Research on the Teaching Reform of Civic and Political Education in Financial Management Specialized Courses Based on Multivariate Statistical Analysis

The main channel of classroom teaching should be used well, and all courses should guard a section of the canal and plant a good field of responsibility, so that all kinds of courses and ideological and political theory courses go in the same direction and form a synergistic effect. Under the guidance of the concept of course ideology and politics, it is necessary to integrate explicit and implicit education organically, and through the integration of course ideology and politics and professional knowledge in professional courses, students can be educated in a subtle way to improve their own moral qualities and ideological behavior [1-4]. It is necessary to be both flagrant and silent, to firmly strengthen the explicit education, but also to actively explore the hidden educational resources, and to use students’ pleasant, vivid and effective teaching methods to convey knowledge with temperature, depth and thickness, so as to achieve the dialectical unity of value rationality and instrumental rationality [5-7].

Schools shoulder the mission of cultivating high-quality talents with professional skills and craftsmanship for China’s socialist modernization, schools must adhere to the cultivation requirements of moral skills and cultivation, and attach great importance to the importance of moral education [8-9]. The Financial Management course, as a backbone course for financial management majors in colleges and universities, is designed to enable students to master basic financial management knowledge and skills, focus on the cultivation of students’ comprehensive vocational ability, and enable students to have the ability to adapt to vocational changes and lifelong learning, i.e., the Financial Management course itself carries the important task of cultivating students’ comprehensive literacy and professionalism [10-13].

At present, there is a big gap between the development status of the teaching of the ideology and politics of the college curriculum and its mission of the times, and some schools and teachers pay more attention to the study of professional courses and neglect the ideological and political education of students [14-15]. Today, the world is in a situation of great change that has not been seen in a hundred years, and the struggle in the field of ideology is severe and complex, so it is more important to exert the power of education to help students to arm their minds with scientific ideas, set up a correct worldview, outlook on life, values, and firm ideals and beliefs [16-18]. Therefore, major universities and colleges should accelerate the construction of course ideology and politics, excavate the depth of the discipline’s ideology and politics resources, integrate the ideology and politics elements into the teaching of professional courses, and play the implicit education function, so that the students can fully comprehend the ideology of the new era while mastering the professional knowledge and skills, and effectively implement the discipline’s cultivation of human beings in the actual classroom, which is of great significance to change the dilemma of ideology and politics education of the college and university finance and management professional courses[19-23].

The article designed and prepared relevant questionnaires, selected appropriate samples, and completed the preparatory work for the study. The research hypotheses on the influencing factors of Civic Education and Teaching Reform in Financial Management Program were made from four aspects, including school support, teachers’ teaching, students’ learning, and teaching-related conditions, and the analysis of variance (ANOVA) method was proposed to observe the influence of the changes in the testing factors on the observed variables. The correlation analysis method is used to analyze the correlation between the variables, and since there are more than two independent variables in the direction of this paper, multiple linear regression is used to explore the influence of different influencing factors on the reform of the financial management professional course of ideological education and teaching. So as to further deepen the reform of the financial management professional program’s civic education and teaching.

2

Survey on teaching and learning in the financial management program

2.1

Selection and use of survey instruments

In order to ensure the quality of the research and the reliability of the data of the relevant research, the author refers to the questionnaire of the relevant course Civics research, matches the relevant evaluation elements of pedagogy, combines the factors of this thesis on the evaluation of the effectiveness of Civics in the financial management course, and starts from multiple aspects, designs and completes the compilation of the Questionnaire on the Teaching and Reform of the Civics Education Course of the Financial Management Students, with a view to the students, the teachers, and the classroom teaching, school management and other perspectives, to dig deeper into the status quo, problems and the causes behind the financial management professional course teaching curriculum Civics in institutions.

2.2

Sample selection

This study was conducted in May-June 2022 with an overall sample of 200 students from each of the freshman, sophomore, junior, and senior years of the financial management program at a university, and questionnaires were distributed to 800 students in the financial management program with the assistance of their classroom teachers.

2.3

Recovery of questionnaires

The entire questionnaire survey process was conducted online and was open for two weeks.The questionnaire survey was conducted anonymously using the Questionnaire Star online platform to distribute 800 questionnaires. 800 questionnaires were returned, and 800 questionnaires were valid.

In this study, the designed questionnaire was tested for reliability and validity, taking into account the objective reality that there are more male and female students in the financial management program itself, the composition of the student population and the situation of only children. It is generally believed that a reliability coefficient above 0.6 indicates good reliability for the questionnaire.Calculations show that the alpha coefficient value of Clonbach’s reliability for each potential variable of the questionnaire in this paper is 0.905, which indicates that the questionnaire has high reliability and can be accepted.The validity was verified using KMO and Bartlett’s test. The KMO metric value was 0.887 and >0.8. The validity of the research data is suitable. In conclusion, it can be presumed that the data from this questionnaire distribution is credible and valid, and it is suitable to conduct the next correlation study.

3

Research hypotheses on impact factors

3.1

Analysis of factors affecting teaching-related conditions

1)

Learning tools

Learning tools in this study refer to the platforms and devices used by students for learning, the network technology environment, the school’s informatization environment, and so on. First of all, a smooth and fast network environment, a browser that is not jammed, web pages that can be opened quickly, and files that can be downloaded smoothly will help to enhance students’ blended learning experience. Secondly, platforms and devices that are interactive or have strong interactive features help students learn independently.Once again, learning platforms and devices are biased towards theoretical knowledge learning and cannot assist students in developing and practicing practical skills. Finally, the application of these intelligent learning platforms and intelligent tools in learning, such as intelligent education cloud platform, intelligent learning platform, classroom robots, etc., can effectively stimulate the learners’ interest in learning and improve the positive and active learning.

2)

Learning atmosphere

A positive, open and free learning atmosphere will make students more willing to participate in learning and create a good and healthy learning environment for students to learn, stimulate students’ motivation and interest in learning, and promote students’ independent learning.

3)

Classroom interaction

In teaching, classroom interaction plays a crucial role. Through classroom interaction, students can better understand and digest the content of the course, and at the same time, they can interact better with the teacher and classmates to improve the learning effect. Therefore, classroom interaction should be emphasized and supported in teaching. In specific practice, classroom interaction can be carried out in a variety of ways, such as group discussion, interactive Q&A, case study, etc., to stimulate students’ interest in learning and enthusiasm, and to improve the efficiency of teaching and learning effects.

3.2

Analysis of factors influencing teachers’ teaching

Students’ personalized learning not only involves their learning, but also cannot be separated from teachers’ teaching. Teachers’ effective teaching support is also an important aspect of promoting students’ learning.The influence of teachers’ teaching on students’ learning mainly lies in the formulation of learning objectives and tasks, supervision and guidance, and the provision of learning resources and content.Teachers’ scientific, reasonable, and effective teaching support can satisfy students’ personalized needs, improve the effect of personalized learning, and promote personalized development.

1)

Learning objectives and tasks

Teachers should fully consider the learning needs, learning characteristics and learning abilities of each student, and set customized learning objectives and design learning tasks for each student. The setting of differentiated learning objectives and tasks will better meet students’ individualized learning needs. At the same time, the setting of differentiated learning objectives and tasks can also promote cooperation and communication among students, expand students’ knowledge and skills, and improve learning efficiency and quality. Therefore, teachers should pay attention to the setting of differentiated learning objectives and tasks to provide students with more personalized and effective learning support and services, so as to improve their learning effectiveness.

2)

Teacher supervision and guidance

Teachers’ supervision and guidance are an important guarantee for students’ learning. Teachers can help students better master the learning content and methods and improve their learning efficiency through supervision and guidance. Teachers can help students rationally arrange their study time and tasks, improve their learning efficiency and quality, and supervise and guide their study plans and progress.

3)

Learning resources

Students need rich and diversified learning resources to meet their learning needs and personality traits. Teachers, as the main providers of learning resources, should first focus on students’ personalized learning needs and provide them with learning resources that meet their needs and characteristics.Secondly, teachers should use a variety of methods and channels to provide students with diverse learning resources, such as papers, teaching videos, online courses, and other forms of learning materials. Again, teachers should provide learning resources combining theory and practical cases, focusing not only on the learning of students’ theoretical knowledge, but also on the cultivation of students’ learning ability.

3.3

Analysis of factors influencing student learning

As the main focus of learning, students’ own characteristics and abilities are the main factors that affect their personalized learning.Through rooted research, it has been found that the factors that affect students’ personalized learning include their information literacy, learning autonomy, learning strategies, and self-efficacy.

1)

Information Literacy

The information society requires every citizen to have basic information literacy, which is the basic requirement for every person in the information society. Good information literacy facilitates students’ personalized learning, which is carried out in the context of teaching and learning with the help of information technology equipment and related platforms, requiring students to have basic information literacy.

2)

Learning autonomy

Learning autonomy refers to students’ learning enthusiasm and initiative. Most students will actively participate in all kinds of learning activities, devote themselves to learning, complete the tasks of learning activities in teaching with high quality, take the initiative to participate in learning discussions, communicate actively with teachers and classmates, be good at expressing their own views, and independently solve the problems encountered in learning. Students with a high degree of autonomy are mainly characterized by active learning, good academic performance, and strong abilities in various aspects.

3)

Learning Strategies

Learning methods and strategies are the skills and means used by students in the learning process. Correct and reasonable learning methods and strategies can assist learners in saving time, mastering knowledge quickly, improving learning efficiency, and making learning twice as effective with half the effort. On the contrary, unreasonable learning methods and strategies will waste a lot of time, reduce learning efficiency, and gradually make learners lose confidence.

4)

Self-efficacy

According to Bandura, self-efficacy refers to people’s confidence in their ability to use the skills they possess to complete a certain task. Students’ self-efficacy has a profound effect on their learning. If students believe they can succeed in a learning task, they are more likely to put in the effort to complete the task. Conversely, if students do not have self-confidence, they may feel frustrated and helpless, and thus be reluctant to attempt to complete learning tasks. In learning, self-efficacy helps students cope better with challenges and difficulties, and when students feel capable of completing tasks, they are more likely to adopt positive learning strategies.

3.4

Analysis of factors influencing school support

The school level should also provide students with more free high-quality database resources, resource retrieval channels, to solve the problem of the lack of resources for teachers and students, the lack of permission to find resources, and to provide more high-quality resources for teachers’ personalized teaching and students’ personalized learning.

4

Methods of analyzing the reform of civic education

4.1

Analysis of variance (ANOVA) methods

ANOVA is an analysis of variance [24] that explores whether a change in the test factor has a significant effect on the observed variable and expresses the degree of variation in the full sample of observations as the sum of squared deviations.

(1)

S_{T} = S_{A} + S_{E} = \sum_{i = 1}^{r} \sum_{j = 1}^{n} {({\bar{x}}_{i} - \bar{x})}^{2} + \sum_{i = 1}^{r} \sum_{j = 1}^{n} {(x_{i j} - \bar{x})}^{2}

\[{{S}_{T}}={{S}_{A}}+{{S}_{E}}=\sum\limits_{i=1}^{r}{\sum\limits_{j=1}^{n}{{{\left( {{{\bar{x}}}_{i}}-\bar{x} \right)}^{2}}}}+\sum\limits_{i=1}^{r}{\sum\limits_{j=1}^{n}{{{\left( {{x}_{ij}}-\bar{x} \right)}^{2}}}}\]

Where: S_A is the sum of the squared deviations of the effects reflecting the extent to which changes in the levels of the factors affect the factors examined. S_E is the sum of squared error deviations reflecting the extent to which random error affects the factors examined. x_ij is the jth observation of group i. $\bar{x}$ $\bar{x}$ is the mean of all observations. ${\bar{x}}_{i}$ ${{\bar{x}}_{i}}$ is the mean of the observations in group i.

The distribution between the effect sum of squares of deviations and the error sum of squares of deviations satisfies the F distribution, where the degrees of freedom of the effect sum of squares of deviations is s – 1 and that of the error sum of squares of deviations is n – s, the test statistic for the F distribution is: (2) $F = \frac{\frac{S_{A}}{s - 1}}{\frac{S_{E}}{n - s}}$ \[F=\frac{\frac{{{S}_{A}}}{s-1}}{\frac{{{S}_{E}}}{n-s}}\]

The F value calculated from the observations is compared with the F_α(s – 1,n – s) value in the statistical table, and if F > F_α(s – 1,n – s), the factors are considered to have a significant effect on the indicator under examination.

Multiple linear regression is the study of how a set of independent variables directly affects a dependent variable. Assuming that a linear relationship is satisfied between dependent variable Y and independent variable X₁,X₂,⋯,X_n, then: (3) $Y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + \dots + β_{n} X_{n} + ε$ \[Y={{\beta }_{0}}+{{\beta }_{1}}{{X}_{1}}+{{\beta }_{2}}{{X}_{2}}+\cdots +{{\beta }_{n}}{{X}_{n}}+\varepsilon \] Where: β₀ is the constant term; β_n is the regression coefficient, which represents the average change in the dependent variable Y 4 when the independent variable X_n is changed by one unit, holding other independent variables constant; and ε is the random error, also known as the residual, which represents the portion of the change in Y that cannot be explained by the independent variable.

4.2

Pearson’s correlation analysis method

The Pearson’s correlation coefficient [25] between the totality of the two indicator variables is used to measure the correlation between the two indicator variables, and its value is between -1 and 1. Let X_i be the i nd indicator variant, ρ_xi,xj be the Pearson correlation coefficient between the i th indicator variant and the jth indicator variant, then the Pearson correlation coefficient between the 2 indicator variables in general is defined as: (4) $ρ_{x_{i}, x_{j}} = \frac{cov (X_{i}, X_{j})}{δ_{x_{i}} δ_{x_{j}}} = \frac{E [(X_{i} - μ_{x_{i}}) (X_{i} - μ_{x_{i}})]}{δ_{x_{i}} δ_{x_{j}}}$ \[{{\rho }_{{{x}_{i}},{{x}_{j}}}}=\frac{\operatorname{cov}\left( {{X}_{i}},{{X}_{j}} \right)}{{{\delta }_{{{x}_{i}}}}{{\delta }_{{{x}_{j}}}}}=\frac{E\left[ \left( {{X}_{i}}-{{\mu }_{{{x}_{i}}}} \right)\left( {{X}_{i}}-{{\mu }_{{{x}_{i}}}} \right) \right]}{{{\delta }_{{{x}_{i}}}}{{\delta }_{{{x}_{j}}}}}\] Where con(X_i,X_j) is the covariance between the ind indicator variable and the jrd indicator variable, δ_x is the overall standard deviation of the ith indicator variant, and μ_x is the overall mean of the ith indicator variant.

The Pearson’s correlation coefficient between the two indicator variants cannot be found directly, but we must first collect the samples corresponding to the two indicator variables, and then find the Pearson’s correlation coefficient between the samples of these two indicator variables, and then use the Pearson’s correlation coefficient between the samples of the two indicator variables to estimate the Pearson’s correlation coefficient between the two indicator variations of the overall.

In order to get the Pearson’s correlation coefficient between the 2 indicator variables in general, this paper fully utilizes the 80 event samples collected, and uses the sample correlation coefficient to estimate the overall correlation coefficient. Let X_ik be the krd component of the ind indicator variable, and r_xi,xj be the Pearson correlation coefficient between the ith indicator variable and the sample of the jth indicator variable, then the Pearson correlation coefficient between the samples of the two indicator variables is defined as: (5) $r_{x_{i}, x_{j}} = \frac{\sum_{k = 1}^{n} (x_{i k} - \bar{x_{i}}) (x_{j k} - \bar{x_{j}})}{\sqrt{\sum_{k = 1}^{n} {(x_{i k} - \bar{x_{i}})}^{2} \sqrt{\sum_{k = 1}^{n} {(x_{j k} - \bar{x_{j}})}^{2}}}}$ \[{{r}_{{{x}_{i}},{{x}_{j}}}}=\frac{\sum\limits_{k=1}^{n}{\left( {{x}_{ik}}-\overline{{{x}_{i}}} \right)}\left( {{x}_{jk}}-\overline{{{x}_{j}}} \right)}{\sqrt{\sum\limits_{k=1}^{n}{{{\left( {{x}_{ik}}-\overline{{{x}_{i}}} \right)}^{2}}}\sqrt{\sum\limits_{k=1}^{n}{{{\left( {{x}_{jk}}-\overline{{{x}_{j}}} \right)}^{2}}}}}}\] where $\bar{x_{i}}$ $\overline{{{x}_{i}}}$ is the sample mean of the first indicator variable and n is the sample size.

4.3

Multiple linear regression models

Regression algorithms are relative to categorical algorithms and are related to the type of value of the target variable Y that you want to predict. If the target variable Y is a categorical variable, such as predicting the gender of the user, predicting the color of the flowers, etc., a classification algorithm is needed to fit the data and make a prediction, and if Y is a continuous variable, such as predicting the revenue of the product, predicting the performance of the sales, etc., a regression model is needed. Linear regression is a simple, versatile and easy to understand model. The equation is obtained by using Y as the dependent variable and X as the independent variable: (6) $Y = α + β X$ \[Y=\alpha +\beta X\]

When parameters α and β are given, a straight line is displayed within the coordinate plot. When only one value of X is used to predict the value of Y, it is called a one-way regression. Univariate linear regression is a calculation to find the most appropriate a straight line to fit the data. Suppose there is a scatter plot based on a set of data, one-way linear regression is to find a straight line that fits the data points in the scatter plot as closely as possible, and the difference between the calculated theoretical data and the actual value is the error, which is denoted by μ in statistics. Thus the equation is obtained: Y = α + βX + μ. The point of linear regression analysis is to find the best straight line that fits the actual data. Here it is necessary to introduce the concept of residuals, the residuals are the difference between the true value and the predicted value, which can be understood as the distance or gap between the two: (7) $e = y - \hat{y}$ \[e=y-\hat{y}\]

For the dependent variable x₁, there is a corresponding independent variable y₁, and the prediction of the resulting ŷ_i, after calculating the value of e_i = y_i – ŷ_i, and then square it, and then calculate each point in the data, and finally add up all the $e_{i}^{2}$ $e_{i}^{2}$ can be calculated to fit a straight line and the actual value of the error between. The so-called best-fitting straight line is the smaller the value of the calculated sum of squares of the residuals, i.e. the smaller the error the better the fit. In order to calculate the minimum sum of squares of the residuals, you can make $Q = \sum_{i = 1}^{n} {[y_{i} - (α + β x_{i})]}^{2}$ $Q=\sum\limits_{i=1}^{n}{{{\left[ {{y}_{i}}-\left( \alpha +\beta {{x}_{i}} \right) \right]}^{2}}}$, the use of calculus for the principle of the extreme value of the α and β partial derivatives, and make its first-order derivatives equal to 0 to solve α and β, that is, the least squares estimation method: (8) $\frac{\partial Q}{\partial α} = 2 Σ (y_{i} - α - β x_{i}) (- 1) = 0$ \[\frac{\partial Q}{\partial \alpha }=2\Sigma \left( {{y}_{i}}-\alpha -\beta {{x}_{i}} \right)(-1)=0\] (9) $\frac{\partial Q}{\partial β} = 2 Σ (y_{i} - α - β x_{i}) (- x_{i}) = 0$ \[\frac{\partial Q}{\partial \beta }=2\Sigma \left( {{y}_{i}}-\alpha -\beta {{x}_{i}} \right)\left( -{{x}_{i}} \right)=0\]

The computational analysis of multiple linear regression [26] is required when there are two or more independent variables. Multiple linear regression analysis addresses the following issues: 1. Determining whether there is a correlation between several specific variables. 2. Predicting or controlling the value of another variable based on the value of one or more variables and can be known to be accurate. 3. Factor analysis, which factors are important and which are minor for the common influence between multiple independent variables of a variable.

In multiple linear regression, a dependent variable begins to be jointly influenced by multiple independent variables, so the form of the equation becomes: (10) $y = β_{0} + β_{1} x_{1} + \dots β_{n} x_{n} + \in$ \[y={{\beta }_{0}}+{{\beta }_{1}}{{x}_{1}}+\cdots {{\beta }_{n}}{{x}_{n}}+\in \] (11) $E (y) = β_{0} + β_{1} x_{1} + \dots β_{n} x_{n}$ \[E\left( y \right)={{\beta }_{0}}+{{\beta }_{1}}{{x}_{1}}+\cdots {{\beta }_{n}}{{x}_{n}}\]

Since an observation in multiple linear regression is no longer a scalar but becomes a vector, the observations of the independent variables become (1,x₁₁,⋯,x_1n), (1,x₂₁,⋯,x_2p), while the observations of the corresponding dependent variables remain unchanged, so superimposing each row of these observations becomes a vector or matrix. Then: (12) $y = [\begin{matrix} y_{1} \\ y_{2} \\ ⋮ \\ y_{n} \end{matrix}] X = [\begin{matrix} 1 & x_{11} & \dots & x_{1 p} \\ 1 & x_{21} & x_{2 p} \\ ⋮ & ⋮ \\ 1 & x_{n 1} & \dots & x_{n p} \end{matrix}]$ \[y=\left[ \begin{matrix} {{y}_{1}} \\ {{y}_{2}} \\ \vdots \\ {{y}_{n}} \\ \end{matrix} \right]X=\left[ \begin{matrix} 1 & {{x}_{11}} & \ldots & {{x}_{1p}} \\ 1 & {{x}_{21}} & {} & {{x}_{2p}} \\ \vdots & {} & {} & \vdots \\ 1 & {{x}_{n1}} & \cdots & {{x}_{np}} \\ \end{matrix} \right]\] (13) $\in = [\begin{matrix} \in_{1} \\ \in_{2} \\ ⋮ \\ \in_{n} \end{matrix}] β = [\begin{array}{r} β_{0} \\ β_{1} \\ ⋮ \\ β_{n} \end{array}]$ \[\in =\left[ \begin{matrix} {{\in }_{1}} \\ {{\in }_{2}} \\ \vdots \\ {{\in }_{n}} \\ \end{matrix} \right]\beta =\left[ \begin{array}{*{35}{r}} {{\beta }_{0}} \\ {{\beta }_{1}} \\ \vdots \\ {{\beta }_{n}} \\ \end{array} \right]\]

After determining the multiple regression equation, the coefficient of determination r² and the standard error of estimate can be used to determine the goodness of fit of the equation. The coefficient of determination is used to explain how much of the variation in Y is explained by the variation in the selected independent variable, and if the value of r² is too large, the presence of multicollinearity between the variables needs to be considered: the standard error of estimation is used to explain the average deviation between the actual value of Y and the estimated value of Ŷ_t when the independent variable is given.

Multiple linear regression equations and univariate linear models are tested in the same way by using t to test each partial regression coefficient and F to test the significance of the entire multiple regression model. The significance test of the equation is used to test whether the equation is valid or not, if the test shows that it is not significant, then the equation is not valid. F test: used for all the independent variables (x₁,⋯,x_n) in the whole for the y linear significance, F test need to look at the Significant F value. P-value and Significant F value is generally required to be less than 0.05, the smaller the result the more significant.

After determining the multiple regression equation, it is also necessary to calculate the variance inflation factor (VIF) of each independent variable to determine whether there is a correlation between the independent variables. If there is a high degree of correlation or complete correlation between the independent variables, then there is multicollinearity among the independent variables, and the effects of each variable on the dependent variable cannot be accurately distinguished. Based on the variance inflation factor it is possible to determine whether multicollinearity exists between each independent variable. It is determined whether the VIF of each independent variable is greater than 5, if so, this variable needs to be eliminated and if the VIF is less than 5, then it can be assumed that the multiple regression equation that has been determined does not suffer from severe multicollinearity.

Residual analysis is used to evaluate the goodness of fit of the linear regression model to the actual data. When testing a multiple linear regression model, the residual plots of the simple linear regression equation between each independent and dependent variable can be analyzed separately. Such a multiple linear regression model can be considered valid if the scatter in the residual plots is shown to be relatively random and it is not obvious that there is a certain pattern in the scatter.

5

Analysis of the scores of access to Civics and each dimension

Table 1 summarizes the total scale of students’ access to civic and political education and the scores for each dimension. The mean score of the total scale of students’ sense of access to civic and political education is 4.09, and the standard deviation of the score is 0.813, which is significantly higher than the median of 3. This indicates that the sample as a whole is at the 4-point scale, which represents the score of “comparatively compliant”, reflecting that the overall sense of access to civic and political education is at a higher level. This indicates that the overall sense of the sample is at a higher level than the median value of 3, which represents a score of 4 for “more compliant”, reflecting that the overall sense of the sample is at a higher level. After the reliability test, the Civic and Political Education Sense of Acquisition Scale for College Students has three dimensions: knowledge acquisition, emotion acquisition and action acquisition, and the theoretical assumption is that these three dimensions are in-depth acquisition levels in sequence. The overall scores of the sample in the three dimensions have some differences, and the mean values of their scores M are, from high to low, emotional acquisition, knowledge acquisition, and action acquisition. Emotional access has the highest mean M, which is higher than the score of 4 representing “more in line with”, indicating that the sample as a whole scores relatively high on the emotional dimension of college students’ sense of access to Civic Education courses. The second highest mean value is knowledge acquisition, which is also above the 4 points representing “more in line”, indicating that the sample as a whole has a relatively high score on the knowledge dimension of the sense of access to the Civic and Political Education courses for college students. Finally, action acquisition is slightly lower than the 4 points representing “comparatively compliant”, but still higher than the middle value of 3 points representing “generally compliant”, indicating that the overall performance of the sample in the action dimension of the sense of access to the Civic and Political Education Classes for College Students is relatively average, lower than that in the dimensions of emotion acquisition and knowledge acquisition. This indicates that the overall performance of the sample in the action dimension of the students’ sense of acquisition of Civic and Political Education is relatively average, lower than that of the emotion acquisition and knowledge acquisition.

Table 1.

Gain the total scale and the scale of each dimension

	Problem number	M	SD	Minimum (N)	Maximum value (X)	Average score
Knowledge acquisition	8	4.06	0.815	1	5	4.021
Affective acquisition	10	4.35	0.744	1	5	4.316
Act up	12	3.87	0.879	1	5	3.908
Total score	30	4.09	0.813	1	5	4.082

From the analysis of score M and related data, it can be seen that in general the sample’s scores on college students’ sense of access to Civic and political education classes are at a high level, reflecting the overall high level of the sample’s sense of access to college students’ Civic and political education classes. From the perspective of each dimension, there are differences in the actual scores of the samples on the dimensions of college students’ sense of access to civic and political education classes, i.e., the scores on the shallow access-knowledge access and emotion access are relatively high, while the scores on the higher level of action access are relatively low, which shows a pattern of inverse correlation between the levels of the sense of access to the college students’ civic and political education classes and the samples’ actual level of access. The law of correlation.

ANOVA F-analysis was conducted with grade level as the independent variable and the total scale of college students’ sense of access to the Civic Education class as well as the scores of the three dimensions as the dependent variable. The results are shown in Table 2. Note: ^*P<0.05, ^**P<0.01, ^***P<0.001.As can be seen from Table 2, the mean scores M of the total scale of the sense of access to college students’ civic and political education classes do not have a relatively significant difference in terms of grade level, and the mean M of the freshmen and senior students is slightly higher than that of the sophomores and juniors. Looking at the scores M on the Knowledge Acquisition, Emotional Acquisition, and Action Acquisition dimensions for the four grade levels revealed that freshman students had the highest score M on the Knowledge Acquisition dimension at 4.03. Senior students had the highest score on both the affective and action acquisition dimensions of the four grade levels. From this, it can be inferred that the possible reason is that first-year students have just finished studying college students’ Civic and Political Education course, and they remember the theoretical knowledge of the classroom of Civic and Political Education course well, and their knowledge acquisition is naturally higher compared with the other grades. With the application of the knowledge reserve in study and life, the deeper emotional acquisition and action acquisition will increase, which rationally explains why the emotional acquisition and action acquisition of the fourth-year students are higher than that of the students in other grades.

Table 2.

The difference in the grade of the student’s thinking

Variable	Freshman year (N=200)		Sophomore (N=200)		Junior (N=200)		Senior year (N=200)		F
Variable	M	SD	M	SD	M	SD	M	SD	F
Knowledge acquisition	4.03	0.811	4.01	0.835	4.01	0.818	3.98	0.949	0.518
Affective acquisition	4.35	0.725	4.26	0.786	4.34	0.756	4.38	0.789	1.226
Act up	3.94	0.849	3.84	0.977	3.96	0.853	4.06	0.752	1.841
Inventory of total	4.08	0.796	4.03	0.861	4.08	0.806	4.13	0.836	1.405

Taking the total scale of college students’ sense of access to civic and political education classes and its standard scores of each dimension as the dependent variable, and converting the grade variable from a categorical variable to a dummy variable as the independent variable respectively, the results of the four one-way regression analyses are shown in Table 3.

Table 3.

Is a form of regression analysis for students

Variable	The regression analysis of the total amount of receiving in the grade			Regression analysis of knowledge obtained in grade			Regression analysis of emotional gain in grade			Regression analysis of grade in grade
Variable	β	R²	F	β	R²	F	β	R²	F	β	R²	F
Student grade	-0.011	0	0.089	-0.032	0.001	1.028	-0.052	0.003	2.112	0.025	0.001	0.611

As can be seen from Table 3, the explanatory quantity of grade level on the total scale of college students’ sense of access to civic education classes is R²=0, and the regression effect (β=-0.011) is also not significant, indicating that the predictive effect of grade level on the total scale of college students’ sense of access to civic education classes is not significant. In terms of the specific dimensions of the scale, the regression effect of grade level (β=0.052) was the most significant on the affective acquisition dimension, with an explanatory variable of 0.3%. Also, the regression effect of grade level on the dimensions of knowledge and action acquisition did not reach a significant level.

The results of the ANOVA F-analysis are shown in Table 4. The results of the ANOVA F-analysis are shown in the distribution of the scores of the total scale of college students’ sense of access to civic and political education classes and the scores of the three dimensions.

Table 4.

Professional difference analysis of acquired sense

Variable	Financial management (N= 200)		Technology (N= 150)		History (N= 150)		Art class (N= 150)		Other (N= 150)		F
Variable	M	SD	M	SD	M	SD	M	SD	M	SD
Knowledge acquisition	4.17	0.797	3.98	0.796	3.98	0.835	4.16	0.759	4.77	0.806	5.312^***
Affective acquisition	4.43	0.916	4.45	0.759	4.26	0.783	4.09	0.765	4.42	0.713	2.563
Act up	4.16	0.818	3.85	0.936	3.84	0.893	4.09	0.786	4.11	0.745	6.692^**
Inventory of total	4.25	0.843	4.07	0.827	4.04	0.836	4.12	0.775	4.45	0.756	4.732^**

In the M distribution of the scores of majors on the total table of college students’ sense of access to civic and political education classes, the scores of students majoring in financial management, art and other majors are higher than those of science and technology and literature and history, which indicates that to a certain extent, the sense of access to college students’ sense of access to civic and political education classes is relatively high among students majoring in financial management, art and other majors, and the sense of access among students majoring in literature and history and science and technology is comparatively low. At the level of specific dimensions, students of financial management majors, arts, and other majors also have higher access to the knowledge dimension than those of arts, history, science, and technology. In the affective access dimension, students in science and engineering scored M the highest. In the action acquisition dimension, students majoring in financial management, arts, and other majors scored M higher than those in science and engineering versus arts and history.It indicates that there are differences in the acquisition of dimensions among students from different majors.

The total scale of college students’ sense of access to Civic and Political Education classes and its standard scores of each dimension were used as dependent variables, and the professional variable was converted from a category variable to a dummy variable as an independent variable to carry out four times one-dimensional regression analyses respectively. The results of the four times one-way regression analysis are shown in Table 5. The amount of explanation of major on the total scale of college students’ sense of access to civic education classes (R²=0.4%) is relatively small, but the regression effect (β=-0.065) is relatively significant, indicating that major has a certain effect on the prediction of the total scale of college students’ sense of access to civic education classes. In terms of the specific dimensions of the scale, the regression effect (β=-0.068) of major on the dimension of emotional access is the most significant, and its explanatory variable is only 0.3%, and the explanatory variables on the dimensions of knowledge access and action access are smaller, and the regression effect is not as significant as that on the dimension of emotional access.

Table 5.

A single regression analysis of college students’ feeling

Variable	The regression analysis of the total amount of acquired inductance			The regression analysis of knowledge			Professional regression analysis of emotional gain			The regression analysis of the professional action
Variable	β	R²	F	β	R²	F	β	R²	F	β	R²	F
Student grade	-0.065	0.004	3.709	-0.055	0.004	2.456	-0.068	0.003	3.812	-0.045	0.002	1.661

6

Multivariate Statistical Analysis of Civics in Financial Management Programs

According to the factor analysis results of the survey scale, four factors could be extracted from all the measured variables, namely “school support”, “teachers’ teaching behavior”, “students’ learning behavior” and “teaching-related conditions”.

The results of the correlation matrix and descriptive analysis of the factors influencing the effect of teaching reform are shown in Table 6. From the results of descriptive analysis, the scores of school support, teachers’ teaching behavior, students’ learning behavior and teaching-related conditions are 4.043, 4.384, 4.227 and 3.397 respectively, which are higher than the theoretical median value of 3, indicating that teachers believe that all these four aspects are more important to the effect of teaching reform. The score of teaching reform effect is 3.583, which is higher than the theoretical median value of 3, indicating that teachers’ evaluation of teaching reform effect is better, and the results of the correlation analysis show that there is a significant correlation between all variables used for the analysis in terms of correlation between variables (p<0.01), which indicates that school support, teachers’ teaching behaviors, students’ learning behaviors, and teaching-related conditions can predict teachers’ teaching reform Effectiveness.

Table 6.

Related matrix and descriptive statistics of teaching effect(N=800)

Variable	M	SD	Teaching effect	School support	Teacher teaching behavior	Student learning behavior	Teaching condition
Teaching effect	3.583	0.7089	1
School support	4.043	0.743	0.368^**	1
Teacher teaching behavior	4.384	0.665	0.367^**	0.598^**	1
Student learning behavior	4.227	0.712	0.364^**	0.586^**	0.756^**	1
Teaching condition	3.397	0.598	0.603^**	0.413^**	0.293^**	0.362^**	1

Based on the results of the variance and correlation analyses, the external and internal influences assumed in the previous period were used as the structural framework of the regression analysis to build a regression model of the influences on the effects of teaching reform and further explore the influences that have a causal relationship with the effects of teaching reform. In the regression model constructed with the effect of teaching reform as the dependent variable, the background characteristics of individual teachers (including the type of university, university region, subject of teaching, and age of teachers) and school support, teachers’ teaching behaviors, students’ learning behaviors, and teaching-related conditions are used as the independent variables in constructing the model, and stepwise regression analysis is conducted on the independent variables and the dependent variables by using the statistical analysis software of spss. The optimized analytical model of the influencing factors on the effect of teaching reform has been obtained, and the influencing mechanisms of each model are shown in Table 7.

Table 7.

Influence factor analysis of teaching reform effect

Control variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
College type	0.05	0.072	0.068	0.036	0.015	0.011
College type	0.279	0.172	0.193	0.452	0.734	0.825
University area	00025	0.021	0.012	0.023	0.005	0.000
University area	0.643	0.705	0.827	0.703	0.905	0.992
Lecture subject	0.098	0.095	0.083	0.074	0.016	0.004
Lecture subject	0.087	0.074	0.117	0.143	0.732	0.911
Teacher age	0.028	0.017	0.002	0.007	0.043	0.045
Teacher age	0.598	0.715	0.973	0.874	0.383	0.284
Student learning behavior		0.368^***	0.215^**	0.153	0.105	0.023
Student learning behavior		0.368^***	0.215^**	0.057	0.154	0.754
Teacher teaching behavior			0.025^**	0.136	0.107	0.152^*
Teacher teaching behavior			0.025^**	0.096	0.142	0.152^*
School support				0.194^**	0.064	0.013
School support				0.194^**	0.273	0.84
Teaching condition						0.438^***
R²	0.014	0.145	0.164	0.186	0.325	0.453
Adjusted R²	0	0.135	0.147	0.169	0.305	0.432
△R²	0.014	0.135	0.014	0.018	0.138	0.132
F	0.987	11.175^***	10.63^***	10.525^***	19.337^***	29.554^***

Model 1 represents the influence mechanism of background information of college teachers on the effect of teaching reform, which is a model about individual characteristic variables of college teachers, and the explainable variance (R²) of this model is 1.4%.

Model 2 incorporates the “student learning behavior” variable based on model 1, and the explained variance of the model is increased to 14.5%, in which the influence of “student learning behavior” (β=0.368) is the largest, which is higher than the variable of teachers’ individual characteristics.

With the addition of the “teacher teaching behavior” variable in Model 3, the explained variance of the model is 16.4%, which is 1.9 percentage points higher than that of Model 2.

Model 4 continued to include the “school support” variable, which increased the explained variance to 18.6%, an increase of 2.2 percentage points over model 3, and the “school support” variable had the greatest impact (β=0.194).

The explainable variance of Model 5 improves to 32.5%, which is 13.9 percentage points higher than Model 4.

The variable of “teaching-related conditions” was further added to model 6, and the variance explainability was increased to 45.3%, which was the best model among the six models, among which the influence of “teaching-related conditions” (β=0.438) was the largest, followed by the influence of “teachers’ teaching behavior”, indicating that the relevant conditions of teaching reform had the greatest influence on the teaching effect, followed by the influence of teachers’ teaching behavior on the teaching effect.

Model 6 is the optimal model in the stepwise regression analysis, and the standardized regression equation for teaching reform effect is obtained from Model 6 as follows:

Teaching reform effect (z) = 0.152* Teachers’ teaching behavior + 0.438* Teaching related conditions.

The standardized regression equation indicates that the two variables of teachers’ teaching behavior and teaching-related conditions are the main factors affecting the effectiveness of teaching reform. From the standardized regression coefficients, the regression coefficients of both variables are positive, indicating that both variables of teachers’ teaching behaviors and teaching-related conditions are positively related to the effect of teaching reform. Among them, the absolute value of beta of teaching-related conditions is the largest, which is 0.438, indicating that teaching-related conditions have a high influence on teaching reform effects and contribute the most to teachers’ teaching reform effects. Therefore, teachers’ teaching behaviors and teaching-related conditions constitute a causal relationship with teachers’ teaching reform effects, and are the main factors influencing teachers’ teaching reform effects.

Multiple linear regression analysis must diagnose the regression model to determine whether the hypothetical model meets the conditions for the use of multiple regression, only to meet the relevant conditions of the regression analysis of the results can be accurate and reliable. Each independent variable and dependent variable used in the study for analysis are continuous variables, the possible influencing factors obtained from the survey are used as independent variables, and the teaching effect is used as the dependent variable to diagnose the model, and the results of the covariance diagnosis of the influencing factor variables and the diagnosis of the serial correlation diagnostic analysis are shown in Table 8.

Table 8.

Diagnosis and sequence correlation diagnosis analysis

Factor variable	VIF	Durbin-Waston
College type	1.064	2.053
University area	1.039
Lecture subject	1.042
Teacher age	2.654
Student learning behavior	2.615
Teacher teaching behavior	1.904
School support	1.498
Teaching condition	1.486

From the results of the analysis, it can be seen that the VIF values of all the influencing factor variables are less than 5, indicating that there is no problem of multicollinearity among the independent variables. The value of the serial correlation diagnosis of variables is 2.053, which is just between 1.9 and 2.1, indicating that there is no serial correlation problem between variables.

The normal distribution of the residuals is shown in Figure 1, from the normal p-p plot of the regression standardized residuals, it can be found that the scatter of the residuals basically falls around the diagonal and is linearly distributed, it can be determined that the residuals of the model obey the requirements of normal distribution, therefore, combining the above results, it can be concluded that the above model of the step regression meets the conditions of the use of multivariate regression analysis and the results of the regression operation are accurate and reliable, with a credibility.

7

Conclusion

The innovation of this paper is to complete the multivariate statistical analysis of the main influencing factors of teaching reform from the perspective of teaching reform, which plays a guiding role in the work of teaching reform of Civic and Political Education.

1) The R²=0 and the regression effect (β=-0.011) of grade on the total table of college students’ sense of access to civic and political education classes is not significant, revealing that the predictive effect of grade on college students’ sense of access to civic and political education classes is not significant. The R²=0.4% and the regression effect (β=-0.065) of major on the total scale of college students’ sense of access to Civic and Political Education classes were relatively significant, revealing that major was effective in predicting the total scale of college students’ sense of access to Civic and Political Education classes. For specific dimensions, the regression effect (β=-0.068) of major on the dimension of emotional access is the most significant, with an explanatory variable of 0.3%, while the regression effects of both the dimension of knowledge access and action access are not significant.

2) Correlation analysis of the influencing factors with the effect of teaching reform, the results of the correlation analysis showed that school support, teachers’ teaching behavior, students’ learning behavior, and teaching-related conditions were significantly correlated with the effect of teaching reform, with scores of 4.043, 4.384, 4.227, and 3.397, respectively.Secondly, the stepwise regression model of the influencing factors of the effect of teaching reform showed that teaching-related conditions and teachers’ teaching behaviors have a causal relationship with the effect of teaching reform, and the above two variables are the main factors affecting the effect of teaching reform, among which, the effect of teaching-related conditions is the most significant, with a maximum absolute value of beta of 0.438.

Project Fund:

1) In 2023, the provincial general topic of Teaching Research in colleges and universities of Jiangxi Province is “Research on Teaching Innovation of Comprehensive Accounting Training Based on the Integration of Production and Education” (Final Certificate No.: JXJG-2023-28-4).

2) In 2018, Jiangxi Province Education Science “13th Five-Year Plan” general topic “Under the background of “Education Poverty Alleviation”, local universities E-commerce major to help rural economic development Strategy research - a case study of Jiangxi Institute of Engineering”(Certificate No.: 13518YB323).

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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Research on the Teaching Reform of Civic and Political Education in Financial Management Specialized Courses Based on Multivariate Statistical Analysis

Li Zou

Pansheng Luo

Published Online: Mar 19, 2025

Received: Nov 07, 2024

Accepted: Feb 14, 2025

DOI: https://doi.org/10.2478/amns-2025-0552

KeywordsANOVA, Correlation analysis, Multiple linear regression model, Curriculum Civic politics

© 2025 Li Zou et al., published by Sciendo

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

Keywords
ANOVA, Correlation analysis, Multiple linear regression model, Curriculum Civic politics