only 12.05% of the respondents from national key vocational schools chose “relatively dissatisfied” and “very dissatisfied” in response to the question about their satisfaction with their employment skills, while the total percentage of students from general colleges and universities was 19.12%. It can be seen that in this regard, general vocational schools have a larger share in it compared to national key vocational schools, reflecting the fact that students are more recognized and affirmed of their potential for employability skills, and therefore have very optimistic prospects for development. However, since national key vocational schools have stronger faculties than general institutions, their advantages in terms of employment skills development are also relatively more obvious, thus creating a higher level of satisfaction. Therefore, under this precondition, general institutions should strengthen the development of students’ innovative consciousness, so that students can enhance their employment skills in practice.

Analysis of differences

In terms of the difference analysis targeting the cultivation of operational skills, the research was mainly based on five different dimensions. The results of the difference analysis of the current situation of the cultivation of employment factors of middle-level students are shown in Table 1. It can be seen through the analysis of the status quo survey that there is a correlation between students’ satisfaction with their own skills and the variability of variables at this stage. Most of the students are satisfied with their employment skills ability.

For vocational schools, due to the different factors that affect the level of students’ skill training, there is a significant difference in the degree of students’ satisfaction with their own skill development. The teachers’ arrangement of the teaching program and the process of acquiring new knowledge and timely transmission will have an impact on students’ employment awareness, in contrast to the changes in textbooks and teaching programs. In terms of cultural foundation, there is a significant difference between it and students’ satisfaction with employment and the effectiveness of employment practices. There is a significant positive correlation between students’ professionalism and their satisfaction with employment (P=0.0000); and there is a small difference between the change of teaching programs and textbooks and students’ employment. And there is a significant positive correlation (p less than 0.05) between faculty strength and students’ employment satisfaction, employment awareness development and employment knowledge acquisition.

By analyzing the relevant data in the table, it is not difficult to find that for students in vocational colleges and universities, due to the different majors studied and gender differences, it leads to a very big difference in the acquisition of innovative knowledge in schools, and this difference mainly refers to the fact that different individuals will use different ways and means to learn innovative knowledge, and the degree of interest is also different. Whether the innovation is based on imitation or practice, it will stimulate students’ awareness of innovation. There is also a significant difference in gender, which occurs because vocational schools have begun to develop students’ operational skills and innovativeness at a higher standard. Its starting point and goal is to provide more professional skills to the community, so schools in professional settings usually have practical aspects regarding automotive maintenance and other specialties. These majors in the learning process, need to be physics, mechanics or engineering, mathematics and computer technology and other knowledge application, and boys are usually very good at this knowledge, so compared to girls, boys have more opportunities to obtain innovative knowledge. This characteristic also shows that boys can occupy a certain advantage over girls in the process of cultivating innovative consciousness and ability, especially in the subjects and knowledge that boys are good at, which can maximize their innovative consciousness.

Logistic model

In multiple regression modeling [25], it is common to use linear regression model and Logistic model [26] and so on. Linear regression models require that the explanatory variable must be a continuous variable, while the explanatory variable in this study is the respondents’ willingness to demand for student employment, and any respondent can only choose to be willing to be employed or not to be employed. This is a typical dichotomous discrete variable that is not suitable for direct use as an explanatory variable in a linear regression model. In actual research, the logistic regression analysis method does not require a linear relationship between the explanatory variable and the explained variable to be analyzed, and has become one of the most commonly used research methods. In this paper, we study how the binary dependent variable is affected by other independent variables. Therefore, we choose a logistic model to analyze the relationship between the dependent and independent variables. In the model, whether students have the willingness to be employed is set as the explanatory variable, which is denoted by Y. Y = 1 indicates that students are willing to be employed and Y = 0 indicates that students are not willing to be employed. According to the Logit econometric model, the logistic transformation of the probability of employment willingness P can be expressed as: (1) logit(pt)=log(pi1−pi)

Further express the above equation as a linear combination of a set of independent variables: (2) logit(pi)=log(pi1−pi)=β0+β1x1i+β2x2i+⋯+βnxni+εi

Where, P_i denotes the probability that the i nd sample students have employment intention: x_n denotes the n th factor that has an effect on employment intention, i.e., the explanatory variable; β₀ is a constant term, and β_n denotes the regression coefficient of the n th influencing factor.

ISM model

Interpretive Structural Modeling (ISM) [27] is a structural modeling technique based on qualitative analysis, which divides a complex system into several subsystems and converts ambiguous ideas into a multilevel model with intuitive structural relationships. In this paper, the ISM model is used to do further structural analysis of the logistically derived factors influencing the significance of the willingness to demand to identify the main influencing factors. The model first uses directed connectivity diagrams to describe the variable relationships, then derives the reachability matrix and reduces it to derive the hierarchical matrix, and finally derives the multilevel recursive ranked directed graphs and builds the explanatory structural model.

Firstly, there are k influencing factors of willingness to demand obtained by using Logistic model, and the willingness to demand denoted by S₀, then the influencing factors are denoted by S_i(i = 1,2,…,K). The adjacency matrix is as follows: (3) Rij={ 1,Si has an effect on Sj when0,When Si has no effect on Sji,j=0,1,2,⋯,k

Secondly, the reachability matrix M can be obtained through Eq. (3): (4) M=(R+I)λ+1=(R+I)λ≠(R+I)λ−1≠⋯≠(R+I)2≠(R+I)

where 1 is the unit matrix, 2 ≤ λ ≤ K, and the operations of the matrix conform to the Boolean algorithm.

Next, the hierarchical relationship between the demand influences is found using the reachability matrix, M, which is determined as follows: (5) L={ Si|P(Si)∩Q(Si)=P(Si);i=0,1,2,⋯,k }

P(S_i) represents the reachable set of all the elements that can be reached from S_i in the reachable matrix, and Q(S_i) represents the prior set of all the elements that can be reached from S_i. After using the formula (5) to get the elements contained in the highest level L₁, the rows and columns corresponding to the elements contained in L₁ are subtracted from the reachability matrix M to get the reduced reachability matrix M′, and repeating the formula (5) to get L₂, and so on to get the elements contained in other levels.