# The Evaluation Index System of Teaching Quality in Colleges and Universities: Based on the CIPP Model

Authors: Yan Li; Conghai Hu
Journal: Mathematical Problems in Engineering
 (2022)

---

## Abstract

Under the comprehensive influence of the national political and economic environment, the law of higher education development, and the characteristics of teaching itself, the relationship between theoretical teaching and teaching should be properly handled, and in accordance with the goals and ideas of talent training in research universities, on the one hand, the teaching of theoretical courses should be done well to ensure the protection of students. A solid theoretical foundation, on the one hand, emphasizes the application-oriented and sexual teaching links and forms a teaching system that combines professional skills and professional technical application ability, and comprehensive ability. For research-oriented university education, it is an inevitable problem to face. Just as the diagnosis of a disease is required first, in order to promote teaching reform, it is necessary to have a clear understanding of the current situation of the school teaching system and to understand the root of the problem. Therefore, it is very necessary to study the evaluation of teaching, which will provide facts for the reform. Fundamentals, Orientation, and Metrics. Based on the CIPP model of effective teaching theory and formative assessment theory, this research constructs a teaching quality assurance index system in colleges, universities, and takes colleges and universities as a case for empirical research. The survey on the status quo of quality assurance and the experimental results show that the index system of teaching quality evaluation in colleges and universities based on the CIPP model has good applicability.


---

## Body

## 1. Introduction

At present, the main way for colleges and universities to carry out teaching activities is still classroom teaching, and evaluating classroom teaching is an important way to test the quality of education and teaching in a school. Classroom teaching evaluation is produced together with classroom teaching. It plays an important role in standardizing all aspects of classroom teaching and is an important means to improve the quality of classroom teaching. In order to realize the requirements of talent training such as knowledge transfer, ability training, and quality improvement, teaching, as an important part of university teaching, began to be given a new mission, and the development of teaching has been promoted to a new stage. Research-oriented college students’ teaching evaluation research can promote the teaching evaluation research in the teaching field, enrich the content and level of teaching theory research, and provide new ideas and research methods for the application of education evaluation theory in the field of teaching. The theory of construction and reform has laid a factual foundation. This study focuses on building a teaching evaluation index system to evaluate engineering colleges and universities, in order to measure and improve the teaching quality of engineering colleges and universities. At this stage, because the teaching evaluation indicators of colleges and universities in China are too unified and there is no difference in treatment, classified evaluation can better play the guiding role of evaluation, guide colleges and universities at different levels and different disciplines to develop their own characteristics, and guide the peaceful mentality of all kinds of schools. The acceptance of evaluation is of great significance and should be used as an important reference for the formulation of a new round of evaluation indicators. The integrity of the evaluation system is more conducive to improving the level of teaching work [1–3].


## 2. Related Work

In the second half of the twentieth century, various types of educational evaluation organizations were gradually formed in European and American countries, and three representative educational evaluation and education quality assurance models have initially formed: the American model, the European model, and the British model. Among them, in the American model, American universities enjoy a high degree of autonomy and mainly rely on various professional academic institutions and groups in the society to conduct educational evaluations, forming a higher education evaluation system by types, majors, and regions. Currently, there are 6 regional professional accreditation agencies (Central, New England, North Central, Northwest, South, and West) recognized by the U.S. Federal Department of Education. Higher Education), mainly to identify private schools. Continental model, in Germany, France, Italy, and other countries, the government strictly controls the evaluation of higher education, the government directly organizes and participates in the evaluation, and the evaluation results are related to government funding. For example, French universities adopt a contract system in raising funds from the state, which requires universities to formulate an overall plan that matches subjective goals with objective reality and reasonable supporting policies necessary to achieve this plan. When the contract is about to expire, The National Evaluation Committee evaluates the implementation of university contract and the achievement of its goals and negotiates a new contract. The evaluation report of the National Evaluation Committee will directly affect the new contract signed by the government and the school and affect the government’s funding for the school. In the British model, British universities have a great degree of freedom due to the continuation of history. The government mainly indirectly influences the university family through the Higher Education Fund Committee. The evaluation agency is relatively neutral, but the evaluation results can be used as a reference for the committee’s funding. In the UK, the Quality Assurance Agency in high education serves the Higher Education Foundation according to the contract, and evaluates universities every 6 years; the Research Assessment Exercise is responsible for evaluating the scientific research of universities [4–10].


## 3. Construction of the Evaluation Index System of Teaching Quality in Colleges and Universities

### 3.1. Basis for the Establishment of Teaching Evaluation Indicators

#### 3.1.1. Basis for the Establishment of Primary Indicators

This research chooses the CIPP model as the evaluation model, including four kinds of evaluations: teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation. Teaching background evaluation is the evaluation of needs, problems, advantages, and opportunities in a specific situation; input evaluation is the evaluation of planning programs, programs, or service strategies and their related work plans and implementation budgets; process evaluation is the evaluation of program implementation, process documents, change the major mistakes or bad operations of certain procedures of the plan, and conduct continuous inspection and evaluation; the outcome evaluation is to evaluate the complete performance of the implementation results of the project and the degree to which the needs of all beneficiaries are met. Therefore, the four first-level indicators are teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation [11–13].


#### 3.1.2. Basis for the Establishment of Secondary Indicators

This paper deeply studies the theory of higher education evaluation, teaching theory, and teaching evaluation theory at home and abroad, clarifies the connotation, significance, and influencing factors of the first-level indicators above, and selects five second-level indicators for each first-level indicator in a comprehensive and systematic way. The secondary indicators of teaching background evaluation can refer to the main observation points of the “Guiding Ideas for Running Schools” in the teaching level evaluation indicators: positioning and planning, educational ideology, and teaching centrality. Combined with the teaching practice, five secondary indicators are listed: teaching concept, teaching purpose, teaching plan, educational regulations, and social needs. The selection of secondary indicators for the evaluation of teaching investment can be obtained by referring to the theory of production factors in economics. There are four types of production factors in economics, productivity, land, capital, and entrepreneurial talent. Later, information technology gradually became an independent production factor. Elements participate in social production activities. Taking into account the difference between the teaching activities of colleges and universities and the general production activities, five secondary indicators are listed: teachers, bases, funding, laboratories, and teaching materials. The selection of secondary indicators of teaching process evaluation can consider the theory of teaching system elements. At present, there are many theories about the constituent elements of the teaching system in the academic circle. Among them, Mr. Li Bingde explained the seven elements in the article “Review and Prospect of Teaching Theory” in 1989, that teaching activities include seven elements: teacher, student, purpose, content, method, environment, and feedback. Taking this theory into consideration, five secondary indicators are listed: teaching content, teaching methods, curriculum setting, teaching supervision, and teaching assessment. The secondary indicators of teaching achievement evaluation can be derived from the subsequent development of the CIPP theory. After establishing this model, Stafford Beam reconsidered the evaluation and believed that the four-step evaluation model was not enough to describe and evaluate long-term, real success reform plan. To this end, he supplemented and improved it, and decomposed the outcome evaluation into four parts: impact, effectiveness, sustainability, and transportability [14–18]. The final indicator system is shown in Table 1 below.Table 1
Selection of secondary indicators of teaching.
First-level indicatorSecondary indicatorsTeaching background evaluationTeaching concept, teaching purpose, teaching planTeaching investment evaluationFaculty, base and equipment stock, and fundingTeaching process evaluationTeaching content, teaching methods, teaching supervision, and assessmentTeaching achievement evaluationStudent ability, reform and innovation, and social recognition


### 3.2. Determination of Teaching Evaluation Index System

The index system of teaching evaluation in research-oriented universities is based on CIPP, as is shown in Table2.Table 2
Index system of teaching evaluation in research-oriented universities based on CIPP model.
First-level indicatorSecondary indicatorsEvaluation descriptionTeaching backgroundB1Teaching conceptC1Teaching concept science, advanced levelTeaching purposeC2The purpose of teaching is clear and in-depthTeaching planC3The teaching plan is clear and reasonableTeaching inputB2FacultyC4The strength of teaching facultyBase and equipment inventoryC5Abundance of base equipment stockFunding inputC6The adequacy of teaching fundingTeaching processB3Teaching contentC7Rich and practical teaching contentTeaching methodsC8Teaching method science, flexibilityTeaching supervision and assessmentC9Teaching supervision and careful assessment of the seriousnessTeaching outcomesB4Student abilityC10Students with strong ability and comprehensive qualityReform and innovationC11Faster reform and innovation of teachingSocial recognitionC12High degree of social recognition of students and institutions


### 3.3. Determination of Evaluation Index System

This study adopts the Delphi method, consulted five professors and experts engaged in higher education teaching and university management, analyzed the returned three-point consultation questionnaire to obtain a scientific and reasonable indicator weight judgment result, and then use the analytic hierarchy process to calculate. For the weights of indicators at all levels, the final weight results are as follows: according to the above calculation results, three decimal places are reserved, and the weight distribution of indicators is as follows: [19, 20].(1)Aw=0.095,0.183,0.355,0.367,B1w=0.122,0.230,0.648,B2w=0.539,0.164,0.297,B3w=0.230,0.648,0.122,B4w=0.539,0.164,0.297.The established teaching evaluation framework is shown in Figure1 below:Figure 1
The teaching evaluation framework of research-oriented universities based on the CIPP model.


## 3.1. Basis for the Establishment of Teaching Evaluation Indicators

### 3.1.1. Basis for the Establishment of Primary Indicators

This research chooses the CIPP model as the evaluation model, including four kinds of evaluations: teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation. Teaching background evaluation is the evaluation of needs, problems, advantages, and opportunities in a specific situation; input evaluation is the evaluation of planning programs, programs, or service strategies and their related work plans and implementation budgets; process evaluation is the evaluation of program implementation, process documents, change the major mistakes or bad operations of certain procedures of the plan, and conduct continuous inspection and evaluation; the outcome evaluation is to evaluate the complete performance of the implementation results of the project and the degree to which the needs of all beneficiaries are met. Therefore, the four first-level indicators are teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation [11–13].


### 3.1.2. Basis for the Establishment of Secondary Indicators

This paper deeply studies the theory of higher education evaluation, teaching theory, and teaching evaluation theory at home and abroad, clarifies the connotation, significance, and influencing factors of the first-level indicators above, and selects five second-level indicators for each first-level indicator in a comprehensive and systematic way. The secondary indicators of teaching background evaluation can refer to the main observation points of the “Guiding Ideas for Running Schools” in the teaching level evaluation indicators: positioning and planning, educational ideology, and teaching centrality. Combined with the teaching practice, five secondary indicators are listed: teaching concept, teaching purpose, teaching plan, educational regulations, and social needs. The selection of secondary indicators for the evaluation of teaching investment can be obtained by referring to the theory of production factors in economics. There are four types of production factors in economics, productivity, land, capital, and entrepreneurial talent. Later, information technology gradually became an independent production factor. Elements participate in social production activities. Taking into account the difference between the teaching activities of colleges and universities and the general production activities, five secondary indicators are listed: teachers, bases, funding, laboratories, and teaching materials. The selection of secondary indicators of teaching process evaluation can consider the theory of teaching system elements. At present, there are many theories about the constituent elements of the teaching system in the academic circle. Among them, Mr. Li Bingde explained the seven elements in the article “Review and Prospect of Teaching Theory” in 1989, that teaching activities include seven elements: teacher, student, purpose, content, method, environment, and feedback. Taking this theory into consideration, five secondary indicators are listed: teaching content, teaching methods, curriculum setting, teaching supervision, and teaching assessment. The secondary indicators of teaching achievement evaluation can be derived from the subsequent development of the CIPP theory. After establishing this model, Stafford Beam reconsidered the evaluation and believed that the four-step evaluation model was not enough to describe and evaluate long-term, real success reform plan. To this end, he supplemented and improved it, and decomposed the outcome evaluation into four parts: impact, effectiveness, sustainability, and transportability [14–18]. The final indicator system is shown in Table 1 below.Table 1
Selection of secondary indicators of teaching.
First-level indicatorSecondary indicatorsTeaching background evaluationTeaching concept, teaching purpose, teaching planTeaching investment evaluationFaculty, base and equipment stock, and fundingTeaching process evaluationTeaching content, teaching methods, teaching supervision, and assessmentTeaching achievement evaluationStudent ability, reform and innovation, and social recognition


## 3.1.1. Basis for the Establishment of Primary Indicators

This research chooses the CIPP model as the evaluation model, including four kinds of evaluations: teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation. Teaching background evaluation is the evaluation of needs, problems, advantages, and opportunities in a specific situation; input evaluation is the evaluation of planning programs, programs, or service strategies and their related work plans and implementation budgets; process evaluation is the evaluation of program implementation, process documents, change the major mistakes or bad operations of certain procedures of the plan, and conduct continuous inspection and evaluation; the outcome evaluation is to evaluate the complete performance of the implementation results of the project and the degree to which the needs of all beneficiaries are met. Therefore, the four first-level indicators are teaching background evaluation, teaching input evaluation, teaching process evaluation, and teaching achievement evaluation [11–13].


## 3.1.2. Basis for the Establishment of Secondary Indicators

This paper deeply studies the theory of higher education evaluation, teaching theory, and teaching evaluation theory at home and abroad, clarifies the connotation, significance, and influencing factors of the first-level indicators above, and selects five second-level indicators for each first-level indicator in a comprehensive and systematic way. The secondary indicators of teaching background evaluation can refer to the main observation points of the “Guiding Ideas for Running Schools” in the teaching level evaluation indicators: positioning and planning, educational ideology, and teaching centrality. Combined with the teaching practice, five secondary indicators are listed: teaching concept, teaching purpose, teaching plan, educational regulations, and social needs. The selection of secondary indicators for the evaluation of teaching investment can be obtained by referring to the theory of production factors in economics. There are four types of production factors in economics, productivity, land, capital, and entrepreneurial talent. Later, information technology gradually became an independent production factor. Elements participate in social production activities. Taking into account the difference between the teaching activities of colleges and universities and the general production activities, five secondary indicators are listed: teachers, bases, funding, laboratories, and teaching materials. The selection of secondary indicators of teaching process evaluation can consider the theory of teaching system elements. At present, there are many theories about the constituent elements of the teaching system in the academic circle. Among them, Mr. Li Bingde explained the seven elements in the article “Review and Prospect of Teaching Theory” in 1989, that teaching activities include seven elements: teacher, student, purpose, content, method, environment, and feedback. Taking this theory into consideration, five secondary indicators are listed: teaching content, teaching methods, curriculum setting, teaching supervision, and teaching assessment. The secondary indicators of teaching achievement evaluation can be derived from the subsequent development of the CIPP theory. After establishing this model, Stafford Beam reconsidered the evaluation and believed that the four-step evaluation model was not enough to describe and evaluate long-term, real success reform plan. To this end, he supplemented and improved it, and decomposed the outcome evaluation into four parts: impact, effectiveness, sustainability, and transportability [14–18]. The final indicator system is shown in Table 1 below.Table 1
Selection of secondary indicators of teaching.
First-level indicatorSecondary indicatorsTeaching background evaluationTeaching concept, teaching purpose, teaching planTeaching investment evaluationFaculty, base and equipment stock, and fundingTeaching process evaluationTeaching content, teaching methods, teaching supervision, and assessmentTeaching achievement evaluationStudent ability, reform and innovation, and social recognition


## 3.2. Determination of Teaching Evaluation Index System

The index system of teaching evaluation in research-oriented universities is based on CIPP, as is shown in Table2.Table 2
Index system of teaching evaluation in research-oriented universities based on CIPP model.
First-level indicatorSecondary indicatorsEvaluation descriptionTeaching backgroundB1Teaching conceptC1Teaching concept science, advanced levelTeaching purposeC2The purpose of teaching is clear and in-depthTeaching planC3The teaching plan is clear and reasonableTeaching inputB2FacultyC4The strength of teaching facultyBase and equipment inventoryC5Abundance of base equipment stockFunding inputC6The adequacy of teaching fundingTeaching processB3Teaching contentC7Rich and practical teaching contentTeaching methodsC8Teaching method science, flexibilityTeaching supervision and assessmentC9Teaching supervision and careful assessment of the seriousnessTeaching outcomesB4Student abilityC10Students with strong ability and comprehensive qualityReform and innovationC11Faster reform and innovation of teachingSocial recognitionC12High degree of social recognition of students and institutions


## 3.3. Determination of Evaluation Index System

This study adopts the Delphi method, consulted five professors and experts engaged in higher education teaching and university management, analyzed the returned three-point consultation questionnaire to obtain a scientific and reasonable indicator weight judgment result, and then use the analytic hierarchy process to calculate. For the weights of indicators at all levels, the final weight results are as follows: according to the above calculation results, three decimal places are reserved, and the weight distribution of indicators is as follows: [19, 20].(1)Aw=0.095,0.183,0.355,0.367,B1w=0.122,0.230,0.648,B2w=0.539,0.164,0.297,B3w=0.230,0.648,0.122,B4w=0.539,0.164,0.297.The established teaching evaluation framework is shown in Figure1 below:Figure 1
The teaching evaluation framework of research-oriented universities based on the CIPP model.


## 4. Application of Teaching Quality Evaluation in Colleges and Universities Based on CIPP Model

### 4.1. Data Collection and Organization

All the scores of the questionnaire were entered and counted with Excel software, and all the questionnaires with the same score for a single index were excluded, and the total score of each questionnaire was calculated based on the weights of the indicators at all levels, and the comprehensive evaluation result was obtained. The calculation method is:(2)VB1=0.112∗C1+0.230∗C2+0.648∗C3,VB2=0.539∗C4+0.164∗C5+0.297∗C6,VB3=0.230∗C7+0.648∗C8+0.122∗C9,VB4=0.539∗C10+0.164∗C11+0.297∗C12,VA=0.095∗VB1+0.183∗VB2+0.355∗VB3+0.367∗VB4.


### 4.2. University Teaching Evaluation Data Analysis

In order to judge the influence relationship between teaching background, teaching input, teaching process and teaching results, and analyze the relationship between teaching background, teaching input and teaching process, on the basis of the above basic statistics and comparative analysis, regression analysis was performed on engineering, liberal arts, and total data, and the process was as follows:


#### 4.2.1. Correlation Analysis of the First-Level Indicators of Science Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the statistical results of science data columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables3–5, the binary linear regression equation of science teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.953 + 0.379 × 1 + 0.309X2 + 0.174X3. b1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.379 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase 0.309 points; b3 means that when the teaching background and teaching input scores are determined, each additional point in the teaching process will increase the teaching results by 0.379 points. The correlation coefficient of regression statistics is 0.768539, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.768539, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.768539. The coefficient of determination is equal to 59.06% for the goodness of fit, indicating that 59.06% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. Generally speaking, a goodness of fit greater than or equal to 0.6 is considered acceptable, and greater than 0.8 is considered excellent. In this analysis, the goodness of fit is approximately 0.6, which is acceptable. Given the significance level a = 0.05, F = 11.062 can be obtained by looking up the table, which is greater than F0.05 (2, 25) = 3.39, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(27-3) = 2.064, the test statistic of b1 t1 = 2.219523 > 2.064, reject the hypothesis of H0:β1 = 0; the test statistic of b2 quantity t2 = 1.464698 < 2.064, the hypothesis of H0:β2 = 0 is not rejected; the test statistic of b3 t3 = 0.89623 < 2.064, the hypothesis of H0:β3 = 0 is not rejected. Therefore, only the regression coefficient b1 is statistically significant, and in the correlation analysis of the science data, only the teaching background has a significant impact on the teaching results [21].Table 3
Correlation analysis table of first-level indicators of science teaching (1).
Regression statisticsMultiple R0.768539R square0.590652A adjusted R square0.537258Standard error1.103188Observations27Table 4
Correlation analysis table of first-level indicators of science teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis340.3892213.4630711.062290.000108_Residual2327.991571.217025_Total2668.38079Table 5
Correlation analysis table of first-level indicators of science teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.953081.215840.783880.4411−1.562083.46824−1.562083.46824X variable 1_0.170810.170812.219520.03650.025770.732490.025770.73249X variable 2_0.309390.211231.464690.1565−0.127580.74637−0.127580.74637X variable 3_0.174650.194870.896230.3794−0.228480.57779−0.228480.57779


#### 4.2.2. Correlation Analysis of the First-Level Indicators of Engineering Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching investment, and teaching process as the independent variables, input the engineering data statistical results B1, B2, B3, and B4 into the Excel data analysis interface, select regression analysis and output the following results.According to the results shown in Tables6–8, the binary linear regression equation of engineering teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = −1.280 + 0.364 × 1 + 0.390 × 2 + 0.408 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.364 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.390 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.408 points. The correlation coefficient of regression statistics is 0.8903, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8903, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8903. The coefficient of determination is equal to 79.27% for the goodness of fit, indicating that 79.27% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is approximately 0.8, which is an excellent level. Given the significance level a = 0.05, F = 40.798 can be obtained by looking up the table, which is greater than F0.05 (2, 33) = 3.28, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(36-3) = 2.035, the test statistic of b1 t1 = 2.390 > 2.035, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 2.553 > 2.035, reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 2.778 > 2.035, reject the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1, b2, and b3 are statistically significant. In the correlation analysis of engineering data, the effects of teaching background, teaching investment, and teaching process on teaching results are all significant.Table 6
Correlation analysis table of first-level indicators of engineering teaching (1).
Regression statisticsMultiple R0.890359R square0.792739A adjusted R square0.773308Standard error0.848307Observations36Table 7
Correlation analysis table of first-level indicators of engineering teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis388.0782929.3594340.798274.8E−11Residual3223.027980.719624Total35111.1063Table 8
Correlation analysis table of first-level indicators of engineering teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept−1.280140.76962−1.663340.10601−2.84780.28752−2.84780.28752X variable 1_0.3643030.152372.390910.022860.0539350.674670.0539350.67467X variable 2_0.390970.153092.5537860.015620.0791270.702810.0791270.70281X variable 3_0.4088390.147162.7781960.009060.1090840.708590.1090840.70859


#### 4.2.3. Correlation Analysis of the First-Level Indicators of Liberal Arts Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the liberal arts data statistical results columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables9–11 above, the binary linear regression equation of liberal arts teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 2.019 + 0.145 × 1 + 0.153 × 2 + 0.490 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.145 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.153 points; b3 means that when the teaching background and teaching investment scores are determined, each additional point in the teaching process will increase the teaching results by 0.490 points. The correlation coefficient of regression statistics is 0.8031, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8031, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8031. The coefficient of determination is equal to 64.49% and the goodness of fit is equal to 64.49%, indicating that 64.49% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.645, which is acceptable. Given the significance level a = 0.05, F = 22.405 can be obtained by looking up the table, which is greater than F0.05(2, 38) = 3.24, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(41-3) = 2.024, the test statistic of b1 t1 = 1.291 < 2.024, the hypothesis of H0:β1 = 0 is not rejected; the test of b2 The statistic t2 = 1.438 < 2.024, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 3.259 > 2.024, rejects the hypothesis of H0:β3 = 0. Therefore, only the regression coefficient b3 is statistically significant, that is, in the correlation analysis of liberal arts data, only the impact of the teaching process on the teaching results is significant.Table 9
Correlation analysis table of first-level indicators of liberal arts teaching (1).
Regression statisticsMultiple R0.833103R square0.644974A adjusted R square0.616188Standard error0.566894Observations41Table 10
Correlation analysis table of first-level indicators of liberal arts teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis321.601657.20055122.405881.93E − 08Residual3711.890650.321369Total4033.4923Table 11
Correlation analysis table of first-level indicators of liberal arts teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept2.019190.655653.079680.003890.690723.347670.6907253.34767X variable 1 _0.145660.112741.291930.20439−0.082790.37411−0.082790.37411X variable 2 _0.153280.106561.438480.15870−0.062630.36920−0.062630.36920X variable 3 _0.490040.150353.259340.002390.185400.794680.1854040.79468


#### 4.2.4. Correlation Analysis of First-Level Indicators of Teaching in Research Universities

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the columns B1, B2, B3, and B4 of all the statistical results of the data into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables12–14 above, the binary linear regression equation of the teaching achievements of research universities can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.650 + 0.399 × 1 + 0.138 × 2 + 0.389 × 3. B1 means that when teaching investment and teaching process scores are determined, for every one-point increase in teaching background, teaching results increase by 0.399 points; b2 means when teaching background and teaching process scores are determined, for every one-point increase in teaching investment, teaching results increase by 0.138 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.389 points. The correlation coefficient of regression statistics is 0.806, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.806, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.806. The coefficient of determination is equal to 65.0% for the goodness of fit, indicating that 65.0% of the changes in the evaluation of teaching results can be explained by changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.65, which is acceptable. Given the significance level a = 0.05, F = 61.9212 can be obtained by looking up the table, which is greater than F0.05(2, 102) = 3.09, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(105-3) = 1.984, the test statistic of b1 t1 = 4.740 > 1.984, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 1.722 < 1.984, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 4.033 > 1.984, rejects the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1 and b3 are statistically significant, that is, in the correlation analysis of research university data, the influence of teaching background and teaching process on teaching results is significant. The data statistics and related analysis results of this chapter are summarized in the following Table 15:Table 12
Correlation analysis table of first-level indicators of teaching in research universities (1).
Regression statisticsMultiple R0.806263R square0.65006A adjusted R square0.639562Standard error0.884838Observations104Table 13
Correlation analysis table of first-level indicators of teaching in research universities (2).
Variance analysisDfs sM SFSignificanceFRegression analysis3145.441548.4804861.92121.03E − 22Residual10078.293840.782938Total103223.7353Table 14
Correlation analysis table of first-level indicators of teaching in research universities (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.650020.465021.397830.16525−0.27251.57262−0.27251.57262X variable 1 _0.399620.084304.740047.09E0.232360.566890.232360.56689X variable 2 _0.138760.080541.722770.08802−0.02100.29857−0.02100.29857X variable 3 _0.389090.096454.033870.0001 00.197720.580460.197720.58046Table 15
Research-oriented teaching evaluation data analysis summary.
Teaching backgroundTeaching inputTeaching processTeaching achievementsTeaching evaluationScience7.293 (significant impact on teaching results)7.3886.8097.1937.102Engineering6.663 (significant impact on teaching results)6.841 (significant impact on teaching outcomes)6.252 (significant impact on teaching results)6.3786.445Liberal arts6.9075.7836.037 (significant impact on teaching results)6.8706.379Research university6.922 (significant impact on teaching results)6.5666.312 (significant impact on teaching outcomes)6.7846.590Remarks: Correlation analysis (regression) goodness of fit is greater than 0.6


## 4.1. Data Collection and Organization

All the scores of the questionnaire were entered and counted with Excel software, and all the questionnaires with the same score for a single index were excluded, and the total score of each questionnaire was calculated based on the weights of the indicators at all levels, and the comprehensive evaluation result was obtained. The calculation method is:(2)VB1=0.112∗C1+0.230∗C2+0.648∗C3,VB2=0.539∗C4+0.164∗C5+0.297∗C6,VB3=0.230∗C7+0.648∗C8+0.122∗C9,VB4=0.539∗C10+0.164∗C11+0.297∗C12,VA=0.095∗VB1+0.183∗VB2+0.355∗VB3+0.367∗VB4.


## 4.2. University Teaching Evaluation Data Analysis

In order to judge the influence relationship between teaching background, teaching input, teaching process and teaching results, and analyze the relationship between teaching background, teaching input and teaching process, on the basis of the above basic statistics and comparative analysis, regression analysis was performed on engineering, liberal arts, and total data, and the process was as follows:


### 4.2.1. Correlation Analysis of the First-Level Indicators of Science Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the statistical results of science data columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables3–5, the binary linear regression equation of science teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.953 + 0.379 × 1 + 0.309X2 + 0.174X3. b1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.379 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase 0.309 points; b3 means that when the teaching background and teaching input scores are determined, each additional point in the teaching process will increase the teaching results by 0.379 points. The correlation coefficient of regression statistics is 0.768539, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.768539, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.768539. The coefficient of determination is equal to 59.06% for the goodness of fit, indicating that 59.06% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. Generally speaking, a goodness of fit greater than or equal to 0.6 is considered acceptable, and greater than 0.8 is considered excellent. In this analysis, the goodness of fit is approximately 0.6, which is acceptable. Given the significance level a = 0.05, F = 11.062 can be obtained by looking up the table, which is greater than F0.05 (2, 25) = 3.39, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(27-3) = 2.064, the test statistic of b1 t1 = 2.219523 > 2.064, reject the hypothesis of H0:β1 = 0; the test statistic of b2 quantity t2 = 1.464698 < 2.064, the hypothesis of H0:β2 = 0 is not rejected; the test statistic of b3 t3 = 0.89623 < 2.064, the hypothesis of H0:β3 = 0 is not rejected. Therefore, only the regression coefficient b1 is statistically significant, and in the correlation analysis of the science data, only the teaching background has a significant impact on the teaching results [21].Table 3
Correlation analysis table of first-level indicators of science teaching (1).
Regression statisticsMultiple R0.768539R square0.590652A adjusted R square0.537258Standard error1.103188Observations27Table 4
Correlation analysis table of first-level indicators of science teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis340.3892213.4630711.062290.000108_Residual2327.991571.217025_Total2668.38079Table 5
Correlation analysis table of first-level indicators of science teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.953081.215840.783880.4411−1.562083.46824−1.562083.46824X variable 1_0.170810.170812.219520.03650.025770.732490.025770.73249X variable 2_0.309390.211231.464690.1565−0.127580.74637−0.127580.74637X variable 3_0.174650.194870.896230.3794−0.228480.57779−0.228480.57779


### 4.2.2. Correlation Analysis of the First-Level Indicators of Engineering Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching investment, and teaching process as the independent variables, input the engineering data statistical results B1, B2, B3, and B4 into the Excel data analysis interface, select regression analysis and output the following results.According to the results shown in Tables6–8, the binary linear regression equation of engineering teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = −1.280 + 0.364 × 1 + 0.390 × 2 + 0.408 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.364 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.390 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.408 points. The correlation coefficient of regression statistics is 0.8903, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8903, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8903. The coefficient of determination is equal to 79.27% for the goodness of fit, indicating that 79.27% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is approximately 0.8, which is an excellent level. Given the significance level a = 0.05, F = 40.798 can be obtained by looking up the table, which is greater than F0.05 (2, 33) = 3.28, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(36-3) = 2.035, the test statistic of b1 t1 = 2.390 > 2.035, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 2.553 > 2.035, reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 2.778 > 2.035, reject the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1, b2, and b3 are statistically significant. In the correlation analysis of engineering data, the effects of teaching background, teaching investment, and teaching process on teaching results are all significant.Table 6
Correlation analysis table of first-level indicators of engineering teaching (1).
Regression statisticsMultiple R0.890359R square0.792739A adjusted R square0.773308Standard error0.848307Observations36Table 7
Correlation analysis table of first-level indicators of engineering teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis388.0782929.3594340.798274.8E−11Residual3223.027980.719624Total35111.1063Table 8
Correlation analysis table of first-level indicators of engineering teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept−1.280140.76962−1.663340.10601−2.84780.28752−2.84780.28752X variable 1_0.3643030.152372.390910.022860.0539350.674670.0539350.67467X variable 2_0.390970.153092.5537860.015620.0791270.702810.0791270.70281X variable 3_0.4088390.147162.7781960.009060.1090840.708590.1090840.70859


### 4.2.3. Correlation Analysis of the First-Level Indicators of Liberal Arts Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the liberal arts data statistical results columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables9–11 above, the binary linear regression equation of liberal arts teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 2.019 + 0.145 × 1 + 0.153 × 2 + 0.490 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.145 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.153 points; b3 means that when the teaching background and teaching investment scores are determined, each additional point in the teaching process will increase the teaching results by 0.490 points. The correlation coefficient of regression statistics is 0.8031, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8031, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8031. The coefficient of determination is equal to 64.49% and the goodness of fit is equal to 64.49%, indicating that 64.49% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.645, which is acceptable. Given the significance level a = 0.05, F = 22.405 can be obtained by looking up the table, which is greater than F0.05(2, 38) = 3.24, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(41-3) = 2.024, the test statistic of b1 t1 = 1.291 < 2.024, the hypothesis of H0:β1 = 0 is not rejected; the test of b2 The statistic t2 = 1.438 < 2.024, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 3.259 > 2.024, rejects the hypothesis of H0:β3 = 0. Therefore, only the regression coefficient b3 is statistically significant, that is, in the correlation analysis of liberal arts data, only the impact of the teaching process on the teaching results is significant.Table 9
Correlation analysis table of first-level indicators of liberal arts teaching (1).
Regression statisticsMultiple R0.833103R square0.644974A adjusted R square0.616188Standard error0.566894Observations41Table 10
Correlation analysis table of first-level indicators of liberal arts teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis321.601657.20055122.405881.93E − 08Residual3711.890650.321369Total4033.4923Table 11
Correlation analysis table of first-level indicators of liberal arts teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept2.019190.655653.079680.003890.690723.347670.6907253.34767X variable 1 _0.145660.112741.291930.20439−0.082790.37411−0.082790.37411X variable 2 _0.153280.106561.438480.15870−0.062630.36920−0.062630.36920X variable 3 _0.490040.150353.259340.002390.185400.794680.1854040.79468


### 4.2.4. Correlation Analysis of First-Level Indicators of Teaching in Research Universities

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the columns B1, B2, B3, and B4 of all the statistical results of the data into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables12–14 above, the binary linear regression equation of the teaching achievements of research universities can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.650 + 0.399 × 1 + 0.138 × 2 + 0.389 × 3. B1 means that when teaching investment and teaching process scores are determined, for every one-point increase in teaching background, teaching results increase by 0.399 points; b2 means when teaching background and teaching process scores are determined, for every one-point increase in teaching investment, teaching results increase by 0.138 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.389 points. The correlation coefficient of regression statistics is 0.806, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.806, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.806. The coefficient of determination is equal to 65.0% for the goodness of fit, indicating that 65.0% of the changes in the evaluation of teaching results can be explained by changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.65, which is acceptable. Given the significance level a = 0.05, F = 61.9212 can be obtained by looking up the table, which is greater than F0.05(2, 102) = 3.09, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(105-3) = 1.984, the test statistic of b1 t1 = 4.740 > 1.984, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 1.722 < 1.984, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 4.033 > 1.984, rejects the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1 and b3 are statistically significant, that is, in the correlation analysis of research university data, the influence of teaching background and teaching process on teaching results is significant. The data statistics and related analysis results of this chapter are summarized in the following Table 15:Table 12
Correlation analysis table of first-level indicators of teaching in research universities (1).
Regression statisticsMultiple R0.806263R square0.65006A adjusted R square0.639562Standard error0.884838Observations104Table 13
Correlation analysis table of first-level indicators of teaching in research universities (2).
Variance analysisDfs sM SFSignificanceFRegression analysis3145.441548.4804861.92121.03E − 22Residual10078.293840.782938Total103223.7353Table 14
Correlation analysis table of first-level indicators of teaching in research universities (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.650020.465021.397830.16525−0.27251.57262−0.27251.57262X variable 1 _0.399620.084304.740047.09E0.232360.566890.232360.56689X variable 2 _0.138760.080541.722770.08802−0.02100.29857−0.02100.29857X variable 3 _0.389090.096454.033870.0001 00.197720.580460.197720.58046Table 15
Research-oriented teaching evaluation data analysis summary.
Teaching backgroundTeaching inputTeaching processTeaching achievementsTeaching evaluationScience7.293 (significant impact on teaching results)7.3886.8097.1937.102Engineering6.663 (significant impact on teaching results)6.841 (significant impact on teaching outcomes)6.252 (significant impact on teaching results)6.3786.445Liberal arts6.9075.7836.037 (significant impact on teaching results)6.8706.379Research university6.922 (significant impact on teaching results)6.5666.312 (significant impact on teaching outcomes)6.7846.590Remarks: Correlation analysis (regression) goodness of fit is greater than 0.6


## 4.2.1. Correlation Analysis of the First-Level Indicators of Science Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the statistical results of science data columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables3–5, the binary linear regression equation of science teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.953 + 0.379 × 1 + 0.309X2 + 0.174X3. b1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.379 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase 0.309 points; b3 means that when the teaching background and teaching input scores are determined, each additional point in the teaching process will increase the teaching results by 0.379 points. The correlation coefficient of regression statistics is 0.768539, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.768539, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.768539. The coefficient of determination is equal to 59.06% for the goodness of fit, indicating that 59.06% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. Generally speaking, a goodness of fit greater than or equal to 0.6 is considered acceptable, and greater than 0.8 is considered excellent. In this analysis, the goodness of fit is approximately 0.6, which is acceptable. Given the significance level a = 0.05, F = 11.062 can be obtained by looking up the table, which is greater than F0.05 (2, 25) = 3.39, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(27-3) = 2.064, the test statistic of b1 t1 = 2.219523 > 2.064, reject the hypothesis of H0:β1 = 0; the test statistic of b2 quantity t2 = 1.464698 < 2.064, the hypothesis of H0:β2 = 0 is not rejected; the test statistic of b3 t3 = 0.89623 < 2.064, the hypothesis of H0:β3 = 0 is not rejected. Therefore, only the regression coefficient b1 is statistically significant, and in the correlation analysis of the science data, only the teaching background has a significant impact on the teaching results [21].Table 3
Correlation analysis table of first-level indicators of science teaching (1).
Regression statisticsMultiple R0.768539R square0.590652A adjusted R square0.537258Standard error1.103188Observations27Table 4
Correlation analysis table of first-level indicators of science teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis340.3892213.4630711.062290.000108_Residual2327.991571.217025_Total2668.38079Table 5
Correlation analysis table of first-level indicators of science teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.953081.215840.783880.4411−1.562083.46824−1.562083.46824X variable 1_0.170810.170812.219520.03650.025770.732490.025770.73249X variable 2_0.309390.211231.464690.1565−0.127580.74637−0.127580.74637X variable 3_0.174650.194870.896230.3794−0.228480.57779−0.228480.57779


## 4.2.2. Correlation Analysis of the First-Level Indicators of Engineering Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching investment, and teaching process as the independent variables, input the engineering data statistical results B1, B2, B3, and B4 into the Excel data analysis interface, select regression analysis and output the following results.According to the results shown in Tables6–8, the binary linear regression equation of engineering teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = −1.280 + 0.364 × 1 + 0.390 × 2 + 0.408 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.364 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.390 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.408 points. The correlation coefficient of regression statistics is 0.8903, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8903, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8903. The coefficient of determination is equal to 79.27% for the goodness of fit, indicating that 79.27% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching input, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is approximately 0.8, which is an excellent level. Given the significance level a = 0.05, F = 40.798 can be obtained by looking up the table, which is greater than F0.05 (2, 33) = 3.28, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(36-3) = 2.035, the test statistic of b1 t1 = 2.390 > 2.035, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 2.553 > 2.035, reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 2.778 > 2.035, reject the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1, b2, and b3 are statistically significant. In the correlation analysis of engineering data, the effects of teaching background, teaching investment, and teaching process on teaching results are all significant.Table 6
Correlation analysis table of first-level indicators of engineering teaching (1).
Regression statisticsMultiple R0.890359R square0.792739A adjusted R square0.773308Standard error0.848307Observations36Table 7
Correlation analysis table of first-level indicators of engineering teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis388.0782929.3594340.798274.8E−11Residual3223.027980.719624Total35111.1063Table 8
Correlation analysis table of first-level indicators of engineering teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept−1.280140.76962−1.663340.10601−2.84780.28752−2.84780.28752X variable 1_0.3643030.152372.390910.022860.0539350.674670.0539350.67467X variable 2_0.390970.153092.5537860.015620.0791270.702810.0791270.70281X variable 3_0.4088390.147162.7781960.009060.1090840.708590.1090840.70859


## 4.2.3. Correlation Analysis of the First-Level Indicators of Liberal Arts Teaching

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the liberal arts data statistical results columns B1, B2, B3, and B4 into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables9–11 above, the binary linear regression equation of liberal arts teaching results can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 2.019 + 0.145 × 1 + 0.153 × 2 + 0.490 × 3. B1 means that when the teaching investment and teaching process scores are determined, for each additional point of teaching background, the teaching results increase by 0.145 points; b2 means that when the teaching background and teaching process scores are determined, for each additional point of teaching investment, the teaching results increase by 0.153 points; b3 means that when the teaching background and teaching investment scores are determined, each additional point in the teaching process will increase the teaching results by 0.490 points. The correlation coefficient of regression statistics is 0.8031, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.8031, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment and teaching process is 0.8031. The coefficient of determination is equal to 64.49% and the goodness of fit is equal to 64.49%, indicating that 64.49% of the changes in the evaluation of teaching results can be explained by the changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.645, which is acceptable. Given the significance level a = 0.05, F = 22.405 can be obtained by looking up the table, which is greater than F0.05(2, 38) = 3.24, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(41-3) = 2.024, the test statistic of b1 t1 = 1.291 < 2.024, the hypothesis of H0:β1 = 0 is not rejected; the test of b2 The statistic t2 = 1.438 < 2.024, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 3.259 > 2.024, rejects the hypothesis of H0:β3 = 0. Therefore, only the regression coefficient b3 is statistically significant, that is, in the correlation analysis of liberal arts data, only the impact of the teaching process on the teaching results is significant.Table 9
Correlation analysis table of first-level indicators of liberal arts teaching (1).
Regression statisticsMultiple R0.833103R square0.644974A adjusted R square0.616188Standard error0.566894Observations41Table 10
Correlation analysis table of first-level indicators of liberal arts teaching (2).
Variance analysisDfs sM SFSignificanceFRegression analysis321.601657.20055122.405881.93E − 08Residual3711.890650.321369Total4033.4923Table 11
Correlation analysis table of first-level indicators of liberal arts teaching (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept2.019190.655653.079680.003890.690723.347670.6907253.34767X variable 1 _0.145660.112741.291930.20439−0.082790.37411−0.082790.37411X variable 2 _0.153280.106561.438480.15870−0.062630.36920−0.062630.36920X variable 3 _0.490040.150353.259340.002390.185400.794680.1854040.79468


## 4.2.4. Correlation Analysis of First-Level Indicators of Teaching in Research Universities

Taking the teaching achievement as the dependent variableY, and the teaching background, teaching input, and teaching process as the independent variables, input the columns B1, B2, B3, and B4 of all the statistical results of the data into the EXCEL data analysis interface, select regression analysis, and output the following results.According to the results shown in Tables12–14 above, the binary linear regression equation of the teaching achievements of research universities can be directly written: Y = b0 + b1∗X1 + b2∗X2 + b3∗X3 = 0.650 + 0.399 × 1 + 0.138 × 2 + 0.389 × 3. B1 means that when teaching investment and teaching process scores are determined, for every one-point increase in teaching background, teaching results increase by 0.399 points; b2 means when teaching background and teaching process scores are determined, for every one-point increase in teaching investment, teaching results increase by 0.138 points; b3 means that when the teaching background and teaching investment scores are determined, for each additional point in the teaching process, the teaching results will increase by 0.389 points. The correlation coefficient of regression statistics is 0.806, indicating that when the three variables change, the degree of correlation between the dependent variable and the independent variable is 0.806, indicating that the multiple correlation coefficient of the three variables of teaching background, teaching investment, and teaching process is 0.806. The coefficient of determination is equal to 65.0% for the goodness of fit, indicating that 65.0% of the changes in the evaluation of teaching results can be explained by changes in the three factors of teaching background, teaching investment, and teaching process, and the remaining factors are random errors. The goodness of fit in this analysis is 0.65, which is acceptable. Given the significance level a = 0.05, F = 61.9212 can be obtained by looking up the table, which is greater than F0.05(2, 102) = 3.09, so the null hypothesis is rejected, indicating that the R2 of the sample is significant, and the established multiple linear regression model is valid. Because the overall relationship of the equation is significantly different from that each independent variable has a significant effect on the dependent variable, a significant t-test is also performed for each independent variable. Given the significance level a = 0.05, look up the table to get t0.05/2(105-3) = 1.984, the test statistic of b1 t1 = 4.740 > 1.984, reject the hypothesis of H0:β1 = 0; the test statistic of b2 The quantity t2 = 1.722 < 1.984, does not reject the hypothesis of H0:β2 = 0; the test statistic of b3 is 4.033 > 1.984, rejects the hypothesis of H0:β3 = 0. Therefore, the regression coefficients b1 and b3 are statistically significant, that is, in the correlation analysis of research university data, the influence of teaching background and teaching process on teaching results is significant. The data statistics and related analysis results of this chapter are summarized in the following Table 15:Table 12
Correlation analysis table of first-level indicators of teaching in research universities (1).
Regression statisticsMultiple R0.806263R square0.65006A adjusted R square0.639562Standard error0.884838Observations104Table 13
Correlation analysis table of first-level indicators of teaching in research universities (2).
Variance analysisDfs sM SFSignificanceFRegression analysis3145.441548.4804861.92121.03E − 22Residual10078.293840.782938Total103223.7353Table 14
Correlation analysis table of first-level indicators of teaching in research universities (3).
CoefficientsStandard errortStatP valueLower 95%Upper 95%Lower limit 95.0%Upper limit 95.0%intercept0.650020.465021.397830.16525−0.27251.57262−0.27251.57262X variable 1 _0.399620.084304.740047.09E0.232360.566890.232360.56689X variable 2 _0.138760.080541.722770.08802−0.02100.29857−0.02100.29857X variable 3 _0.389090.096454.033870.0001 00.197720.580460.197720.58046Table 15
Research-oriented teaching evaluation data analysis summary.
Teaching backgroundTeaching inputTeaching processTeaching achievementsTeaching evaluationScience7.293 (significant impact on teaching results)7.3886.8097.1937.102Engineering6.663 (significant impact on teaching results)6.841 (significant impact on teaching outcomes)6.252 (significant impact on teaching results)6.3786.445Liberal arts6.9075.7836.037 (significant impact on teaching results)6.8706.379Research university6.922 (significant impact on teaching results)6.5666.312 (significant impact on teaching outcomes)6.7846.590Remarks: Correlation analysis (regression) goodness of fit is greater than 0.6


## 5. Conclusion

On the basis of fully consulting teaching experts, this research selects 12 secondary indicators, uses the expert consultation method to collect experts’ judgment of the importance of the indicators, and uses the analytic hierarchy process to finally determine the indicator system. Then, this research uses this index system to study the teaching evaluation of research universities in Guangdong Province, describes the current research university teaching status, points out the problems existing in the current research university teaching, and analyzes the current situation of research university teaching on the basis of statistics and analysis data. The reasons for the current problems in the teaching of research universities. The whole research adheres to the principle of objectivity and authenticity. The CIPP model used in this study is an analysis model suitable for the research situation. The problem analysis and countermeasure suggestions are completely based on real data, and the results are authentic and credible. Due to the limitation of the professional quality of researchers and the number of consultants, the evaluation indicators proposed in this paper may not be all reasonable and will be used as a scope for further improvement in the future.



---

*Source: 3694744-2022-08-23.xml*