The Quantitative Impact of Higher Education Management Informatization on the Enhancement of Career Planning and Employability of Art Students

Career planning and employability have important significance for art college students. First of all, career planning helps to clarify the career goals and development direction of individuals, so that individuals will not lose their way or go into a blind state in the process of career development [1–3]. Secondly, career planning can help college students understand their own strengths and weaknesses, find suitable career positions, and develop a series of reasonable steps and strategies for realizing career goals [4–5]. In addition, career planning also allows college students to prepare for their careers in advance, including the learning and cultivation of required skills to enhance their employability. And at present, many college students’ career planning and employability are vague, and the information management of colleges and universities has not solved this problem brings opportunities [6–9].

With the wide application of the Internet, big data, artificial intelligence and other new technologies, the management of students in colleges and universities can not only be more efficient information management, but also innovative management methods to improve the management level. The application of information technology provides a new solution for the management of art students in colleges and universities [10–13], which can comprehensively improve management efficiency and service quality, better meet the needs of students, and formulate personalized career planning and employability training programs according to students’ individual needs and development directions, which not only realizes the modernization of college education, but also lays the foundation for the better development of students’ careers [14–17].

Management informatization has become an indispensable part of enterprises, institutions and colleges and universities, and effective management and application can greatly enhance the management efficiency and teaching level of colleges and universities, and then promote the development of the entire education industry. Literature [18] emphasizes that in the information age, the degree of informationization has become an important symbol of a country’s modernization level, and higher education, in addition to innovations in the field of education, should also innovate in teaching and research management and other aspects. Literature [19] describes that in the era of big data colleges and universities based on information technology management mode effectively improve the quality of management and management information reform, but at the same time, it is also necessary to understand the challenges that exist in the process of information technology management innovation, and only by enhancing the understanding of information technology management can we further promote the development of the education industry. Literature [20] reveals that the process of informatization in colleges and universities has had a significant impact on teaching and learning, and the traditional teaching environment such as campuses and libraries has changed in this process, and some colleges and universities have completed the construction of campus networks and realized office automation and so on. Literature [21] indicates that in the new era, the construction of information technology in colleges and universities has become inevitable, and modern information technology is of great significance in enhancing the level of teaching management, improving the competitiveness of colleges and universities, as well as establishing the students’ view of learning, clarifying the direction and motivation of learning, and so on. Literature [22] reveals that the construction of informationization of university management is an important way to improve the level and quality of university management and promote the progress and development of universities. It emphasizes the importance of combining information technology and management in university management reform. It also puts forward coping strategies for the factors affecting the construction of management informatization in colleges and universities.

With the development of society and economy, the career planning of college students becomes more and more important. In the ever-changing society, the career planning of art college students becomes more and more important. The exploration of career planning goals and strategies plays a vital role in their growth and development. Literature [23] outlines the impact of the curriculum of art and design majors in higher vocational colleges and universities on students’ career planning. It was pointed out that rational, effective and forward-looking curriculum design is conducive to enhancing students’ knowledge, fostering their creative thinking and improving their employability. Literature [24] describes the impact of online teaching and learning on art students’ career planning. It shows the characteristics of online teaching such as flexibility and openness. It also identifies the problems in vocational education in the context of career planning in order to develop an online course design program suitable for career planning in art majors. Literature [25] emphasized the importance of artistic practice and career planning for music performance majors, and examined the promotion of students’ growth through the integration of artistic development and career planning. Literature [26] discusses the impact of career path design programs in art colleges on students. With art schools such as the Academy of Fine Arts and the Conservatory of Music having research subjects, it was concluded through interviews that most students did not have clear career paths and their dream was to enter the military academy, while the vast majority of interviewees were positive about their personal experiences. Literature [27] investigated the current status of students’ career planning and career awareness with the Department of Music as the research object. The results showed that the career planning development of music students is at a medium level and needs to be further improved.

Difficulty in employment of college students has become a social focus of the problem, the employment of art students in addition to the overall constraints of the situation of national employment pressure, there are also graduates of the professional skills of knowledge is weak, lack of social competence and other problems, directly or indirectly affect the employment of art students, so it is important to improve the employability of art students. Literature [28] explored the factors affecting the perceived employability of art graduates based on human capital and self-determination theory. Data collected from recent art students through an online survey found that self-directed learning and career adaptation were positively related to perceived employability, and this relationship would be strengthened based on students’ access to lecturers. Literature [29] aimed to understand the factors affecting the employability of art and design students. The questionnaire survey concluded that the factors affecting employability are specialty and adaptability. It also put forward suggestions to improve students’ employability by emphasizing the cultivation of students’ professional ability, strengthening employment guidance and improving teaching methods. Literature [30] introduces the intelligent assessment method of environment perception and autonomous behavioral decision-making ability using NRA, which can assess the employment ability of art and design students. Through experimental analysis, it was concluded that the method helps to promote the teaching reform of art and design majors in colleges and universities. Literature [31] mentioned that the employability of college students includes comprehensive qualities including subject knowledge, psychological quality, etc., and art education as an important part of comprehensive development education should play a unique role, including the development of professional ethics education, emphasizing the creativity of art education, and promoting the development of students’ personalities, and other measures in order to promote the employment of students. Literature [32] emphasizes the importance of employment for graduates. It describes the current employment situation of art graduates and the challenges they face, and proposes employment strategies with high quality and adequate employment in response to the severe employment situation.

This paper investigates the basic characteristics of art students in college management information technology, career planning, and employability, and explores the relationship between these three variables. The above related research hypotheses were firstly put forward, and then three research tools, namely, college management informatization level scale, career planning scale and employability scale, were used to collect relevant data in an art school, and multiple linear regression model, Pearson’s correlation coefficient, and mediation effect model were used to process the data to verify the four hypotheses put forward in the previous article, so as to serve the art colleges and universities in fostering more art students with good career planning ability and employability service, which has certain practical guiding significance.

2

Study design

2.1

Research hypothesis and target audience

2.1.1

Research hypothesis

Hypothesis 1:

There is a significant difference between efficient management informatization, career planning, and employability on some demographic variables.

Hypothesis 2:

The three variables of efficient management informatization, career planning, and employability are each correlated in two.

Hypothesis 3:

Efficient management informationization can positively predict career planning and employability. Career planning can positively predict employability. Employability can positively predict career planning.

Hypothesis 4:

Art students’ career planning has a mediating role in the relationship between college management informatization on employability.

2.1.2

Objects of study

A cluster sampling method was applied to select college students in a university as subjects and conduct three consecutive questionnaire surveys. The first survey was conducted in January 2022, and then a follow-up survey was conducted every six months. To avoid the practice effect, the first field survey was conducted, followed by the second and third online responses, with the order of the previous questionnaire questions disrupted. The first measurement obtained 240 effective subjects, the second measurement obtained 220 effective subjects, the third measurement obtained 200 effective subjects, after integrating the three measurements and removing all kinds of invalid subjects, a total of 200 effective subjects were obtained, and the independent samples t-test was conducted to test the career planning, employability, and efficient management informatization of the attrition subjects and the overall subjects at the time of the first administration of the measurements, and the difference was no statistical significance, indicating that the subject attrition is normal systematic attrition.

2.2

Research tools

2.2.1

Informatization level of university management scale

When measuring the maturity of teaching management informatization, the measurement indexes are summarized with reference to the informatization maturity model and the survey of the actual situation in universities. Informatization maturity model studies the evolution of the organization’s informatization from mature to immature laws, such as what projects have been done in the informatization of colleges and universities, informatization is in what stage, etc., combined with the previous visit to the actual situation of colleges and universities summarized the degree of informatization of the degree of teaching and learning management of colleges and universities to measure the degree of informatization from the information to share information, informatization to improve the efficiency of the degree of information, and whether to repeatedly replace the information system and so on.

2.2.2

Career planning scale

The Career Planning Scale for College Students was utilised. The scale consists of 20 questions divided into four dimensions: self-perception, career perception, career planning, and affective attitudes. A 4-point scale was used, with 1 representing “completely incompatible” and 4 representing “completely compatible”. The higher the overall score, the better the individual’s career planning.

2.2.3

Employability Scale

The Self-Assessment of Employability Scale for College Students was used, with 26 questions. It was also measured on a 5-point Likert scale. In terms of dimensionality, self-development consisted of 7 questions, ranging from 1-7. Interpersonal communication skills consisted of 5 questions, ranging from 8-12. Self-confidence in Employment totaled 5 questions for 13-17. Practical Ability totaled 5 questions for 18-22. Finally, Adaptability is 23-26 questions. There are no questions on this scale that require directional scoring; simply add up the scores for each question.

2.3

Data processing methods

2.3.1

Multiple linear regression models

When there is only one independent variable x predicting dependent variable y, the purpose of linear regression is to fit all the scatter in the plot as closely as possible by a straight line.

Similarly, when a dependent variable contains two or more independent variables the most multiple linear regression [33] is modeled as: 1 $y = β_{0} + β_{1} x_{1} + β_{2} x_{2} \dots + β_{m} x_{m} + δ$ where x₁,⋯x_m is a non-random variable, β₀ is a constant term, β₁,β₂⋯β_m is a regression coefficient, δ is a random error term, and the mathematical expectation is equal to zero.

If n collections are made for y and x, n sets of observations are obtained: 2 $y_{i} = β_{0} + β_{1} x_{1 i} + β_{2} x_{2 i} \dots + β_{m} x_{m i} + δ_{i}$

Represented by a matrix: 3 $y = [\begin{matrix} y_{1} \\ y_{2} \\ ⋮ \\ y_{n} \end{matrix}], x = [\begin{matrix} 1 & x_{11} & \dots & x_{m 1} \\ 1 & x_{12} & \dots & x_{m 2} \\ ⋮ \\ 1 & x_{1 n} & \dots & x_{m n} \end{matrix}], β = [\begin{matrix} β_{0} \\ β_{1} \\ ⋮ \\ β_{m} \end{matrix}], δ = [\begin{matrix} ε_{1} \\ ε_{2} \\ ⋮ \\ ε_{n} \end{matrix}]$

At this point the model can be represented as: 4 $y = X β + δ$

δ is the error revealed between the data fitted to the model and the actual data.

2.3.2

Pearson’s correlation coefficient

Pearson correlation coefficient method is a statistical method to accurately measure the closeness of the relationship between two variables [34], widely used in signal analysis, risk prediction, etc. The size of Pearson correlation coefficient can reflect the strength of the linear correlation between two variables. For variables X = [x₁, x₂, ⋯, x_n]^T and Y = [y₁, y₂, ⋯, y_n]^T, the Pearson correlation coefficient is calculated as: 5 $r = \frac{\sum_{i = 1}^{n} (x_{i} - \bar{x}) (y_{i} - \bar{y})}{\sqrt{\sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2} \sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}}}$

In Eq. (5): $\bar{x}$ and $\bar{y}$ are the mean values of the n data. The value of correlation coefficient r is in the range of (-1, 1), i.e., the closer |r|≤ 1 and |r| are to 1, the higher the correlation between x and y. If r = – 1, it means that there is a perfect negative linear correlation between x and y ; if r = 1, it means that there is a perfect positive linear correlation between x and y; if r = 0, it means that there is no linear correlation between x and y.

Generally, at |r|≥ 0.8, it can be considered as highly correlated; at 0.5 ≤|r|< 0.8, it can be considered as moderately correlated; at 0.3 ≤|r|< 0.5, it can be considered as lowly correlated; and at |r|< 0.3, it indicates that the linear correlation between the two variables is very weak and can be considered as non-linear.

In order to examine the reliability of the correlation coefficient r, a significance test is required. First, the original hypothesis H₀ is that the two variables are uncorrelated, and then the statistic for the test is calculated, which is usually done using the t distribution test, calculated as: 6 $t = | r | \sqrt{\frac{n - 2}{1 - r^{2}}} \sim t (n - 2)$

Finally, the critical value of t_α2(n – 2) is found out using the t distribution table based on the given level of significance α and degree of freedom d_i = n – 2. If |t|> t_α2, the original hypothesis H₀ is rejected, indicating a significant linear relationship between the overall two variables. Since the main focus of the paper is on the analysis of correlation, the level of significance α is not given, but the statistic is derived from the calculation and the α value that satisfies the rejection H₀ is found by checking the t distribution table.

The closer the Pearson’s correlation coefficient is to 1, the more linearly related the two variables are.

2.3.3

Mediated effects model

If the independent variable X affects the dependent variable Y by influencing the variable M, M is said to be the mediating variable [35]. If only one mediating variable is considered in a mediation model, it is called a single mediator model, which is shown in Figure 1.

The single mediator factor model is defined as: 7 $Y = i_{1} + c X + e_{1}, M = i_{2} + a X + e_{2}, Y = i_{3} + c^{'} X + b M + e_{3},$ where coefficient c is the total effect of independent variable X on dependent variable Y ; coefficient a is the effect of independent variable X on mediator variable M ; coefficient b is the effect of mediator variable M on dependent variable Y ; and coefficient c′ is the direct effect of independent variable X on dependent variable Y, with e₁, e₂ and e₃ being the zero-mean error terms, and i₁, i₂ as well as i₃ being the intercept terms. The mediating effect is the product of coefficients a and b. The relationship between the total effect, mediating effect and direct effect is: 8 $c = c^{'} + a b$

Thus, the mediating effect can also be defined as the difference between the total effect and the direct effect i.e. ab = c – c′.

The Bootstrap method [36] is used to construct asymmetric confidence intervals for the mediating effect. The specific steps are as follows: 9 $T = \frac{\hat{a} \hat{b} - a b}{{\hat{σ}}_{a b}}$ where ${\hat{σ}}_{a b} = {{\hat{a}}^{2} {\hat{σ}}_{b}^{2} + {\hat{b}}^{2} {\hat{σ}}_{a}^{2}}^{1 / 2} \cdot \hat{a}, \hat{b}$ and the corresponding standard error can be derived by least squares estimation, great likelihood estimation, and so on. If the confidence level is 1 – δ, asymmetric two-sided confidence intervals can be constructed by statistic T: 10 $C I = [\hat{a} \hat{b} - q_{1 - δ / 2} {\hat{σ}}_{a b}, \hat{a} \hat{b} - q_{δ / 2} {\hat{σ}}_{a b}]$ where δ ∈ (0,1), q_v are the v – quantile of T. Repeated sampling of the data sample points using Bootstrap method yields an approximate distribution of 5, i.e.: 11 $T^{*} = \frac{{\hat{a}}^{*} {\hat{b}}^{*} - \hat{a} \hat{b}}{{\hat{σ}}_{a b}^{*}}$ where ${\hat{a}}^{*}$ and ${\hat{b}}^{*}$ are estimates computed from Bootstrap samples. ${\hat{σ}}_{a} b^{*} = {{\hat{a}}^{*} 2 {\hat{σ}}_{b}^{*} 2 + {\hat{b}}^{*} 2 {\hat{σ}}_{a}^{*}}^{1 / 2}$ , ${\hat{σ}}_{a}^{*}$ & ${\hat{σ}}_{b}^{*}$ are the standard deviations of ${\hat{a}}^{*}$ and ${\hat{b}}^{*}$ respectively. The series of T* obtained by repeated sampling, ordered from smallest to largest, v – quantile is an approximation of the v – quantile q_v of the distribution of the statistic T. At this point, the confidence interval for the mediating effect is estimated: 12 $C I = [\hat{a} \hat{b} - q_{1 - δ / 2}^{*} {\hat{σ}}_{a b}, \hat{a} \hat{b} - q_{δ / 2}^{*} {\hat{σ}}_{a b}]$

3

Findings and discussion

3.1

Descriptive statistical analysis

The current situation of college management informatization of college students is shown in Table 1, from which it can be seen: 1)

The level of college management informatization of college students (M=3.50) is in the medium or medium-high level, indicating that the overall situation of college management informatization of college students is relatively good.

2)

As far as the dimensions are concerned, the degree of informatization to improve efficiency (M=3.75) is at a high level, and whether to replace the information system repeatedly (M=3.19) is low, so attention should be paid to further enhancement research on the degree of system replacement of college management informatization of college students.

Table 1.

The overall characteristics of university information management

	Mean	Standard deviation
Whether to achieve information sharing	3.55	4.25
The degree of efficiency of informationization	3.75	3.65
Whether to change the information system again and again	3.19	3.44
University management informationization	3.50	3.61

The gender differences in college students’ college management informatization are shown in Table 2, from which it can be seen: 1)

There is no significant gender difference in college management informatization of college students.

2)

In the dimensions of college management informatization, there is no significant gender difference in the dimensions of whether to achieve information sharing, the degree of efficiency of informatization, and whether to repeatedly change the information system.

Table 2.

The gender difference of university management informationization

Project	Man	Female	t
Whether to achieve information sharing	24.19±3.35	24.25±4.12	-0.129
The degree of efficiency of informationization	24.25±4.12	24.29±4.03	-0.116
Whether to change the information system again and again	22.11±2.09	22.15±3.06	-0.156
University management informationization	20.45±4.22	20.49±4.26	-0.158

The grade level differences in college students’ college management informatization are shown in Table 3, and the results of one-way ANOVA show that: 1)

Significant grade-level differences in college management informatization (F=6.789,p<0.001) among college students.

2)

On the dimensions of college management informatization, there are significant grade-level differences in the dimension of whether to achieve information sharing (F=5.458,p<0.01), the dimension of the degree of informatization efficiency (F=7.028,p<0.001), and whether to repeatedly replace the information system (F=4.978,p<0.01), and it was found through LSD post hoc multiple comparisons that the senior college management informatization levels were higher than the other three grades.

Table 3.

College management information grade difference

Project	Freshman year	Sophomore	Junior	Senior year	F
Whether to achieve information sharing	24.25±3.12	24.98±4.25	25.69±3.31	26.24±4.55	5.458
The degree of efficiency of informationization	24.21±4.18	24.96±4.05	25.65±4.08	26.27±4.26	7.028
Whether to change the information system again and again	21.11±3.08	22.15±3.25	23.16±3.09	24.18±3.24	4.978
University management informationization	22.45±4.24	23.29±4.25	23.98±4.56	24.69±4.52	6.789

Table 4 shows the current situation of college students’ career planning, which can be seen: 1)

College students’ career planning (M=3.73) is at a medium or medium-high level, indicating that the overall situation of college students’ career planning is relatively good.

2)

In terms of sub-dimensions, college students’ affective attitude (M=3.89) contributes more to college students’ career planning, followed by self-knowledge (M=3.81), career knowledge (M=3.75) and career planning (M=3.48).

Table 4.

The overall characteristics of career planning

	Mean	Standard deviation
Self-cognition	3.81	2.09
Occupational cognition	3.75	3.44
Career planning	3.48	2.59
Love attitude	3.89	2.68
Overall career planning	3.73	8.97

Table 5 shows the gender differences in the career planning of university students, as the results of Table 5 show, there is no significant gender difference in the career planning and all dimensions of university students.

Table 5.

The gender difference of university management informationization

Project	Man	Female	t
Self-cognition	11.19±3.25	11.15±3.05	0.457
Occupational cognition	11.21±4.22	11.26±4.05	-0.175
Career planning	12.18±2.59	12.15±3.05	0.256
Love attitude	12.46±4.22	12.45±4.27	0.155
Overall career planning	74.45±4.25	74.41±4.27	0.785

Table 6 shows the grade level differences in college students’ career planning, and the results of one way ANOVA showed that there were significant differences in college students’ career planning (F=4.976,p<0.01) and their career planning in different grade levels on the self-perception (F=5.478,p<0.01), career perception (F=3.455,p<0.05), career planning (F=6.658,p<0.001), affective attitude (F=4.195,P<0.01) dimensions were significantly different. Post hoc multiple comparisons by the LSD method revealed that first-year students had the lowest scores on the career planning and sub-dimensions and fourth-year students had the highest scores.

Table 6.

College management information grade difference

Project	Freshman year	Sophomore	Junior	Senior year	F
Self-cognition	10.75±2.12	11.92±1.22	11.23±2.35	11.85±2.51	5.478
Occupational cognition	21.81±3.28	22.95±4.45	21.98±4.68	23.27±4.24	3.455
Career planning	12.11±3.28	14.19±3.25	13.16±3.55	14.28±3.46	6.658
Love attitude	15.45±4.54	15.79±4.12	16.76±4.11	16.98±4.59	4.195
Overall career planning	70.34±11.57	74.28±10.15	74.73±10.45	74.96±10.64	4.976

Table 7 shows the current status of employability of university students, from Table 7 it can be seen that. 1)

Employability of college students (M=3.45) is in the middle to high level.

2)

In the sub-dimensions, college students’ self-development (M=3.46), interpersonal communication ability (M=3.32), self-confidence in employment (M=3.41), practical ability (M=3.37), and adaptability (M=3.36) are at an average level, among which interpersonal communication ability (M=3.31) scores the lowest, which shows that college students’ interpersonal communication ability when facing the need to communicate needs to be improve.

Table 7.

The overall characteristics of college students’ employment ability

	Mean	Standard deviation
Self-development	3.46	4.25
Interpersonal skills	3.32	5.12
Job confidence	3.41	5.23
Practical ability	3.37	4.23
Adaptive ability	3.36	4.62
College student employment ability	3.45	22.55

Table 8 shows the gender differences in employability of university students and the results of Table 8 show that there is no significant gender difference in employability of university students.

Table 8.

The gender difference of college students’ employment ability

Project	Man	Female	t
Self-development	15.19±3.21	15.15±3.08	1.457
Interpersonal skills	15.65±4.12	15.52±4.08	1.175
Job confidence	15.18±2.55	15.15±2.05	1.051
Practical ability	15.43±5.27	15.35±4.23	1.155
Adaptive ability	15.95±4.22	15.79±4.37	1.385
College student employment ability	75.69±4.29	75.45±4.28	1.289

Table 9 shows the results of one-way ANOVA for grade differences in college students’ employability shows: 1)

There is a significant grade difference in college students’ employability (F=2.98, p<0.05).

2)

There are significant grade-level differences in interpersonal communication (F=3.18, p<0.05) and self-confidence in employment (F=2.95, p<0.05) dimensions. According to LSD post hoc multiple comparisons, it was found that senior year was higher than the other three grades.

Table 9.

College management information grade difference

Project	Freshman year	Sophomore	Junior	Senior year	F
Self-development	20.75±2.18	20.82±2.22	20.73±2.35	20.85±2.56	1.478
Interpersonal skills	30.71±3.27	32.75±4.25	33.78±4.65	34.52±4.21	3.18*
Job confidence	30.15±3.26	32.11±3.21	33.12±3.51	34.28±3.11	2.95*
Practical ability	25.45±4.24	25.79±4.22	25.76±4.21	25.78±4.59	1.195
Adaptive ability	20.34±5.57	20.28±5.15	20.72±5.41	20.36±5.64	1.976
College student employment ability	132±17.57	135±16.58	138±17.59	139±19.56	2.98*

To sum up, it has been confirmed that efficient management information technology, career planning, and employability differ significantly based on grade level variables.

3.2

Correlation analysis

Pearson correlation analysis was used to test the correlations between the dimensions of college management informatization and the overall level, the dimensions of career planning and the overall level, and the dimensions of employability and the overall level, and the results are shown in Table 10. Numbers 1-15 represent whether or not information sharing is realized, the degree of efficiency of informatization, whether or not the information system is repeatedly replaced, college management informatization, self-perception, career perception, career planning, affective attitudes, career planning, and self-development, respectively. There are fifteen dimensions of interpersonal communication ability, employment self-confidence, practical ability, adaptability, and employability. In research hypothesis 2 we hypothesized that there is a correlation between career planning and employability, and Table 10 shows that there is a correlation between multiple factors of career planning and the dimensions of employability, which provides support for research hypothesis 2.

Table 10.

Correlation analysis of the three elements

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1	1
2	0.312	1
3	0.548	0.255	1
4	0.812	0.215	0.669	1
5	0.315	0.452	0.663	0.658	1
6	0.425	0.521	0.454	0.458	0.458	1
7	0.356	0.568	0.515	0.515	0.46	0.735	1
8	0.45	0.544	0.557	0.558	0.51	0.473	0.498	1
9	0.522	0.569	0.457	0.454	0.563	0.508	0.485	0.356	1
10	0.564	0.532	0.536	0.529	0.441	0.567	0.494	0.566	0.455	1
11	0.45	0.421	0.563	0.556	0.535	0.433	0.569	0.442	0.506	0.432	1
12	0.525	0.285	0.459	0.456	0.562	0.538	0.425	0.564	0.565	0.425	0.521	1
13	0.566	0.325	0.529	0.525	0.461	0.569	0.556	0.569	0.429	0.432	0.258	0.526	1
14	0.454	0.691	0.577	0.581	0.528	0.463	0.568	0.462	0.562	0.359	0.658	0.458	0.542	1
15	0.525	0.547	0.454	0.458	0.585	0.538	0.465	0.527	0.565	0.758	0.774	0.765	0.582	0.152	1

In research hypothesis 3 we hypothesized that there is a correlation between career planning and university management informatization, and Table 10 shows that there is a highly significant correlation between career planning and all dimensions of university management informatization (P<0.01). Research hypothesis 3 is fully supported. The correlation coefficients between self-perception in career planning and the dimensions of college management information technology are moderately correlated between 0.3 and 0.6.

In research hypothesis 3 we hypothesized that there is a correlation between college management informatization and the dimensions of employability, and the results of the study in Table 10 provide partial support for research hypothesis 3, which shows that there is a significant correlation between college management informatization and the dimensions of employability (P<0.01).

In summary, there is a significant positive correlation between career planning and employability, and there is a highly significant correlation between the dimensions of career planning and the dimensions of employability. There is a significant positive correlation between college management informatization and career planning in general, and there is a correlation between the dimensions of career planning and the dimensions of college management informatization. There is a significant correlation between the dimensions of college management information technology and the dimensions of employability.

In this paper, the four dimensions of career planning and the five dimensions of employability are used as predictor variables, and the total score of college management information technology is used as the dependent variable to carry out multiple stepwise regression analysis, in which the dimensions of self-knowledge and practical ability enter the regression equation, and the model fitting goodness of fit is 0.411, which indicates that the model is well fitted. The regression results are shown in Table 11. The regression equation included career planning and practical ability, which accounted for 41.1% of the variance in college management information technology. The regression model coefficients of self-perception, practical ability and career planning with the total score of college management informatization were extremely significant (P<0.001), and the regression model coefficients of practical ability with the total score of college management informatization were highly significant (P<0.01), indicating that college students’ self-perception, practical ability, and career planning have a significant predictive effect on college management informatization.

Table 11.

Regression analysis of three variables

	Model	Beta	T	R²	Adjusted R²	F	P
1	Constants	0.605	17.587	0.365	0.364	438.153	0.000
1	Career planning		20.236
2	Constants	0.756	18.754	0.389	0.387	245.612	0.000
	Career planning	-0.256	20.135
	self-cognition		-6.254
3	Constants	0.721	13.056	0.413	0.411	172.568	0.000
	Career planning	-0.223	17.569
	Self-cognition	0.112	-5.872
	Practical ability	0.652	3.541

3.3

Mediated effects test

To deeply explore the influence of career planning on college management informatization on employability, the mediating role of college management informatization between the two is analyzed through the AMOS structural equation. With career planning as the mediating variable, the mediation model of career planning on college management informatization and employability is established, and the mediation effect model is shown in Figure 2. From the model, it can be seen that the path coefficient of career planning on college management informatization is significant (β = 0.12, P < 0.01), and the path coefficient of college management informatization on employability is significant (β = 0.15, P < 0.01), and the path coefficient of college management informatization on employability is still significant (β = 0.25, P < 0.01) after adding the mediating variable career planning. Thus, it is judged that career planning plays a mediating role between management information technology in art colleges and employability, and hypothesis 4 is verified.

4

Conclusion

Based on the research on the current situation of college students’ employability, career planning and college management informatization, this paper explores the difference situation of these three variables in some aspects, and then analyzes the relationship between the three variables.

The average college management informatization level of art students is 3.50, which is in the middle or moderately high level, indicating that their college management informatization situation is relatively good. There is no significant gender difference in college management informatization of art students, but its difference in grade level is significant, and it is found that the level of college management informatization of senior year is higher than that of the remaining three grades.

The mean value of career planning of art students is 3.73, which is at a medium or medium-high level, and the affective attitude of college students contributes the most to the career planning of college students. There is no significant gender difference in this variable. There is a significant difference in career planning between college students in different grades in terms of self-perception, career perception, and affective attitude. And the first year scored the lowest on career planning and dimensions, while the fourth year scored the highest.

The mean value of art students’ employability is 3.45, which is in the middle to high range. Their interpersonal communication ability dimension scored the lowest, indicating that college students’ interpersonal communication ability needs to be improved. There is no significant gender difference in college students’ employability, but there is a significant difference in grade. The score of the fourth year of college is higher than that of the other three grades.

There is a significant positive correlation between college management automation, career planning, and employability of art students. Efficient management informationization positively predicts career planning and employability. Career planning has a positive impact on employability, and employability has a positive impact on career planning.

Art students’ career planning mediates the relationship between college management informatization and employability.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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The Quantitative Impact of Higher Education Management Informatization on the Enhancement of Career Planning and Employability of Art Students

Ying Qi

Wenze Ma

Published Online: Mar 19, 2025

Received: Nov 02, 2024

Accepted: Feb 18, 2025

DOI: https://doi.org/10.2478/amns-2025-0430

KeywordsMultiple linear regression model, Mediation effect model, Pearson correlation coefficient, Career planning, Employability

© 2025 Ying Qi et al., published by Sciendo

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

Keywords
Multiple linear regression model, Mediation effect model, Pearson correlation coefficient, Career planning, Employability