Construction and Model Application of Evaluation System of Business English Civic Education Based on Traditional Culture

Education is the key to the development of the country and the revitalization of the nation.In 2020, the Ministry of Education issued the Guideline for the Construction of Civics and Politics in Higher Education Programs, which requires the implementation of the fundamental task of establishing moral education, and the full implementation of Civics and Politics in the curriculum of all disciplines and majors in colleges and universities, with the focus on political identity, family and national sentiments, ethical cultivation, rule of law awareness, and cultural literacy [1-4]. In the same year, the Ministry of Education issued the Teaching Guidelines for Undergraduate Foreign Language and Literature Specialties in General Colleges and Universities, which clarified the specifications for cultivating talents with the trinity of quality, knowledge and ability of students majoring in Business English. In the context of curriculum ideology and politics is to organically combine value leadership with the teaching of language knowledge and the cultivation of language application ability, that is, to consciously pay attention to value leadership all the time in the process of knowledge teaching and ability cultivation, and to put value leadership in an important position [5-9]. Civic and political education should be realized through the “main channel” of classroom teaching. Comprehensive Business English, as the core course of Business English majors, is one of the “main battlefield” of Civic and Political Education [10-12]. Based on the necessity and feasibility of implementing the Civic and Political Education in the comprehensive Business English course, the teachers of the course group should actively change their teaching concepts, improve the Civic and Political ability of the course through various ways, highlight the goal of Civic and Political Education in the design of the course objectives, and innovate the teaching content and teaching methods [13-16]. At the same time, a reasonable evaluation system of business English ideological and political education based on traditional culture is established to realize the organic integration of the three objectives of value shaping, knowledge transmission and ability cultivation, with a view to cultivating business foreign language talents to meet the needs of the construction of socialism with Chinese characteristics in the new era [17-20].

Based on the CIPP evaluation model, the G1-CRITIC method and the fuzzy comprehensive evaluation method are used for the study. The evaluation indexes for the quality of the Civics and Politics course were selected, and the subjective and objective weights of each index were determined using the G1-CRITIC method. On this basis, the fuzzy comprehensive evaluation method was used to evaluate the business English and Civics education in G-School. At the same time, the obtained questionnaire data were subjected to validity and reliability tests, correlation analysis and regression analysis to prove the correlation between each dimension and the overall evaluation of Business English Civic and Political Education as well as the impact of individual differences on its overall evaluation level.

2

Construction of the evaluation system of business English ideological education integrated with traditional culture

2.1

CIPP-based Civic and Political Evaluation Index System for Business English Courses

2.1.1

CIPP model

The CIPP evaluation model was proposed by American scholar Stufflebeam on the basis of the improvement of Taylor’s behavioral goal model, which shifts the focus of evaluation from students to the whole curriculum program, breaks through the mode of defining effectiveness only by explicit goals, and integrates the goals of comprehension, appreciation, and emotion that can not be fully transformed into behaviors into the evaluation model as well.The CIPP evaluation model consists of four main elements: background evaluation, input evaluation, process evaluation, and outcome evaluation. Background Evaluation, Input Evaluation, Process Evaluation and Outcome Evaluation, and the relationship between the elements is shown in Figure 1.

The inner circle of the CIPP evaluation model is the core value of the project: the middle circle is the focus of the evaluation, including objectives, plans, actions and results; the outer circle is the four dimensions of the evaluation, including background, inputs, processes and results, which correspond bi-directionally to the objectives, plans, actions and results of the middle circle. The core values of the inner circle determine the goals, plans, actions, and outcomes of the middle circle, while the middle circle and the outer circle influence each other. The CIPP evaluation model is an excellent tool for project evaluation, especially for projects that have been conducted for a long period of time and are expected to receive continuous improvement [21]. Before the implementation of the project, it is necessary to conduct a background evaluation to find out the needs, problems and opportunities of the project in the context of a specific teaching environment, and to determine the objectives; input evaluation corresponds to the project plan, and it is necessary to analyze the available strategies, plans and programs; process evaluation monitors, checks and gives feedback on specific behaviors in the implementation of the project, and to determine the reasonableness of the results of the evaluation of the achievement of the objectives and the effectiveness of the activities for a comprehensive evaluation, modification and refinement of the objectives. [22].

2.1.2

Evaluation index system of Business English Civic Education

Through reviewing relevant literature and studying documents such as the Guiding Outline for the Construction of Civics and Politics in Higher Education Courses, the Overall Program for Deepening the Reform of Educational Evaluation in the New Era, and the Specialized English Course Standards for Higher Vocational Education (2021 Edition), this paper refined 32 descriptive indicators for the evaluation of Civics and Politics in Higher Vocational English Courses based on the CIPP Evaluation Model based on the analysis of some scholars’ quality evaluation models, as Table 1 Shown.

Table 1.

Ideological and political evaluation index system based on CIPP model

Level 1 indicators	Level 2 indicators
Curriculum background	Course orientation
Curriculum background	Course objectives
Course investment	Human resources
	Course resources
	Financial resources
Curriculum implementation	Teaching preparation
	Teaching process
	Learning process
Course effect	Teaching effect
Course effect	Course impact

2.2

Methodology based on the combination of G1-CRITIC indicators for weighting

2.2.1

G1 method for determining subjective weight calculation process

G1 method of subjective weight determination calculation steps: 1)

Determine the relationship between the sequence of indicators to be evaluated

With the help of experts in the identified security risk indicators in ${A_{1}, A_{2}, \dots, A_{n}}$ , select the most important indicators recorded as A₁; then in the remaining n − 1 indicators continue to select the most important indicators recorded as A₂; according to this method until the marking of A_n indicators, thus obtaining the importance of the indicators ranked: A₁ > A₂ > ⋯ > A_n.

2)

Judging the importance of neighboring indicators

Assuming that the ratio of the importance of neighboring indicators is r_k, the formula for calculating r_k is as follows: (1) $r_{k} = \frac{A_{k - 1}}{A_{k}} (k = 1, 2, \dots, n)$

In the formula A_k and A_k−1 are the weights of the k rd indicator and the k − 1th indicator. 3)

Calculation of indicator weights

By assigning the importance r_k between neighboring indicators by experts, the weight A_k of the n th indicator is calculated by the formula: (2) $A_{k} = {[1 + \sum_{k = 2}^{n} \prod_{i = k}^{n} r_{i}]}^{- 1}$

Then from the weights of Ak the weights of the indicators of the n − 1, n − 2, …, 2, 1st can be calculated with the formula: (3) $A_{k - 1} = A_{k} \times r_{k} (k = 1, 2, \dots, n - 1, n)$

4)

Calculation of weights of secondary indicators

The calculation of the weights of the second-level indicators is the same as the above process of calculating the weights of the first-level indicators, which only requires the experts to assign values to each second-level indicator r_k, and repeat the above operation to calculate the weights of all the second-level indicators.

2.2.2

Calculation process for determining objective weights by the CRITIC method

CRITIC method objective weight determination calculation steps: 1)

Construct evaluation index set

Assuming that there are m evaluation subject and n evaluation indicators for the safety risk evaluation of underground space engineering construction, the indicators of the i rd rating subject of the j th are expressed by x_ij, from which the evaluation matrix can be constructed: (4) $X = [\begin{matrix} x_{11} & \dots & x_{1 n} \\ ⋮ & ⋱ & ⋮ \\ x_{m 1} & \dots & x_{m n} \end{matrix}]$

2)

Dimensionless processing of the evaluation matrix

Because the above indicator system involves different dimensions and subjects, the indicators in the unit, order of magnitude and other aspects of the existence of large differences, so the evaluation matrix of the indicators in the data of the dimensionless processing of the standardized matrix X^*, evaluation matrix standardized processing formula, positive indicators of the dimensionless processing of the formula (5), negative indicators of the dimensionless processing of the formula (6). (5) $x_{i j}^{*} = \frac{x_{i j} - \min_{i} x_{i j}}{\max_{i} x_{i j} - \min_{i} x_{i j}}$ (6) $x_{i j}^{*} = \frac{\max_{i} x_{i j} - x_{i j}}{\max_{i} x_{i j} - \min_{i} x_{i j}}$

In the formula, min_ix_ij and max_ix_ij are the maximum and minimum values of row i and column j of the evaluation matrix, respectively. 3)

Calculation of indicator variability

CRITIC method indicator variability is expressed by the standard deviation, the larger the standard deviation indicates that the greater the difference in the value of the indicator, the more information is released. (7) ${\bar{x}}_{j} = \frac{1}{m} \sum_{i = 1}^{m} x_{i j} (i = 1, 2, \dots, m; j = 1, 2, \dots, n)$ (8) $S_{j} = \sqrt{\frac{\sum_{i = 1}^{m} {(x_{i j} - {\bar{x}}_{1})}^{2}}{m - 1}} (i = 1, 2, \dots, m; j = 1, 2, \dots, n)$ (9) $V_{j} = \frac{S_{j}}{{\bar{x}}_{j}} (j = 1, 2, \dots, n)$

${\bar{x}}_{j}$ represents the mean of the jnd indicator in the evaluation matrix, S_j represents the standard deviation of the j th indicator, and V_j is the coefficient of variation of the j th indicator. 4)

Indicator conflict and information calculation

The conflict of indicators is expressed by linear correlation coefficient, the larger the coefficient is, the smaller the conflict with other indicators is, the more the evaluation content is repeated with other indicators, and the weight of the indicator should be reduced appropriately, and the formula is as in (10): (10) $R_{j} = \sum_{i = 1}^{n} (1 - r_{i j}) (i = 1, 2, \dots, m; j = 1, 2, \dots, n)$

The amount of information is denoted by C_j. The larger the C_j, the more information is contained, the greater the effect on the evaluation matrix and the more weight should be given to it. C_j The formula is as in (11): (11) $C_{j} = V_{j} \times R_{j} \times S_{j} (j = 1, 2, \dots, n)$ 5)

Calculation of indicator weights

After calculating the above formula, we can know the formula for calculating the weight w_j of the j st indicator in the evaluation matrix, as in (12): (12) $w_{j} = \frac{C_{j}}{\sum_{j = 1}^{n} C_{j}} (j = 1, 2, \dots, n)$

2.2.3

G1-CRITIC method of portfolio weight determination

In this paper, we use the preference coefficient tool for linear weighting to determine the portfolio weights, then the portfolio weight W_z is calculated as: (13) $w_{z} = α w_{M a s t e r} + (1 - α) w_{G u e s t}$

Formula α is a linear weighting coefficient with a value range of $(0 - 1)$ . Usually, the value of α is 0.5, so that the subjective and objective weights each account for half of the weight, so that the design can make the weight of the indicator more reasonable.

2.3

Fuzzy Comprehensive Evaluation of Civic Education in Business English Course

2.3.1

Principles of the Fuzzy Integrated Evaluation Method (FIEM)

Fuzzy comprehensive evaluation method is a kind of evaluation method that uses the principle of fuzzy mathematics to synthesize the multi-level and multi-category factors affecting a certain thing, and uses some kind of arithmetic to get the evaluation results of the research object. In this process, it is necessary to determine the indicator set, comment set and weight set of the evaluation object, and assign scores to the comment set, and use the weight of each indicator and the score value of the comment set to establish a fuzzy judgment matrix [23]. The implementation of this method is simple, easy to grasp, not only through the final evaluation of the size of the score value in order to evaluate the object of sorting, but also the use of the principle of maximum affiliation to determine the level of the evaluation object.

2.3.2

Fuzzy integrated evaluation algorithms

1)

Determination of indicator set

Indicator set $(U)$ is mainly constructed according to the hierarchy of influencing factors of the evaluation object. Different levels and categories of influencing factors correspond to different sets of evaluation indicators. Setting the set to $U = {U_{1}, U_{2}, U_{3} \dots, U_{n}}$ means that the indicator set consists of j subsets of evaluation indicators. Similarly, the set of secondary indicators can be set to $U_{n} = {U_{n 1}, U_{n 2}, U_{n 3} \dots, U_{n j}}$ , indicating that the secondary indicator consists of a subset of j evaluation indicators. The set of tertiary indicators can be expressed as follows: (14) $U_{n j m} = {U_{n j 1}, U_{n j 2}, U_{n j 3} \dots U_{n j m}}$ (15) $U = {U_{1}, U_{2}, U_{3} \dots, U_{n}}$

2)

Determining the evaluation set

The rating set, i.e. the set of evaluation results, is an uncertain range of values that contains all the evaluation results that the evaluation subject may make on the evaluation object. In this study, a five-point Richter scale was used to implement the questionnaire survey, so the evaluation set can be set to $V = {V_{1}, V_{2}, V_{3}, V_{4}, V_{5}}$ , which means excellent, good, average, qualified, unqualified, and the higher the score means the better the performance of the indicator. (16) $V = {V_{1}, V_{2}, V_{3}, V_{4}, V_{5}}$

3)

Construct a fuzzy judgment matrix

The fuzzy judgment matrix exists in the single-factor evaluation, which is mainly to judge the membership degree of the second-level index to which the third-level index belongs through the comprehensive evaluation, and then the single-factor evaluation of the second-level indicators can be carried out, the fuzzy judgment matrix can be established to determine the membership degree of each first-level index, the single-factor evaluation is implemented layer by layer, and the corresponding fuzzy judgment matrix is established to evaluate the evaluation index u_i in the index set U, and r_ij represents the membership degree of the i th evaluation index to the j th element in the comment set V, then the index factor The fuzzy evaluation results of u_i can be expressed as: (17) $r = \frac{N u m b e r o f \exp e r t s s e l e c t i n g V_{i} f o r a n i n d i c a t o r}{T o t a l n u m b e r o f \exp e r t s}$ (18) $R = [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 j} \\ r_{21} & r_{22} & \dots & r_{2 j} \\ \dots & \dots & \dots & \dots \\ r_{i 1} & r_{i 2} & \dots & r_{i j} \end{matrix}]$

4)

Determine the weight set

The weights of the indicators determined in the previous section can be directly used as the weight set W in the fuzzy evaluation method, expressed as: (19) $w = {w_{1}, w_{2}, w_{3} \dots, w_{i}}$

5)

Calculation of fuzzy evaluation score

The final fuzzy evaluation score B is calculated from the weight vector W and the fuzzy relationship matrix R by using appropriate operators to prove the affiliation of each index in each evaluation level. The commonly used fuzzy synthesis operators are main factor determinant, main factor highlighting, taking small and bounded type and weighted average type, and according to the characteristics of each operator, the weighted average type is used for the operation. After that, according to the principle of maximum affiliation, the largest value is selected as the final data. At the same time, it is also necessary to normalize the fuzzy evaluation scores to get the final evaluation results. (20) $\begin{array}{rcl} B & = & W * R = {w_{1}, w_{2}, w_{3} \dots, w_{i}} * [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 j} \\ r_{21} & r_{22} & \dots & r_{2 j} \\ \dots & \dots & \dots & \dots \\ r_{i 1} & r_{i 2} & \dots & r_{i j} \end{matrix}] \\ = & {b_{1}, b_{2}, b_{3} \dots, b_{n}} \end{array}$

In the process of fuzzy synthesis operation, different operators have different advantages. The small-big operator only considers the largest and mainly influential index factors in the affiliation degree; the multiplication-big operator also emphasizes the large operation; the small-bounded operator focuses on the superposition and consideration of the less influential factors; the multiplication-bounded operator takes all the indexes into account in a balanced way according to the weights and sizes, which ensures the reasonableness and logic of the calculation process. Therefore, the multiplicative-bounded operator is chosen for the calculation.

3

Analysis of the application of the evaluation system of business English civic education

In order to further amend and improve the effectiveness and practicability of the evaluation index system of Business English Civic and Political Education, this study, in connection with the actual situation, selects a certain representative G school as an individual case to carry out the practical exploration of this evaluation index system, and selects the questionnaire survey to issue questionnaires to the professional teachers and students to get the results of the survey.

3.1

Analysis of the weights of evaluation indexes of Business English Civic Education

Table 2 shows the weight values of each constituent element in the evaluation system of Business English Civic and Political Education in colleges and universities. According to the size of the weight values and the combination of weight values, the relative importance of each constituent element can be seen, which also illustrates that some aspects of the current Business English Civic and Political Education still need to be strengthened from one side. In the following, the rationality of the weights of the elements at each level will be analyzed one by one according to the actual needs of the development of Business English, Civic and Political Education. Based on the four elements of the CIPP model, this study identifies four first-level indicators and ranks them in order of weighted value from the largest to the smallest as follows: course implementation (0.4354) > course input (0.2976) > course effect (0.1838) > course background (0.0832).

Table 2.

The weight of ideological and political education evaluation index

Level 1 indicators	Final weight	Level 2 indicators	Final weight
Curriculum background	0.0832	Course orientation	0.3671
Curriculum background	0.0832	Course objectives	0.6329
Course investment	0.2976	Human resources	0.2482
		Course resources	0.5741
		Financial resources	0.1777
Curriculum implementation	0.4354	Teaching preparation	0.2286
		Teaching process	0.3537
		Learning process	0.4177
Course effect	0.1838	Teaching effect	0.8352
Course effect	0.1838	Course impact	0.1648

3.2

Data acquisition and organization

3.2.1

Questionnaire reliability tests

The reliability test of the questionnaire can measure the reliability of the questionnaire, and the higher the reliability of the questionnaire, the greater the consistency of the results obtained after several tests. In this study, Cronbach’s alpha coefficient was used as an index to measure the reliability of the questionnaire, and a larger Cronbach’s alpha coefficient indicates that the questionnaire has a higher level of reliability and trustworthiness. The Cronbach’s alpha coefficients of the four level one indicators were 0.842, 0.885, 0.852, and 0.879, respectively, obtained by using SPSS26.0 software to calculate the reliability of the four level one indicator subscales and the questionnaire as a whole as shown in Table 3. The questionnaire’s overall reliability is 0.891, which indicates that it has good internal consistency.

Table 3.

The coefficient of internal consistency of the questionnaire

	Cronbach’sα	Problem number
Curriculum background	0.842	8
Course investment	0.885	10
Curriculum implementation	0.852	9
Course effect	0.879	9
The α number of the total questionnaire	0.891	36

3.2.2

Questionnaire design and implementation

In this study, a questionnaire was designed according to the established evaluation index system of Business English Civic Education, based on traditional culture. The questionnaire consists of two aspects: the first is the basic information of the survey respondents, including their gender, age, specialty, and so on. The second is the main content of the questionnaire, and the scale is scored by Likert five-degree scale, which is rated from “not conforming at all” to “conforming completely”. The questionnaire was mainly distributed in the form of WeChat, QuestionStar and other online forms.

3.2.3

Data recovery and organization

This questionnaire is mainly for the students of G school, a total of 450 questionnaires were distributed, 380 were recovered, with a recovery rate of 86.4%, by filtering and removing the invalid questionnaires, 314 valid questionnaires were finally obtained, with an effective rate of 69.78%. SPSS26.0 was used to process the questionnaire data as shown in Table 4. The results show that 49.04% of the men and 50.96% of the women participated in this survey, and there is not much difference between the proportions of men and women; the number of first-year students is the largest, accounting for 53.18% of the total; the students whose place of origin is towns and cities account for most of the students, accounting for 61.46% of the total; students of humanities and social sciences predominate, accounting for 55.1% of the total; and 42.99% of the students have had the experience of Civic and Political Education; The number of students who have participated in the Civic and Political Knowledge Contest is 59.24% of the total; most of the students have received Civic and Political Education courses, accounting for 81.85%.

Table 4.

Data recovery and collation

Basic situation	Options	Frequency	Proportion(%)
Gender	Male	154	49.04
Gender	Female	160	50.96
Grade	Freshman year	167	53.18
	Sophomore	123	39.17
	Junior	24	7.64
Majors	Humanities and social sciences	173	55.1
Majors	Science and engineering	141	44.90
Biotically	Countryside	121	38.54
Biotically	Town	193	61.46
Whether there is a reflection on education	Yes	135	42.99
Whether there is a reflection on education	No	179	57.01
Whether to participate in the thinking of political knowledge competition	Yes	186	59.24
	No	128	40.76
Whether to accept the thoughts of the political education course	Yes	257	81.85
	No	57	18.15

3.3

Evaluation Results of Business English Civic Education Based on Traditional Culture

Based on the above results, according to the principle of maximum affiliation, the evaluation level of traditional culture-based business English civic education is “average”, indicating that the traditional culture-based business English civic education in School G is average. At the same time, values were assigned to each evaluation level. In this evaluation, the scores were set at 90 or above as excellent, 80-89 as good, 70-79 as average, 60-69 as poor, and 60 or below as unqualified.

After calculating the evaluation score of Business English Civic and Political Education based on Traditional Culture is shown in Table 5. From the table, it can be found that the comprehensive score of Business English Civic and Political Education based on Traditional Culture is 72.0105, and the score range is between 70-79, which indicates that the overall effect of Business English Civic and Political Education based on Traditional Culture in School G is average, and there is still much room for improvement. The evaluation system consists of 4 primary indicators and 10 secondary indicators, and the analysis of the scores of the indicators can comprehensively show the problems that still exist in the traditional culture-based business English civic education.

Table 5.

Fuzzy comprehensive evaluation score

In general	Level 1 indicators	Score	Level 2 indicators	Score
Business English thinking policy education(72.0105)	Course background	67.4152	Course orientation	70.2451
	Course background	67.4152	Course objectives	73.3764
	Course input	72.1756	Human resources	71.9522
			Course resources	72.5643
			Financial resources	72.4829
	Curriculum implementation	72.0845	Teaching preparation	72.4562
	Curriculum implementation	72.0845	Teaching process	71.7631
	Course effect	72.6731	Learning process	70.2478
			Teaching effect	72.5634
			Course impact	72.7211

3.4

Correlation analysis

3.4.1

Correlation analysis

Correlation is mainly used to study the closeness of the relationship between two or more random variables. If the level of significance is less than 0.01, there is a correlation between two variables. When the Pearson correlation coefficient is greater than 0.8, it means that there is a strong positive linear relationship between the variables; when the correlation coefficient is between 0.6-0.8, it means that there is a positive linear correlation between the variables; when the correlation coefficient is less than 0.3, it means that there is a weak linear relationship between the variables.

In this paper, Pearson’s simple correlation coefficient is used to study the correlation between the dimensions of course orientation, course objectives, human resources, course resources, financial resources, teaching preparation, teaching process, learning process, teaching effect and course impact, and the overall evaluation of the traditional culture-based business English civic education.

The correlation between each dimension and the overall evaluation of Business English Civics Education is shown in Table 6. From the table, it can be seen that the significance level between course orientation, course objectives, human resources, course resources, financial resources, teaching preparation, teaching process, learning process, teaching effect and course impact and the overall evaluation of business English Civic and Political Education based on traditional culture are all 0, which is less than 0.01, and the Pearson correlation coefficients are 0.652, 0.778, 0.672, respectively, 0.834, 0.698, 0.753, 0.865, 0.812, 0.731, 0.792, which are between 0.6 and 0.8, indicating that there is a certain linear correlation between the dimensions and the overall evaluation of traditional culture-based business English civic education. According to the strength of the correlation between each dimension and the overall evaluation of traditional culture-based Business English Civics Education, they are ranked in descending order: teaching process, course resources, learning process, course impact, course objectives, teaching preparation, teaching effectiveness, financial resources, human resources, and course orientation.

Table 6.

Overall evaluation of the correlation

Dimension	Correlation coefficient
Course orientation	Pearson correlation	0.652**
Course orientation	Significance (double tail)	.000
Course objectives	Pearson correlation	0.778**
Course objectives	Significance (double tail)	.000
Human resources	Pearson correlation	0.672**
Human resources	Significance (double tail)	.000
Course resources	Pearson correlation	0.834**
Course resources	Significance (double tail)	.000
Financial resources	Pearson correlation	0.698**
Financial resources	Significance (double tail)	.000
Teaching preparation	Pearson correlation	0.753**
Teaching preparation	Significance (double tail)	.000
Teaching process	Pearson correlation	0.865**
Teaching process	Significance (double tail)	.000
Learning process	Pearson correlation	0.812**
Learning process	Significance (double tail)	.000
Teaching effect	Pearson correlation	0.731**
Teaching effect	Significance (double tail)	.000
Course impact	Pearson correlation	0.792**
Course impact	Significance (double tail)	.000

3.4.2

Regression analysis

The degree of fit of the multiple linear regression equation is tested by the adjusted coefficient of determination (R²), and the test results are shown in Table 7, with the increase of the number of models, the value of the goodness-of-fit indicator R² gradually increases, and the adjusted R² of model 10 is 0.720, indicating that the independent variables of the 10 dimensions explain 72% of the overall evaluation of the traditional culture-based Civic and Political Education in Business English. The standard error of estimation is 0.180, which indicates that the equation has a high degree of fit, the error is small, and the regression equation can be accepted. And the DW value is 1.783, close to 2, indicating that there is no autocorrelation among the 10 independent variables, which can explain the regression equation better.

Table 7.

Model summary^k

Model	R	R²	Adjusted R²	Standard error	significance	Durbin-Wat son(U)
1	.645^a	.442	.441	.221	.000
2	.711^b	.563	.562	.201	.000
3	.784^c	.573	.571	.189	.000
4	.812^d	.614	.612	.183	.000
5	.701^e	.645	.643	.178	.000
6	.831^f	.664	.662	.181	.000
7	.676^g	.671	.669	.181	.000
8	.877^h	.673	.671	.185	.000
9	.731ⁱ	.678	.675	.180	.000
10	.782^j	.724	.720	.180	.000	1.783

Predictor variable: (constant), course orientation

Predictor variable: (constant), course orientation, course objectives

Predictor variable: (constant), course orientation, course objectives, human resources

Predictor variable: (Constant), Curriculum Orientation, Curriculum Objectives, Human Resources, Curriculum Resources

Predictor variable: (Constant), Curriculum Orientation, Curriculum Objectives, Human Resources, Curriculum Resources, Financial Resources

Predictor Variable: (Constant), Curriculum Orientation, Curriculum Objectives, Human Resources, Curriculum Resources, Financial Resources, Instructional Readiness

Predictor variable: (constant), course orientation, course objectives, human resources, curricular resources, financial resources, instructional readiness, instructional process,

Predictor variable: (constant) course orientation, course objectives, human resources, curricular resources, financial resources, instructional readiness, instructional process, learning process

Predictor variable: (Constant) Curriculum orientation, curriculum objectives, human resources, curriculum resources, financial resources, instructional preparation, instructional process, learning process, instructional effectiveness

Predictor variables: (constant) course orientation, course objectives, human resources, course resources, financial resources, instructional preparation, instructional process, learning process, instructional effectiveness, curricular impacts

Dependent variable: business English ideological education based on traditional culture

Table 8 is a table of coefficients showing the constants in the regression relationship between each dimension and the overall evaluation of traditional culture-based Business English Civic Education, the regression coefficients. The P-value of the regression coefficients of course orientation, course objectives, human resources, course resources, financial resources, teaching preparation, teaching process, learning process, teaching effect, and course impact are all less than 0.05, which reaches the level of significance, indicating that they can significantly respond to the evaluation of Business English Civic and Political Education based on Traditional Culture. And the expansion factor (VIF) in each dimension is less than 5, indicating that there is no covariance among the 10 independent variables. There are 10 factors of course orientation, course objectives, human resources, course resources, financial resources, teaching preparation, teaching process, learning process, teaching effect, and course impact with β-values of 0.205, 0.228, 0.020, 0.199, 0.194, 0.140, 0.211, 0.101, 0.089, 0.157, and 0.061 with a constant of 3.132. from which the regression equation between the 10 dimensions and the overall evaluation of traditional culture-based business English civic education can be derived as:

Table 8.

Coefficient a

	Nonnormalized coefficient		Normalization factor	t	significance	Common linear statistics
	B	Standard error	β	t	significance	Admissible	VIF
Constants	3.132	.011		156.131	.000
Course orientation	.081	.023	.205	4.138	.000	.328	2.619
Course objectives	.120	.020	.228	3.889	.000	.263	2.910
Human resources	.096	.028	.199	3.490	.000	.314	3.873
Course resources	.047	.017	.194	4.259	.000	.402	2.662
Financial resources	.076	.023	.140	3.373	.000	.246	3.082
Teaching preparation	.093	.020	.211	3.296	.000	.337	2.490
Teaching process	.086	.022	.101	2.852	.000	.257	3.805
Learning process	.045	.026	.089	6.476	.000	.329	3.048
Teaching effect	.029	.017	.157	2.589	.000	.242	3.184
Course impact	.075	.029	.061	5.344	.000	.384	2.601

Dependent Variable: Business English Civic Education Based on Traditional Culture

Y=0.205×curriculum orientation+0.228×curriculum objectives+0.199×human resources+0.194× curriculum resources+0.140×financial resources+0.211×teaching preparation+0.101×teaching process+0.089×learning process+0.157×teaching effect+0.061×curriculum impact+3.132

The degree of influence of each dimension on the overall evaluation of traditional culture-based business English civic education, in descending order, is as follows: course objectives, teaching preparation, course orientation, human resources, course resources, teaching effectiveness, financial resources, teaching process, learning process, and course impact.

Figure 2 shows a normal probability plot of the standardized residuals, in which all the points are basically evenly distributed above and below the straight line, suggesting a linear distribution among the variables.

3.5

Impact of individual differences on the evaluation system

3.5.1

Gender differences

Gender and degree type are dichotomous variables, so this study tested the difference in the influence of individual factors on the overall evaluation of business English civic education based on traditional culture through an independent samples t-test. Age, major, and grade level were tested by One-Way ANOVA to test the hypothesis of the difference in the influence of individual factors on the overall evaluation of business English civic education based on traditional culture.

The results of the chi-square test (Levene’s test), t-test, and 95% confidence intervals are shown in Table 9. The results of Levene’s test for the dimension of course orientation were: F-value was 11.65, and the probability of significance was P=0.657>0.05.Therefore, there was no significant difference in the variance of course orientation between males and females. The result of Levene’s test for the dimension of course objectives is: F value is 14.45 and the probability of significance is P=0.173>0.05.Therefore, there is no significant difference in the variance of course objectives between males and females.

Table 9.

Independent sample inspection

		F	Significance	t	Freedom	significance
Course orientation	Homogeneity of variance has been assumed	11.654	.657	2.352	334	.057
Course orientation	Homogeneity of the variance was not assumed			2.251	132.613	.061
Course objectives	Homogeneity of variance has been assumed	14.451	.173	3.372	334	.001
Course objectives	Homogeneity of the variance was not assumed			3.674	141.43	.005
Human resources	Homogeneity of variance has been assumed	3.673	.356	1.853	334	.053
Human resources	Homogeneity of the variance was not assumed			1.654	149.217	.047
Course resources	Homogeneity of variance has been assumed	2.782	.000	1.872	334	.001
Course resources	Homogeneity of the variance was not assumed			1.543	152.632	.011
Financial resources	Homogeneity of variance has been assumed	.764	.275	2.542	334	.022
Financial resources	Homogeneity of the variance was not assumed			2.757	156.764	.001
Teaching preparation	Homogeneity of variance has been assumed	2.452	.000	3.537	334	.065
Teaching preparation	Homogeneity of the variance was not assumed			3.784	164.935	.002
Teaching process	Homogeneity of variance has been assumed	10.653	.000	1.685	334	.001
Teaching process	Homogeneity of the variance was not assumed			1.574	177.262	.001
Learning process	Homogeneity of variance has been assumed	8.543	.000	3.545	334	.031
Learning process	Homogeneity of the variance was not assumed			3.672	185.434	.001
Teaching effect	Homogeneity of variance has been assumed	4.21	.000	1.856	334	.021
Teaching effect	Homogeneity of the variance was not assumed			1.426	191.653	.001
Course impact	Homogeneity of variance has been assumed	9.753	.000	2.673	334	.023
Course impact	Homogeneity of the variance was not assumed			2.572	198.542	.003

The result of Levene’s test for teaching effectiveness dimension is: F value is 4.21 and probability of significance P=0.000<0.05.Therefore, there is a significant difference in the variance of teaching effectiveness between males and females. The result of independent samples t-test showed T=1.426 with significance P=0.001<0.05, so there is a significant difference in the mean value of quality gap of teaching effectiveness dimension by gender.

3.5.2

Regional differences

The ANOVA analysis of different regions is shown in Table 10. The table shows that the p-value of each dimension is less than 0.05 for different places of origin on all 10 dimensions, which means that there is a significant difference between students from different places of origin in terms of course orientation, course objectives, human resources, course resources, financial resources, preparation for teaching, teaching and learning process, learning process, teaching and learning effectiveness, and course impact.

Table 10.

ANOVA analysis in different regions

		Sum of squares	df	Mean square	F	Significance
Course orientation	Intergroup	11.452	3	3.213	9.452	.000
	Within group	123.452	351	.343
	Total	134.904	352
Course objectives	Intergroup	12.671	3	3.784	6.432	.000
	Within group	131.734	351	.454
	Total	144.405	352
Human resources	Intergroup	7.573	3	2.367	5.351	.000
	Within group	138.656	351	.411
	Total	146.229	352
Course resources	Intergroup	12.442	3	3.452	4.212	.000
	Within group	143.631	351	.357
	Total	156.073	352
Financial resources	Intergroup	17.422	3	3.651	4.231	.000
	Within group	156.375	351	.355
	Total	173.797	352
Teaching preparation	Intergroup	10.572	3	3.641	6.322	.000
	Within group	167.542	351	.552
	Total	178.114	352
Teaching process	Intergroup	12.754	3	4.431	7.355	.000
	Within group	174.338	351	.414
	Total	187.092	352
Learning process	Intergroup	10.653	3	5.232	5.543	.000
	Within group	182.663	351	.562
	Total	193.316	352
Teaching effect	Intergroup	12.775	3	3.521	6.241	.000
	Within group	198.435	351	.435
	Total	211.21	352
Course impact	Intergroup	10.741	3	4.262	8.532	.000
	Within group	213.56	351	.351
	Total	224.301	352

4

Conclusion

Based on the theory of the CIPP evaluation model, this paper collects relevant research data to determine the evaluation system of Business English Civic and Political Education based on traditional culture. The subjective and objective weights of the indicators were calculated using the G1-CRITIC method, the reliability and validity of the indicators were tested with the help of SPSS26.0, and the traditional culture-based Business English Civic and Political Education in G school was practically applied to obtain objective evaluation results. Finally, through empirical analysis, the correlation and regression relationship between the dimensions of Business English Civic Education based on Traditional Culture and the overall evaluation were analyzed. Based on the evaluation of traditional culture-based Business English and Civics Education in School G, the following conclusions were obtained: 1)

The overall level of traditional culture-based Business English Civic and Political Education in School G is medium to high, which still needs to be strengthened. Secondly, the overall score of Business English Civics Education based on Traditional Culture is 72.0105, with a score range of 70-80, so School G still needs to further strengthen the Business English Civics Education for students.

2)

Through empirical analysis, the correlation and regression relationship between the dimensions of the evaluation of Business English Civic and Political Education based on Traditional Culture and the overall quality evaluation were analyzed. It is verified that different individual factors: gender and region will have different degrees of influence on the evaluation level of Business English Civics Education based on Traditional Culture.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

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Construction and Model Application of Evaluation System of Business English Civic Education Based on Traditional Culture

Menglin Deng

Jin Hu

Ken Chen

Publicado en línea: 21 mar 2025

Recibido: 17 oct 2024

Aceptado: 04 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0693

Palabras claveCIPP evaluation model, G1-CRITIC method, Linear weighting method, Fuzzy comprehensive evaluation, Business English Civic and Political Education

© 2025 Menglin Deng et al., published by Sciendo

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

Palabras clave
CIPP evaluation model, G1-CRITIC method, Linear weighting method, Fuzzy comprehensive evaluation, Business English Civic and Political Education