Research on the Matching Mechanism between Career Planning Education and Job Market from the Perspective of Educational Administrators

With the development of the times, career planning education has been emphasized more and more. Career planning education refers to the process of helping students to understand themselves, their careers, set career goals and plan career development through education. With the continuous change and development of the society, career planning education should also keep pace with the times and adapt to the needs of the times [1-4].

Chinas employment market has gradually shifted from being dominated by state-owned enterprises in the past to a diversified market-oriented employment market. With the development of the economy, the demand for labor in the job market has become more diversified. In addition to the traditional manufacturing and service industries, there is also demand from emerging fields such as the Internet industry and cultural and creative industries. These changes in the job market obviously put forward higher requirements for the development of career planning education [5-8]. However, the current problem lies in the disconnect between career planning education and employment market demand. Many educational institutions still follow the traditional teaching mode and curriculum, ignoring the changes and development of the market. This has led to many students finding, after graduation, that there is a big gap between the knowledge and skills they have learned and the requirements of the workplace, making it difficult to meet the actual needs of employers [9-12]. At the same time, with the rapid adjustment of economic structure, the rise of emerging industries and the transformation of traditional industries, the requirements of the job market for talents are also constantly upgrading, which poses a higher challenge to career planning education. Therefore, while cultivating students self-knowledge ability, we should provide diversified career information and choices, emphasize practice and practical training, and focus on personalized development [13-16]. Only in this way can career planning education be more effective in helping students make correct career choices and planning to realize the interface with the job market [17].

This paper takes career planning education as the independent variable and college students employability as the dependent variable, establishes a research model of career education planning and employability, and puts forward relevant research hypotheses. Educational administrators of colleges and universities in region S were selected as survey respondents, and after collecting data, we used multiple linear regression methods to analyze the correlation between internship practice, mentoring and curriculum design in vocational education planning on employability (analytical thinking dimension, vocational identity dimension, professional competence dimension, interpersonal influence dimension, and personal conduct dimension), so as to clarify the docking mechanism between vocational planning education and the job market. Subsequently, questionnaire surveys were conducted on college students and employers to understand the evaluation of college students on their own employability and the demand of employers for college students employability, and to compare the differences between the two. On this basis, the results of the analysis are combined to discuss the strategies for improving the career planning and employability of college students.

2

Mechanisms for matching career planning education with the job market

With the development of social economy and the adjustment of industrial structure, the career development of university graduates has been widely concerned. How to improve the comprehensive career quality of college students and provide more practical educational resources for career development are the urgent problems of current university education. In this context, employment-oriented career planning education for college students has become particularly important. Based on the perspective of education administrators, this chapter constructs a model with college students career planning education and college students employability as variables to explore the docking mechanism between college career planning education and the job market.

2.1

Research Model and Assumptions

2.1.1

Research model

This paper takes college students career planning education as the independent variable and employability as the dependent variable. The research model of career planning education and employability is shown in Figure 1. Among them, career education planning includes three dimensions such as internship practice, mentor guidance, and curriculum design. The employment ability influenced by career planning education includes five dimensions such as analytical thinking, career identity, professional ability, interpersonal influence, and personal conduct.

2.1.2

Research hypotheses

In order to verify that college students career planning education can improve college students employability, the relevant hypotheses are proposed through the research form of questionnaire, three dimensions of college students career planning education and five dimensions of college students employability are selected for correlation analysis, and the following research hypotheses are proposed:

H1: There is a positive correlation between career planning education on college students employability.

H2: Internship practice in career planning education has a significant positive effect on college students employability.

H2a: There is a significant positive effect of internship practice on analytical thinking dimension.

H2b: There is a significant positive effect of internship practice on career identity dimension.

H2c: There is a significant positive effect of internship practice on the dimension of professional competence.

H2d: There is a significant positive effect of internship practice on the dimension of interpersonal influence.

H2e: There is a significant positive effect of internship practice on the dimension of personal conduct.

H3: There is a significant positive effect of curriculum design in career planning education on employability of college students.

H3a: There is a significant positive effect of curriculum design on analytical thinking dimension.

H3b: There is a significant positive effect of curriculum design on career identity dimension.

H3c: There is a significant positive effect of course design on the dimension of professional competence.

H3d: There is a significant positive effect of course design on interpersonal influence dimension.

H3e: There is a significant positive effect of course design on personal conduct dimension.

H4: There is a positive and significant effect of mentoring in career planning education on employability of college students.

H4a: There is a significant positive effect of mentoring on analytical thinking dimension.

H4b: There is a significant positive effect of mentoring on career identity dimension.

H4c: There is a significant positive effect of mentoring on the dimension of professional competence.

H4d: There is a significant positive effect of mentoring on interpersonal influence dimension.

H4e: There is a significant positive effect of mentoring on personal conduct dimension.

2.2

Questionnaires

In the design of the questionnaire, according to the research model of this paper and the specific content of the research hypothesis that needs to be investigated, the logic and convenience of filling out the questionnaire, as well as the impact of the number of questionnaire questions on the pressure of the subjects to fill out the questionnaire and so on, it is divided into three parts, namely, the basic information of the individual, the situation of college students vocational planning education and the survey on employability.

After the pre-survey and revision, the formal distribution of the questionnaire began. The questionnaires were mainly distributed to educational administrators, including staff and teachers related to career planning education, at universities in Region S. The questionnaires were divided into an electronic survey version and a paper survey version, and the distribution and recovery were carried out through various channels. A total of 254 questionnaires were distributed and 241 were collected, of which 236 were valid questionnaires, with a recovery rate of 94.88%, and an effective recovery rate of about 97.93%.

2.3

Methods of statistical analysis

In this study, the questionnaire was statistically analyzed using SPSS 19.0 software as well as the pivot table tool in EXCEL to understand the basic characteristics and distribution of the data. Multiple regression analysis was also used to explore the relationship between the variables.

In the study of practical problems, the closeness of the relationship between interrelated variables varies. At one extreme, the change in one or some of the variables completely determines the change in the other or some of the variables, and the variables show a completely definite relationship. Regression analysis in statistics is the solution to such problems. Many economic phenomena are often influenced by more than one factor, and the study of the influence of explanatory variables on more than one explanatory variable requires the use of multiple regression models.

2.3.1

Multiple linear regression models

A multiple linear regression model is interpreted as having a linear relationship between variable Y and multiple explanatory variables X₁,X₂,⋯,X_k. The assumption that there is a linear relationship between the explained variable Y and multiple explanatory variables X₁,X₂,⋯,X_k is a multiple linear function of the explanatory variables and is called a multiple linear regression model. I.e: (1) $Y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{k} X_{k} + μ$ where Y is the explained variable (dependent variable), X_j(j = 1,2,⋯,k) is the k explanatory variable (independent variable), β_j(j = 0,1,2,⋯,k) is the k+1 unknown parameter, which is the regression coefficient, and μ is the random error, also known as the residual, which is usually assumed to be μ~N(0,σ²).

The linear equation between the expected value of the explained variable Y and the explanatory variable X₁,X₂,⋯,X_k is: (2) $E (Y) = β_{6} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{k} X_{k}$

It is called the multivariate overall linear regression equation, or overall regression equation for short.

For n set of observations Y_i,X_1i,X_2i,⋯,X_ki(i = 1,2,⋯,n), the system of equations takes the form: (3) $Y_{i} = β_{0} + β_{1} X_{1 i} + β_{2} X_{2 i} + \dots + β_{k} X_{k i} + μ_{i}, (i = 1, 2, \dots, n)$

To wit: (4) ${\begin{array}{l} Y_{1} = β_{0} + β_{1} X_{11} + β_{2} X_{21} + \dots + β_{k} X_{k 1} + μ_{1} \\ Y_{2} = β_{0} + β_{1} X_{12} + β_{2} X_{22} + \dots + β_{k} X_{k 2} + μ_{2} \\ \dots \dots \\ Y_{n} = β_{0} + β_{1} X_{1 n} + β_{2} X_{2 n} + \dots + β_{k} X_{k n} + μ_{n} \end{array}$

Its matrix form is: (5) $[\begin{matrix} Y_{1} \\ Y_{2} \\ ⋮ \\ Y_{n} \end{matrix}] = [\begin{matrix} 1 & X_{11} & X_{21} & \dots & X_{k 1} \\ 1 & X_{12} & X_{22} & \dots & X_{k 2} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & X_{1 n} & X_{2 n} & \dots & X_{k n} \end{matrix}] [\begin{matrix} β_{0} \\ β_{1} \\ β_{2} \\ ⋮ \\ β_{k} \end{matrix}] + [\begin{matrix} μ_{1} \\ μ_{2} \\ ⋮ \\ μ_{n} \end{matrix}]$

To wit: (6) $Y = X β + μ$ where $Y_{n \times 1} = [\begin{matrix} Y_{1} \\ Y_{2} \\ ⋮ \\ Y_{n} \end{matrix}]$ is the vector of observations of the explanatory variables, $X_{n \times (k + 1)} = [\begin{matrix} 1 & X_{11} & X_{21} & \dots & X_{k 1} \\ 1 & X_{12} & X_{22} & \dots & X_{k 2} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & X_{1 n} & X_{2 n} & \dots & X_{k n} \end{matrix}]$ is the matrix of observations of the explanatory variables, $β_{(k + 1) \times 1} = [\begin{matrix} β_{0} \\ β_{1} \\ β_{2} \\ ⋮ \\ β_{k} \end{matrix}]$ is the vector of overall regression parameters, and $μ_{n \times 1} = [\begin{matrix} μ_{1} \\ μ_{2} \\ ⋮ \\ μ_{n} \end{matrix}]$ is the vector of random error terms.

The overall regression equation is expressed as: (7) $E (Y) = X β$

Multiple linear regression analysis is the process of estimating each parameter in the model based on the observed samples, and performing statistical tests on the estimated parameters and regression equations so that the regression model can be utilized for size prediction and analysis. The multiple linear regression model contains multiple explanatory variables, and multiple explanatory variables act on the explanatory variable Y at the same time. To examine the effect of one of the explanatory variables on Y, it is necessary to analyze the model by assuming that the other explanatory variables remain unchanged. Therefore, the regression coefficients in the multiple linear regression model are partial regression coefficients, i.e., they reflect the mean effect of one of the explanatory variables on the dependent variable Y when the other variables in the model are held constant.

Since the parameters β₀,β₁,β₂,⋯β_k are all unknown, they can be estimated using the sample observations (X_1i,X_2i,⋯X_ki;Y_i). If the calculated parameter estimates are ${\hat{β}}_{0}, {\hat{β}}_{1}, {\hat{β}}_{2}, \dots, {\hat{β}}_{k}$ , and the parameter estimates are used to replace the unknown parameters β₀,β₁,β₂,⋯,β_k of the overall regression function, the multiple linear sample regression equation is obtained: (8) ${\hat{Y}}_{i} = {\hat{β}}_{0} + {\hat{β}}_{1} X_{1 i} + {\hat{β}}_{2} X_{2 i} + \dots + {\hat{β}}_{k} X_{k n}$ where ${\hat{β}}_{i} (j = 0, 1, 2, \dots, k)$ is the parameter estimate and Ŷ_i(i = 1,2,⋯,n) is the sample regression value or sample fit, sample estimate of Y_i.

Its matrix expression is in the form: (9) $Y = X β$ where $Y_{n \times 1} = [\begin{matrix} {\hat{Y}}_{1} \\ {\hat{Y}}_{2} \\ ⋮ \\ {\hat{Y}}_{n} \end{matrix}]$ is the column vector of n×1rd-order fitted values for the vector of sample observations of the explanatory variables Y, $X_{n \times (k + 1)} = [\begin{matrix} 1 & X_{11} & X_{21} & \dots & X_{k 1} \\ 1 & X_{12} & X_{22} & \dots & X_{k 2} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & X_{1 n} & X_{2 n} & \dots & X_{k n} \end{matrix}]$ is the n×(k+1) th-order matrix of sample observations of the explanatory variables X, and ${\hat{β}}_{(k + 1) \times 1} = [\begin{matrix} {\hat{β}}_{0} \\ {\hat{β}}_{1} \\ {\hat{β}}_{2} \\ ⋮ \\ {\hat{β}}_{k} \end{matrix}]$ is the column vector of (k+1)×1 th-order estimates of the vector of unknown parameters β.

The deviation between the estimated value of the explanatory variable Ŷ_i obtained from the sample regression equation and the actual observed value Y_i is called the residual e_i: (10) $e_{i} = Y_{i} - {\hat{Y}}_{i} = Y_{i} - ({\hat{β}}_{b} + {\hat{β}}_{1} X_{1 i} + {\hat{β}}_{2 i} + \dots + {\hat{β}}_{i i} X_{k i})$

2.3.2

Solving for parameter estimates

The following assumptions must be met when the multiple linear regression model utilizes ordinary least squares (OLS) to estimate the parameters:

Zero mean assumption: E(μ_i) = 0, i = 1,2,⋯,n, i.e.: (11) $E (μ) = E [\begin{matrix} μ_{1} \\ μ_{2} \\ ⋮ \\ μ_{n} \end{matrix}] = [\begin{matrix} E (μ_{1}) \\ E (μ_{2}) \\ ⋮ \\ E (μ_{n}) \end{matrix}] = 0$

Same variance assumption (variance of μ is the same constant): (12) $V a r (μ_{i}) = E (μ_{i}^{2}) = σ^{2}, (i = 1, 2, \dots, n)$

No self-relevance: (13) $C o v (μ_{i}, μ_{j}) = E (μ_{j} μ_{j}) = 0, (i \neq j, i, j = 1, 2, \dots, n)$ (14) $\begin{matrix} E (μ μ') = [E [\begin{matrix} μ_{1} \\ μ_{2} \\ ⋮ \\ μ_{n} \end{matrix}] (μ_{1}, μ_{2}, \dots, μ_{n})] = E [\begin{matrix} μ_{1}^{2} & μ_{1} μ_{2} & \dots & μ_{1} μ_{n} \\ μ_{2} μ_{1} & μ_{2}^{2} & \dots & μ_{2} μ_{n} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ μ_{n} μ_{1} & μ_{n} μ_{2} & \dots & μ_{n}^{2} \end{matrix}] \\ = [\begin{matrix} E (μ_{1}^{2}) & E (μ_{1} μ_{2}) & \dots & E (μ_{1} μ_{n}) \\ E (μ_{2} μ_{1}) & E (μ_{2}^{2}) & \dots & E (μ_{2} μ_{n}) \\ ⋮ & ⋮ & ⋮ & ⋮ \\ E (μ_{n} μ_{1}) & E (μ_{n} μ_{2}) & \dots & E (μ_{n}^{2}) \end{matrix}] \\ = [\begin{matrix} σ_{μ}^{2} & 0 & \dots & 0 \\ 0 & σ_{μ}^{2} & \dots & 0 \\ ⋮ & ⋮ & ⋮ & ⋮ \\ 0 & 0 & \dots & σ_{μ}^{2} \end{matrix}] = σ_{u}^{2} I_{n} \end{matrix}$

Random error term μ is uncorrelated with explanatory variable X (this assumption holds automatically): (15) $C o v (X_{j i}, μ_{i}) = 0, (j = 1, 2, \dots, k, i = 1, 2, \dots, n)$

The random error term μ follows a normal distribution with mean zero and variance σ²: (16) $μ_{9} \sim N (0, σ_{μ}^{2} I_{n})$

There is no multicollinearity between the explanatory variables: (17) $r a n k (X) = k + 1 \leq n$

That is, the sample observations of each explanatory variable are linearly independent of each other, and the seconds of the matrix of sample observations of explanatory variables X are the number of parameters k+1, thus ensuring a unique estimate of parameter β₀,β₁,β₂,⋯,β_k.

For a multiple linear regression model containing k explanatory variables: (18) $Y_{i} = β_{0} + β_{1} X_{1 i} + β_{2} X_{2 i} + \dots + β_{k} X_{k i} + μ_{i} (i = 1, 2, \dots, n)$

Setting ${\hat{β}}_{0}, {\hat{β}}_{1}, \dots, {\hat{β}}_{k}$ as the estimator of parameter β₀,β₁,⋯,β_k, respectively, yields the sample regression equation: (19) ${\hat{Y}}_{i} = {\hat{β}}_{0} + {\hat{β}}_{1} X_{1 i} + {\hat{β}}_{2} X_{2 i} + \dots + {\hat{β}}_{k} X_{k i}$

The residuals e_i of observation Y_i and regression Y_i are: (20) $e_{i} = Y_{i} - {\hat{Y}}_{i} = Y_{i} - ({\hat{β}}_{0} + {\hat{β}}_{1} X_{1 i} + {\hat{β}}_{2 i} + \dots + {\hat{β}}_{k i} X_{k i})$

By the method of least squares ${\hat{β}}_{0}, {\hat{β}}_{1}, \dots, {\hat{β}}_{k}$ should minimize the sum of the squares of the residuals e_i of all observations Y_i and regressions Ŷ_i, even though: (21) $\begin{array}{l} Q ({\hat{β}}_{0}, {\hat{β}}_{1}, {\hat{β}}_{2}, \dots, {\hat{β}}_{k}) = \sum e_{i}^{2} = \sum {(Y_{i} - {\hat{Y}}_{i})}^{2} \\ = \sum {(Y_{i} - {\hat{β}}_{0} - {\hat{β}}_{1} X_{1 i} - {\hat{β}}_{2} X_{2 i} - \dots - {\hat{β}}_{k} X_{k i})}^{2} \end{array}$

Obtain the minimum value. According to the extremum principle for multivariate functions, Q takes the first order partial derivative of ${\hat{β}}_{0}, {\hat{β}}_{1}, \dots, {\hat{β}}_{k}$ respectively and makes it equal to zero, denoted as: (22) $\frac{\partial Q}{\partial {\hat{β}}_{j}} = 0, (j = 1, 2, \dots, k)$

Unfolding as: (23) ${\begin{array}{l} \frac{\partial Q}{\partial {\hat{β}}_{0}} = 2 \sum (Y_{i} - {\hat{β}}_{0} - {\hat{β}}_{1} X_{1 i} - {\hat{β}}_{2} X_{2 i} - \dots - {\hat{β}}_{k} X_{k i}) (- 1) = 0 \\ \frac{\partial Q}{\partial {\hat{β}}_{1}} = 2 \sum (Y_{i} - {\hat{β}}_{0} - {\hat{β}}_{1} X_{1 i} - {\hat{β}}_{2} X_{2 i} - \dots - {\hat{β}}_{k} X_{k i}) (- X_{1 i}) = 0 \\ \dots \dots \\ \frac{\partial Q}{\partial {\hat{β}}_{k}} = \sum (Y_{i} - {\hat{β}}_{0} - {\hat{β}}_{1} X_{1 i} - {\hat{β}}_{2} X_{2 i} - \dots - {\hat{β}}_{k} X_{k i}) (- X_{k i}) = 0 \end{array}$

Simplify to obtain the following system of equations: (24) ${\begin{array}{l} n {\hat{β}}_{0} + {\hat{β}}_{1} \sum X_{1 i} + {\hat{β}}_{2} \sum X_{2 i} + \dots + {\hat{β}}_{k} \sum X_{k i} = \sum Y_{i} \\ {\hat{β}}_{0} \sum X_{1 i} + {\hat{β}}_{1} \sum X_{1 i}^{2} + {\hat{β}}_{2} \sum X_{2 i} X_{1 i} + \dots + {\hat{β}}_{k} \sum X_{k i} X_{1 i} = \sum X_{1 i} Y_{i} \\ \dots \dots \\ {\hat{β}}_{0} \sum X_{k i} + {\hat{β}}_{1} \sum X_{1 i} X_{k i} + {\hat{β}}_{2} \sum X_{2 i} X_{k i} + \dots + {\hat{β}}_{k} \sum X_{k i}^{2} = \sum X_{k i} Y_{i} \end{array}$

The above (k+1) equation is called the regular equation and its matrix form is: (25) $[\begin{matrix} n & \sum X_{1 i} & \sum X_{2 i} & \dots & \sum X_{k i} \\ \sum X_{1 i} & \sum X_{1 i}^{2} & \sum X_{2 i} X_{1 i} & \dots & \sum X_{k i} X_{1 i} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ \sum X_{k i} & \sum X_{1 i} X_{k i} & \sum X_{2 i} X_{k i} & \dots & \sum X_{k i}^{2} \end{matrix}] [\begin{matrix} {\hat{β}}_{0} \\ {\hat{β}}_{1} \\ {\hat{β}}_{2} \\ ⋮ \\ {\hat{β}}_{k} \end{matrix}] = [\begin{matrix} \sum Y_{i} \\ \sum X_{1 i} Y_{i} \\ ⋮ \\ \sum X_{k i} Y_{i} \end{matrix}]$

Because: (26) $\begin{matrix} [\begin{matrix} n & \sum X_{1 i} & \sum X_{2 i} & \dots & \sum X_{k i} \\ \sum X_{1 i} & \sum X_{1 i}^{2} & \sum X_{2 i} X_{1 i} & \dots & \sum X_{k i} X_{1 i} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ \sum X_{k i} & \sum X_{1 i} X_{k i} & \sum X_{2 i} X_{k i} & \dots & \sum X_{k i}^{2} \end{matrix}] \\ = [\begin{matrix} 1 & 1 & \dots & 1 \\ X_{11} & X_{12} & \dots & X_{1 n} \\ X_{21} & X_{22} & \dots & X_{2 n} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ X_{k 1} & X_{k 2} & \dots & X_{k n} \end{matrix}] [\begin{matrix} 1 & X_{11} & X_{21} & \dots & X_{k 1} \\ 1 & X_{12} & X_{22} & \dots & X_{k 2} \\ ⋮ & ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & X_{1 n} & X_{2 n} & \dots & X_{k n} \end{matrix}] = X^{'} X \\ [\begin{matrix} \sum Y_{i} \\ \sum X_{1 i j} Y_{i} \\ ⋮ \\ \sum X_{k i} Y_{i} \end{matrix}] = [\begin{matrix} 1 & 1 & \dots & 1 \\ X_{11} & X_{12} & \dots & X_{1 n} \\ X_{21} & X_{22} & \dots & X_{2 n} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ X_{k 1} & X_{k 2} & \dots & X_{k n} \end{matrix}] [\begin{matrix} Y_{1} \\ Y_{2} \\ ⋮ \\ Y_{n} \end{matrix}] = X^{'} Y \end{matrix}$

Let $\hat{β} = [\begin{matrix} {\hat{β}}_{0} \\ {\hat{β}}_{1} \\ {\hat{β}}_{2} \\ ⋮ \\ {\hat{β}}_{k} \end{matrix}]$ be the vector of estimates and the sample regression model Y = Xβ+e with both sides multiplied by the transpose matrix X′ of the matrix of sample observations X, then we have: (27) $X^{'} Y = X^{'} X β + X^{'} e$ to obtain a regular system of equations: (28) $X^{'} Y = X^{'} X β$

From the absence of multicollinearity between the explanatory variables it follows that R(X) = k+1, X′X are square matrices of order (k+1), so that X′X is full rank and the inverse matrix (X′X)⁻¹ of X′X exists. Thus: (29) $β = {(X^{'} X)}^{- 1} X^{'} Y$ is the 0LS estimate of vector β.

2.4

Results of regression analysis

In order to explore the correlation of different dimensions of college students career planning education on college students employability, linear regression analyses were conducted with different dimensions of career planning education (internship practice, tutor guidance, and course design) as independent variables and different dimensions of employability as dependent variables.

2.4.1

Analyzing the thinking dimension

The regression analysis of the three dimensions of college students career planning education on the dimension of analytical thinking is shown in Table 1. In the regression analysis of the three dimensions of college students career planning education on the dimension of “analytical thinking” in college students employability, the model P-value: <1.8e-15, indicating that the model is significant at 95% confidence level. From the perspective of the significance of each dimension, internship practice, mentoring and course design have a significant positive relationship with the dimension of analytical thinking in employability (P<0.05). It shows that college students career planning education is conducive to improving the ability of “analytical thinking” in college students employability, and hypothesis H2a, hypothesis H3a and hypothesis H4a are true.

Table 1.

Regression analysis of vocational planning education in thinking

Coefficients	Estimate	Std.Error	t value	Pr(>\|t\|)
(Intercept)	7.552	1.177	2.497	0.000***
Internships	0.386	0.088	0.421	0.017*
Mentor guidance	0.551	0.067	0.153	0.000***
Course design	0.432	0.063	0.151	0.000***
Signif codes: 0.001 *, 0.01 , 0.05 *
Multiple R-squared: 0.4833
P-value: <1.8e-15

2.4.2

Dimensions of professional identity

The regression analysis of the dimensions of college students vocational education on the dimension of career identity is shown in Table 2. The model presents significant at 95% confidence level with its P-value: <2.3e-15. Internship practice, mentoring and curriculum design in college students career planning education all have positive correlations on career identity in college students employability (P < 0.01). Hypothesis H2b, hypothesis H3b and hypothesis H4b passed the test.

Table 2.

Regression analysis of vocational planning education in professional identity

Coefficients	Estimate	Std.Error	t value	Pr(>\|t\|)
(Intercept)	5.217	1.414	3.411	0.000***
Internships	0.374	0.029	2.558	0.007**
Mentor guidance	0.319	0.049	1.295	0.000***
Course design	0.542	0.058	4.216	0.000***
Signif codes: 0.001 *, 0.01 , 0.05 *
Multiple R-squared: 0.4762
P-value: <2.3e-15

The results of the above analysis show that all three dimensions of career planning education have a significant positive effect on the dimension of vocational identity in college students employability, which is conducive to improving college students ability in the dimension of vocational identity such as career awareness, career planning and employment expectations.

2.4.3

Professional competence dimension

The regression analysis of the dimensions of college students career education on the dimension of professional competence is shown in Table 3. All three dimensions of college students career planning education can improve college students professional competence in their employability (P < 0.01), which is conducive to the improvement of college students professional knowledge and competence, professionalism, computers and English and other professional knowledge dimensions, and hypotheses H2c, hypotheses H3c and hypotheses H4c are valid.

Table 3.

Regression analysis of vocational planning education in professional ability

Coefficients	Estimate	Std.Error	t value	Pr(>\|t\|)
(Intercept)	5.288	2.406	2.167	0.000***
Internships	0.443	0.094	4.216	0.004**
Mentor guidance	0.353	0.077	1.458	0.000***
Course design	0.534	0.085	3.289	0.002**
Signif codes: 0.001 *, 0.01 , 0.05 *
Multiple R-squared: 0.4698
P-value: <1.9e-14

2.4.4

Interpersonal influence dimension

The regression analysis of the dimensions of college students vocational education on the interpersonal influence dimension is shown in Table 4. There is a significant positive correlation between internship practice, mentor guidance and curriculum design in career planning education and interpersonal influence dimension in employability, the corresponding regression coefficients are 0.459, 0.564 and 0.396 respectively, with a significance level of 1%. Meanwhile, the P-value:<1.7e-13 of the model indicates that internship practice, mentor guidance and curriculum design can enhance the interpersonal influence ability in the employability of college students, and hypothesis H2d, hypothesis H3d and hypothesis H4d are valid.

Table 4.

Regression analysis of vocational planning education in interpersonal influence

Coefficients	Estimate	Std.Error	t value	Pr(>\|t\|)
(Intercept)	7.388	1.328	4.151	0.000***
Internships	0.459	0.068	2.577	0.002**
Mentor guidance	0.564	0.095	3.269	0.000***
Course design	0.396	0.045	1.346	0.000***
Signif codes: 0.001 *, 0.01 , 0.05 *
Multiple R-squared: 0.4672
P-value: <1.7e-13

2.4.5

Personal conduct dimension

Table 5 shows the regression analysis of the dimensions of college students vocational education on the dimension of personal conduct. There is a positive correlation between the three dimensions of career planning education on the personal conduct of college students employability at the 5% level, in which the influence of mentor guidance is the largest, with a regression coefficient of 0.583, indicating that the hypothesis H2e, hypothesis H3e, and hypothesis H4e pass the test. College students career planning education is conducive to improving college students sense of responsibility, enterprise, work initiative, dedication, so that college students are more diligent, down-to-earth and conscientious, emotionally stable, and other personal conduct dimensions of the ability to work.

Table 5.

Regression analysis of vocational planning education in personal character

Coefficients	Estimate	Std.Error	t value	Pr(>\|t\|)
(Intercept)	7.472	1.488	4.469	0.000***
Internships	0.266	0.041	1.543	0.035*
Mentor guidance	0.583	0.059	5.207	0.000***
Course design	0.486	0.078	2.173	0.004**
Signif codes: 0.001 *, 0.01 , 0.05 *
Multiple R-squared: 0.4735
P-value: <2.1e-15

The above analysis shows that the three dimensions of internship practice, mentor guidance, and curriculum design of college students career planning education have a positive effect on the five dimensions of personal conduct dimension, career identity dimension, and professional knowledge ability to enhance college students employability, which verifies the hypotheses H1~H4 that career planning education in colleges and universities can promote the enhancement of college students employability, and realize the docking with the job market.

3

Differences between the employability of university students and the needs of the job market

Understanding market demand is critical to developing an individuals educational and career path. Only when an individuals skills match the market demand can his or her employment success rate and career satisfaction be improved. Through the questionnaire distributed to college students to let them self-assess the status of their employability, and the questionnaire distributed to enterprises to understand the current demand of employers for college students employability. The above situations are compared to find the difference between the current situation of college students employability and the actual demand of employers.

3.1

Construction of the Employability System

In order to gain a deeper understanding of the current situation of college students employment ability, the employment ability system of contemporary college students is categorized into four major ability systems, i.e., basic ability A1, job-seeking ability A2, career-adaptation ability A3, and career-development ability A4.

Among them, the basic ability includes professional knowledge B1 and professional skills B2 acquired by college students during their school years.

Job-seeking ability includes communication ability B3, practical ability B4, analyzing and decision-making ability B5, adaptability B6 and comprehensive knowledge B7.

Career adaptation ability includes information gathering ability B8, stress resistance ability B9, teamwork ability B10, organization and coordination ability B11, and interpersonal ability B12.

Career development competencies include innovation B13, continuous learning B14, research B15, dedication B16, and career planning competencies B17.

3.2

Questionnaire survey and collection

This survey research mainly on undergraduate graduates, employers as a sample of the survey, the sample is mainly sampling selected S region of the students of various colleges and universities, including education, grammar, economics and management, science and technology, computers, and other majors for research.

A total of 400 questionnaires were distributed, 376 were retrieved, and the valid questionnaires were 358, with an effective recovery rate of 95.21%. Among them, 200 were distributed to enterprises, 176 were recovered, 164 were valid questionnaires, and the effective recovery rate was 93.18%. For undergraduate students, 200 copies were distributed, 200 copies were recovered, 194 copies were valid, and the effective recovery rate was 97.00%.

3.3

Analysis of findings

3.3.1

Results of self-assessment by university students

The current status of the employability skills that college students self-assessed having is shown in Figure 2. The proportion of professional knowledge B1 and professional skills B2 that college students themselves think they possess is the highest, 86.96% and 83.35% respectively, indicating that college students spend most of their time on learning professional knowledge and skills during their four-year college life.

This is followed by college students communication ability B3, analyzing and decision-making ability B5, stress-resistant ability B9, interpersonal ability B12, continuous learning ability B14 and dedication B16, with the proportion ranging from 42.55% to 59.23%. It indicates that by getting along with people in the process of growing up, living independently in college, and bearing the pressure of coursework will exercise the employment ability of undergraduates.

The proportion of adaptability B6, comprehensive knowledge B7, information gathering ability B8, teamwork ability B10, organization and coordination ability B11 and career planning ability B17 among undergraduates employability ranges from 28.86% to 36.25%, which indicates that some undergraduates employability in these aspects is still weak and is neglected in the overall training program.

Compared with the above abilities, undergraduates think that their practical ability B4, innovation ability B13 and scientific research ability B15 are very insufficient, which are 11.8%, 12.25% and 10.41% respectively.

3.3.2

Results of job market demand

Based on the statistics of the questionnaires recovered from social employers, from which we analyze what competencies undergraduates should have when social employers consider them in recruiting undergraduates at present. The current situation of the employment ability of undergraduates needed by the job market is shown in Figure 3. From the results of the survey, it can be seen that most of the enterprises in the recruitment of undergraduate graduates pay most attention to undergraduates practical ability B4 and communication ability B3, accounting for 85.03% and 83.74% of the proportion of the survey.

Secondly, employers generally value undergraduates interpersonal skills B12, professionalism B16, professional skills B2, professional knowledge B1 and adaptability B6, which accounted for 70.48% to 72.82% of the survey. Undergraduates analytical decision-making ability B5, stress-resistant ability B9, teamwork ability B10, organizational and coordination ability B11, and continuous learning ability B14 are also the abilities to be comprehensively examined by employers, and the survey results show that they accounted for 40.38%~53.65% of the weight respectively. For undergraduate innovation ability B13 and scientific research ability B15 only a small number of units on such employment ability is more important, accounting for 26.29% and 17.84% of the total survey sample.

3.3.3

Analysis of differences in employability

According to the questionnaire feedback of undergraduates employment ability status and the current situation of undergraduates employment ability needed by social employers for comparison, the correspondence between the employment ability of undergraduates and the employment market demand is shown in Figure 4. It is not difficult to see that there is a big gap between the expectations of the unit and the actual employment ability status of undergraduates in terms of practical ability B4 (-73.25%), and undergraduates have obvious deficiencies in practical ability. At the same time, the needs of enterprises in terms of adaptability B6 (-38.26%), professionalism B16 (-29.40%) and communication ability B3 (-24.51%) are also much higher than the actual ability level of undergraduates. In terms of professional knowledge B1, professional skills B2, analytical decision-making ability B5 and continuous learning ability B14, the current status of undergraduates abilities and the needs of social employers can basically reach a match.

4

Countermeasures for the enhancement of college students career planning and employability

Synthesizing the above discussion on the docking mechanism between career planning education and the job market, as well as the analysis of the differences between college students employability and the job market demand, the countermeasures for the enhancement of college students career planning and employability are proposed.

4.1

Rationalization of the curriculum

To improve the employability of college students, it is necessary to pay attention to the curriculum, optimize the knowledge structure through the reasonable setting of the curriculum, and closely contact with the current situation of the employment market to cultivate college students oriented to the needs of society. In the process of optimizing the knowledge structure and rationally setting up the curriculum, it is necessary to realize that the knowledge structure is the core of the employability, adjust the professional structure in time, and the professional curriculum should be set up to keep pace with the times, so as to ensure that the direction of talent cultivation is in line with the social demand.

4.2

Developing practical skills

Enhancing practical teaching and cultivating practical ability are crucial to improving the employability of college students. At present, the social practice experience of college students is what employers pay more and more attention to, and at the same time, social practice is also an important way for college students to improve their comprehensive quality. Colleges and universities should increase the pace of school-enterprise cooperation, increase practical teaching, cultivate practical ability, provide more bases for the social practice of college students, enrich the form of practical activities, and provide college students with various types of social practice at all levels.

4.3

Guidance on career planning

Guiding career planning and enhancing job application ability play a key role in enhancing the employability of college students. Career planning for college students can help them develop themselves with clear goals and by avoiding their shortcomings. When improving the employability of college students, colleges and universities should make more efforts to guide college students career planning and check the implementation of the plan, guide college students to plan the best career goals and career development routes, and guide college students to guide their own study and practice according to the career planning.

4.4

Strengthening psychological counseling

Psychological counseling work has an inestimable role in enhancing the employment ability of college students, in order to alleviate the employment pressure of college students, in order to strengthen the psychological counseling work, should strengthen the sense of autonomy of college students, in the play of college students subjective initiative based on the adjustment of college students employment orientation, matching the needs of college students to meet the needs of their own development of the workplace. There are many ways to strengthen psychological counseling, individual counseling and psychological counseling activities are good forms of counseling, through strengthening psychological counseling, so that college students can be better prepared for employment, improve employability.

5

Conclusion

With the development of economy and the progress of technology, the job market is characterized by rapid changes. The article constructs a research model of career planning education and college students employability under the perspective of education administrators, and explores the docking mechanism between career education planning and employment market by combining multiple linear regression analysis. Then it investigates the difference between college students employability and the demand of the employment market, and then proposes countermeasures to improve college students career planning and employability. The following research results are obtained:

1) Internship practice, mentoring and curriculum design in career planning education have a significant positive effect on all dimensions of college students employability, with a significance level of less than 5%. Career planning education can promote the improvement of college students employability through internship practice activities, mentor guidance planning and curriculum teaching, so as to connect with the job market.

2) The job market values college students practical ability (85.03%) and communication ability (83.74%) the most. The gap between the practical ability of college students and the demand of the job market is large at -73.25%, followed by adaptability, dedication and communication ability, and can be matched with the demand of the job market in terms of professional knowledge, professional skills, analytical decision-making ability and continuous learning ability.

3) In the process of enhancing college students career planning and employment ability, we should optimize the knowledge structure, reasonably set up the curriculum, increase practical teaching, cultivate practical ability, guide career planning, enhance job application ability, strengthen psychological counseling, relieve employment pressure, actively explore effective ways to enhance college students employment ability, and promote college students better employment.

Sprache:: Englisch

Zeitrahmen der Veröffentlichung:: 1 Hefte pro Jahr
Fachgebiete der Zeitschrift:: Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere

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Research on the Matching Mechanism between Career Planning Education and Job Market from the Perspective of Educational Administrators

Wei Zhang

Yanwu Zhao

Online veröffentlicht: 17. März 2025

Eingereicht: 28. Okt. 2024

Akzeptiert: 26. Jan. 2025

DOI: https://doi.org/10.2478/amns-2025-0213

SchlüsselwörterMultiple linear regression, Statistical analysis, Research model, Questionnaire survey, Career planning education

© 2025 Wei Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multiple linear regression, Statistical analysis, Research model, Questionnaire survey, Career planning education