# A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

Authors: Chunfang Chen
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

In many universities and colleges, the government is now paying more attention to the quality of teaching assessment, and research on English teaching quality evaluation is becoming increasingly significant. The goal of this paper is to investigate how to use a fuzzy neural network to assess the quality of English education. The research of the model NN and the evaluation of the quality of English teaching is detailed first. A fuzzy NN is an algorithm that examines the quality of English instruction evaluation using fuzzy criteria. In the case results, the data show that the previous evaluation of quality teaching is between the intermediate and the intermediate level, while the research on the evaluation quality of English teaching based on fuzzy NN shows that the evaluation quality is between the intermediate and the advanced level. Therefore, the combination of Fuzzy Rules (FR) and NN methods can effectively improve the quality of college teachers’ English teaching evaluation. In college English teaching, FRs can simplify the analysis steps of evaluation of quality teaching, accurately judge the quality of college English teaching evaluation, and provide support for the improvement of evaluation of quality teaching.


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## Body

## 1. Introduction

Artificial intelligence is an interesting research domain, and this is due to its overwhelming characteristics, that is, enabling the devices (electronic or machines or robots) to perform those tasks which are only destined to be carried out by human beings. With the passage of time, this domain has been divided into various subbranches in order to improve the productivity and accuracy ratio of the developed systems. These systems have been extensively utilized in numerous domains of research, and various organizations have provided funding to support real-time projects like how the AI-enabled robot could be enabled to behave like ordinary human beings. Along those projects, AI has been continuously utilized in the education sector as well such as how computers or other devices could be enabled to understand these languages directly like human beings and answer questions; hence, computers could do that as well. Among these domains, the quality of the teaching in the institution is one of the possible areas where AI-enabled systems could have the potential to extend the teaching capabilities of the ordinary teacher. For this purpose, fuzzy NN and FRs were developed in the literature.In the process of judging the quality of college English teaching evaluation, previous methods of evaluation of quality of English teaching were limited to simple operations such as comparison and evaluation. In the previous evaluation of evaluation of quality teaching, it is difficult for university management to accurately judge the quality of English teaching evaluation, and the accuracy of the results is very low. Therefore, the use of FRs to judge the quality of university English teaching evaluation reduces the intensity and complexity of the quality judgment, improves the accuracy of the quality judgment of university English teaching evaluation, and provides a good basis for the quality examination of university teaching evaluation.Intelligent algorithms offer modern judgment methods for universities and colleges, improve the accuracy of judgment outcomes, and improve the accuracy of quality examinations of college English teaching. As a result, evaluating the quality of college English education is a methodical, regulated, and intelligent procedure. Under the reform of education, intelligent assessment of English teaching quality is an unavoidable prerequisite for English teaching innovation. It is vital to continually enhance the intellectualization level of the evaluation of English teaching quality in order to accomplish a good job in English teaching.The innovations of this paper are as follows: (1) explaining the theoretical knowledge of fuzzy NN and English evaluation of quality teaching research and using the fuzzy NN algorithm to analyze how the fuzzy NN plays a role in the research of English evaluation of quality teaching; (2) analyzing the previous ways of English teaching in universities and colleges and the research on the quality of English teaching evaluation based on fuzzy NN. The result of the case shows that the research on the quality of English teaching evaluation based on fuzzy NN can promote the development of English teaching in universities and colleges.The rest of the paper is organized as given as follows.Summary of the related works, which are already published, has been described in the upcoming section, that is, Related Work. In Section 3, we have described and presented the basic definition of the fuzzy NNs along with the mechanism of how the quality of English teaching is improved and what mechanisms are adopted. FR-enabled fuzzy NN is presented to resolve the aforementioned issue with minimum possible overheads. As the proposed mechanism is implemented, a whole section is dedicated to it for describing the observations of the various results. Finally, we have discussed the proposed approach in a comprehensive manner.


## 2. Related Work

At present, with the development of intelligent algorithms, the study of English evaluation of quality teaching is becoming more and more important. JacksiK holds that the evaluation of English evaluation of quality teaching plays an important role in every college and university, affecting the teaching effect, the efficiency of English learning, and other evaluation results of evaluation of quality teaching. The precision with which English teachers assess instruction quality needs to be considerably improved. Others have proposed an FR-based quality assessment system for English teaching evaluation, with the goal of analyzing English teachers’ activities in the evaluation of teaching quality. However, because the researcher has not specified the English teaching assessment quality examination system, it is impossible to know if it fits the requirements [1]. To improve the quality of English teaching assessment and examine the techniques for assessing the quality of English teaching assessment, LakhnoV provides a method for evaluating the quality of English teaching assessment with an intelligent support subsystem. However, the scholar has not been verified by a specific case, so the reliability and authenticity of this method are controversial [2]. BuczakA considers that it is a combination of machine learning and fuzzy NN methods to evaluate the quality of English teaching and achieves good results. However, it is also pointed out that machine learning and fuzzy NN methods have their own shortcomings and cannot be analyzed accurately, which makes the evaluation of English teaching more difficult. The views of the above-mentioned scholars are too vague to describe the process of evaluating the quality of English teaching and the specific measures [3]. SheuJJ analyzes the influencing factors of English teaching assessment quality and calculates the correlation between influencing factors by using FRs, so as to evaluate the quality of English teaching assessment more accurately. He believes that there is a close relationship between English teaching and quality assessment, but the main influencing factors and the degree of correlation are not explained. CzibulaIG puts forward the concept of combining FRs with NNs, which can be used as general rules to analyze the quality examination of English teaching assessment [4]. CzibulaIG proposed a fuzzy extension rule, proposed a more intelligent FR, such as Apriori’s algorithm, and combined the FR with the NN to analyze the quality examination of English teaching evaluation. However, the scholar did not carry out specific case validation and could not prove the stability of the method, so his conclusions are not persuasive [5]. Because FRs are commonly utilized in artificial intelligence algorithms, ZouC provides an algorithm based on FRs and evaluates the system’s performance using a case study. The NN’s computation outcome improves after the FRs are incorporated into the algorithm. Some researchers have not looked into specific situations or confirmed specific data [6]. Previous optimization methods, according to CanU, are inefficient in solving complex optimization issues. As a result, the quality of English instruction assessment is studied using the Iterative Search algorithm and fuzzy criteria. This algorithm can search for results that conform to FRs from the teaching evaluation database of English evaluation of quality teaching. In addition, the applicability of FRs is high, which can improve the research level of English evaluation of quality teaching. Under the study of FRs, the algorithm automatically adjusts the range value, which makes the FRs more flexible and more in line with the requirements of English evaluation of quality teaching research [7]. NasereddinH believes that the combination of FRs and NNs eliminates the need to repeatedly search the teaching assessment databases of English teaching assessment quality. And compared with the previous methods, FRs can save a lot of time. In summary, although scholars at home and abroad have put forward different ideas on the quality of English teaching assessment, they have not described the FRs and the NN model, nor have they conducted relevant case studies, which makes the results unreliable [8].


## 3. Basic Concepts of Fuzzy NN and the Study of English Evaluation of Quality Teaching

With the rapid development of intelligent algorithms and the increasing number of studies on the quality of English teaching evaluation, Chinese universities have entered the era of standardized evaluation of quality of English teaching [9]. In the process of intellectualized analysis in universities and colleges, the quality examination method of intellectualized teaching evaluation is indispensable. Intelligent analysis is shown in Figure 1.Figure 1
Intelligent analysis application diagram.As shown in Figure1, at present, modern evaluation of quality of English teaching theory appears, and intelligent analysis has been introduced into evaluation of quality of English teaching work in universities and colleges. The traditional analysis method is based on the previous experience of evaluation of the quality of English teaching method, while the intelligent method is based on the modern intelligent calculation theory of evaluation of the quality of English teaching method [10].Using the fuzzy NN algorithm, this paper conducts a comprehensive analysis of English teachers and teaching data, identifies regularities, conducts a better study on the evaluation of teaching quality in universities and colleges, improves the quality of teaching evaluation, and promotes the overall development of English teaching in universities and colleges [11]. Figure 2 depicts the fuzzy NN.Figure 2
Fuzzy NN structure diagram.The prior English teaching data is used to conduct a comprehensive analysis, as illustrated in Figure2. The gathering of vast volumes of data can enhance calculation accuracy spatially [12]. The vast volume of data could not be efficiently processed in the prior thorough analysis technique. Evaluation of quality of English teaching is still a classic analytic approach in universities and colleges, which has poor dependability and directly influences the quality of teaching evaluation. To successfully address these flaws, researchers must examine the quality of English education assessment studies using fuzzy NNs.The emergence of large amounts of data and the expansion of the Internet have caused a sharp increase in the number of teaching evaluation databases for evaluation of quality teaching, which makes teaching evaluation more and more intensive and difficult. The construction of fuzzy NN algorithm can discover critical data from massive data [13]. The flow of the fuzzy NN is shown in Figure 3.Figure 3
Fuzzy NN process.As shown in Figure3, the fuzzy NN not only helps English teachers extract critical data from the teaching assessment database but also helps them to conduct a comprehensive analysis of the data and predict future trends in teaching assessment quality examination [14].The quality of teaching evaluation involves interactive networks. The quality examination of teaching evaluation in universities and colleges should also keep up with the times in order to achieve the depth and breadth of college English teaching and to help relevant scholars continue their research and analysis [15]. The process of studying the quality of English teaching assessment based on fuzzy NN is shown in Figure 4.Figure 4
Research process of English teaching quality assessment.As shown in Figure4, assessors must promote the development of teaching quality examination in universities and colleges to improve the accuracy of results, evaluate the teaching quality according to the teaching requirements, and take appropriate safeguards to improve the effectiveness of college English teaching evaluation [16].A fuzzy NN is a comprehensive analytic method that combines the correlation between several evaluation indexes from a huge amount of data. English teachers are increasingly focusing on extensive analysis of relevant findings as the amount of data in the teaching assessment database grows.


## 4. FRs Based on Fuzzy NN

An extensive analysis method, qualitative and quantitative, is reported in this section which is specifically designed for the fuzzy NNs.


### 4.1. FRs Based on Fuzzy NN

FR is a calculation rule based on fuzzy concept, which can be used for quantitative and qualitative analysis. First, set the fuzzy set, the unit of the fuzzy set, and the number of fuzzy sets [17]. The FR is shown in Figure 5.Figure 5
FR structure.As shown in Figure5, the fuzzy degree Q of the FRs in any dataset D is the unit of teaching quantity that the teaching evaluation database meets the requirements, PDX∀Y is a probability calculation method, and the Q calculation formula is as follows: (1)Q=∑i=1nPDX∀Yi|t,where PDX∀Y represents any item in subset Di and X∀Y represents the proportion meeting the requirements in dataset D.The reliabilityh of FRs in the proportion meeting the requirements in D represents the amount of teaching with XX in D and 95% CI with y. Then, the calculation formula of HH is as follows:(2)C=limi⟶0PDYi|X|t∑i=1nPDYi|X|t,where limi⟶0PDYi|X|t represents any item of the data subset Di.RegardingSi, according to the probability PDX∀Y mentioned in the FR constraint, the evolution formula can be obtained as follows:(3)Si=limi⟶0fi∑mi∈1,2,…,n.The fuzziness of the itemsetX∀Y is recorded as G, and then (4)S=∑i=1nSi.Whens≥mins, the itemset X∀Y is a fuzzy set. According to formula (4), the fuzzy degree is used to express the projection of teaching quality examination. In special cases, it is more accurate to use the frequency to express the fuzzy degree projection [18], and the fuzzy degree projection is expressed as follows:(5)S⟶=f⟶1,f⟶2,…f⟶n.Whens≤mins, the itemset X∀Y is not a fuzzy set.Under FRs, the above algorithm does not reflect the fuzzy degree of itemsetX∀Y and can only provide the proportion of X∀Y [19]. From the perspective of FRs, S⟶ and C⟶ are consistent, and the teaching quality examination result of C⟶ is redundant. In view of the above problems of FRs, rerestrict S⟶ and C⟶, as described in the following formula:(6)SV=s⟶X∀Y1,s⟶X∀Y2,…,s⟶X∀Yn.Based on the above FRs, the corresponding projections and the results of teaching quality examination are significantly different from previous studies. Further explanation of the FRs is helpful to improve the results of teaching quality examination.


### 4.2. Multiangle FR Algorithm Based on Fuzzy NN

The main task of the multiangle fuzzy NN is to realize the multiangle evaluation of teaching quality, not only to achieve data preprocessing but also to determine the relationship between teaching quality and evaluation [20]. The multiangle fuzzy NN is shown in Figure 6.Figure 6
Multiangle fuzzy NN.As shown in Figure6, generally speaking, the results of teaching quality assessment can only be divided into one dimension, and the fuzzy NN can be divided into multiple dimensions to obtain the optimal set of teaching quality assessment [21].Whens≤mins and si⇒v2, use bisection function to analyze the minimum value, as shown in the following formula:(7)mins=Δs−12dsh−v2∂2d∂s∂v.From the teaching point of view, the Jaccard similarity coefficient is applied to the fuzzy NN as follows:(8)DETELX,Y=X∀Y∑iX∀Y.From formula (8), the similarity between X∧Y can be obtained, and the weighted average method is used to calculate the similarity likei estimation of each value, as shown in the following formula:(9)likei=∑dsh−v2⋅∑j=0mij.The basic idea of multiangle analysis is to determine the weights of different results of teaching quality examination, use sampling points to judge different weights, and get the corresponding threshold [22].xii=1,2,…,n is the sampling point of teaching quality examination results, ZAi is the observed value of teaching quality examination results, λi is the weight of xi pair, and A0 is the initial weight:(10)Zxi=∑i=1nλ0⇒ZAi−1.Use the weighted method to calculate the degree of fuzziness of teaching quality examination results, and filter the evaluation results that do not conform to the FRs as follows:(11)DEλi=maxdio−μ−Δdio−μ∑i=1nλi−1dio−μ,Not only the distance between the FR Ai and the initial weight A0 but also the correlation coefficient μ should be considered. The larger its value is, the greater the weight of the distance teaching quality examination result is.Compute the similarity between the datasets of teaching quality assessment results and construct a similarity matrix as follows:(12)Mij=JaccardA,B=x11⋯xin⋮⋱⋮xi1⋯xnn.Using the weighted average method, theotheri estimation value of the unknown ambiguity degree in M is shown as follows:(13)otheri=1m⋅∑j=1mxMij⋅supj.The complete analysis of the fuzzy NN algorithm is accomplished, and a comprehensive dataset is generated from the perspective of comprehensive analysis. It is more realistic in the process of teaching quality examination.Assume the teaching evaluation database contains teaching quality examinations that are stored. As indicated in the formula, the accuracy rate of teaching quality assessment is as follows:(14)reasultx=δxP∗100%.The confidence degree refers to the fact that the teaching inC includes the teaching quality examination of X and also includes B results. The condition is Pb|t which is recorded as cona|t as shown in the following formula:(15)ccona|t=Δporta|b∑porta∗100%,where reasultx is an important parameter for the implementation of FRs. The fuzzy NN’s important notions of fuzzy degree and confidence, which measure the value of teaching data, are fuzzy degree and confidence [23]. The term “result” refers to a parameter in FRs as well as two notions in the thorough analysis of linked rules. Rules for determining credibility are as follows.The data is divided inton blocks, and the global minimum fuzziness is equal to the sum of the local minimum fuzziness of n nodes. Thus, the local minimum fuzziness is equal to the product of the global minimum fuzziness and the number of teaching bars of the node data blocks, which is expressed as follows: (16)Lmin=Δmin⟶∗n∑BR.The degree of correlation between global and local extremes, expressed as quotient of the fuzzy degree count and expressed asWeight, is shown as follows:(17)Weight=k∑xiWeightx,where Weight is the global extremum probability of B, 1≤i≤x.When calculating results at each teaching quality examination point, local and global extremes are merged to form corresponding sets.First, the teaching evaluation databaseD traverses, searches all the teaching evaluation databases, constructs corresponding FRs, and forms the final x.set as follows:(18)x.set=∑xTi.In formula (18), i=1,2,…,n, n is the teaching evaluation standard, and ∑xTi is the comprehensive teaching evaluation standard.Calculate the fuzzy degree count and averageWeight of teaching evaluation, expressed by weight, as shown as follows:(19)Weight=xiWeight|t⇒∑minx.G,where x.G is the global result of x, which is also a special case of the local synthesis result and the final output result.


## 4.1. FRs Based on Fuzzy NN

FR is a calculation rule based on fuzzy concept, which can be used for quantitative and qualitative analysis. First, set the fuzzy set, the unit of the fuzzy set, and the number of fuzzy sets [17]. The FR is shown in Figure 5.Figure 5
FR structure.As shown in Figure5, the fuzzy degree Q of the FRs in any dataset D is the unit of teaching quantity that the teaching evaluation database meets the requirements, PDX∀Y is a probability calculation method, and the Q calculation formula is as follows: (1)Q=∑i=1nPDX∀Yi|t,where PDX∀Y represents any item in subset Di and X∀Y represents the proportion meeting the requirements in dataset D.The reliabilityh of FRs in the proportion meeting the requirements in D represents the amount of teaching with XX in D and 95% CI with y. Then, the calculation formula of HH is as follows:(2)C=limi⟶0PDYi|X|t∑i=1nPDYi|X|t,where limi⟶0PDYi|X|t represents any item of the data subset Di.RegardingSi, according to the probability PDX∀Y mentioned in the FR constraint, the evolution formula can be obtained as follows:(3)Si=limi⟶0fi∑mi∈1,2,…,n.The fuzziness of the itemsetX∀Y is recorded as G, and then (4)S=∑i=1nSi.Whens≥mins, the itemset X∀Y is a fuzzy set. According to formula (4), the fuzzy degree is used to express the projection of teaching quality examination. In special cases, it is more accurate to use the frequency to express the fuzzy degree projection [18], and the fuzzy degree projection is expressed as follows:(5)S⟶=f⟶1,f⟶2,…f⟶n.Whens≤mins, the itemset X∀Y is not a fuzzy set.Under FRs, the above algorithm does not reflect the fuzzy degree of itemsetX∀Y and can only provide the proportion of X∀Y [19]. From the perspective of FRs, S⟶ and C⟶ are consistent, and the teaching quality examination result of C⟶ is redundant. In view of the above problems of FRs, rerestrict S⟶ and C⟶, as described in the following formula:(6)SV=s⟶X∀Y1,s⟶X∀Y2,…,s⟶X∀Yn.Based on the above FRs, the corresponding projections and the results of teaching quality examination are significantly different from previous studies. Further explanation of the FRs is helpful to improve the results of teaching quality examination.


## 4.2. Multiangle FR Algorithm Based on Fuzzy NN

The main task of the multiangle fuzzy NN is to realize the multiangle evaluation of teaching quality, not only to achieve data preprocessing but also to determine the relationship between teaching quality and evaluation [20]. The multiangle fuzzy NN is shown in Figure 6.Figure 6
Multiangle fuzzy NN.As shown in Figure6, generally speaking, the results of teaching quality assessment can only be divided into one dimension, and the fuzzy NN can be divided into multiple dimensions to obtain the optimal set of teaching quality assessment [21].Whens≤mins and si⇒v2, use bisection function to analyze the minimum value, as shown in the following formula:(7)mins=Δs−12dsh−v2∂2d∂s∂v.From the teaching point of view, the Jaccard similarity coefficient is applied to the fuzzy NN as follows:(8)DETELX,Y=X∀Y∑iX∀Y.From formula (8), the similarity between X∧Y can be obtained, and the weighted average method is used to calculate the similarity likei estimation of each value, as shown in the following formula:(9)likei=∑dsh−v2⋅∑j=0mij.The basic idea of multiangle analysis is to determine the weights of different results of teaching quality examination, use sampling points to judge different weights, and get the corresponding threshold [22].xii=1,2,…,n is the sampling point of teaching quality examination results, ZAi is the observed value of teaching quality examination results, λi is the weight of xi pair, and A0 is the initial weight:(10)Zxi=∑i=1nλ0⇒ZAi−1.Use the weighted method to calculate the degree of fuzziness of teaching quality examination results, and filter the evaluation results that do not conform to the FRs as follows:(11)DEλi=maxdio−μ−Δdio−μ∑i=1nλi−1dio−μ,Not only the distance between the FR Ai and the initial weight A0 but also the correlation coefficient μ should be considered. The larger its value is, the greater the weight of the distance teaching quality examination result is.Compute the similarity between the datasets of teaching quality assessment results and construct a similarity matrix as follows:(12)Mij=JaccardA,B=x11⋯xin⋮⋱⋮xi1⋯xnn.Using the weighted average method, theotheri estimation value of the unknown ambiguity degree in M is shown as follows:(13)otheri=1m⋅∑j=1mxMij⋅supj.The complete analysis of the fuzzy NN algorithm is accomplished, and a comprehensive dataset is generated from the perspective of comprehensive analysis. It is more realistic in the process of teaching quality examination.Assume the teaching evaluation database contains teaching quality examinations that are stored. As indicated in the formula, the accuracy rate of teaching quality assessment is as follows:(14)reasultx=δxP∗100%.The confidence degree refers to the fact that the teaching inC includes the teaching quality examination of X and also includes B results. The condition is Pb|t which is recorded as cona|t as shown in the following formula:(15)ccona|t=Δporta|b∑porta∗100%,where reasultx is an important parameter for the implementation of FRs. The fuzzy NN’s important notions of fuzzy degree and confidence, which measure the value of teaching data, are fuzzy degree and confidence [23]. The term “result” refers to a parameter in FRs as well as two notions in the thorough analysis of linked rules. Rules for determining credibility are as follows.The data is divided inton blocks, and the global minimum fuzziness is equal to the sum of the local minimum fuzziness of n nodes. Thus, the local minimum fuzziness is equal to the product of the global minimum fuzziness and the number of teaching bars of the node data blocks, which is expressed as follows: (16)Lmin=Δmin⟶∗n∑BR.The degree of correlation between global and local extremes, expressed as quotient of the fuzzy degree count and expressed asWeight, is shown as follows:(17)Weight=k∑xiWeightx,where Weight is the global extremum probability of B, 1≤i≤x.When calculating results at each teaching quality examination point, local and global extremes are merged to form corresponding sets.First, the teaching evaluation databaseD traverses, searches all the teaching evaluation databases, constructs corresponding FRs, and forms the final x.set as follows:(18)x.set=∑xTi.In formula (18), i=1,2,…,n, n is the teaching evaluation standard, and ∑xTi is the comprehensive teaching evaluation standard.Calculate the fuzzy degree count and averageWeight of teaching evaluation, expressed by weight, as shown as follows:(19)Weight=xiWeight|t⇒∑minx.G,where x.G is the global result of x, which is also a special case of the local synthesis result and the final output result.


## 5. A Case Study on the Evaluation of English Teaching Quality

The motive of this research work is to improve the overall quality of the teaching, especially English, in universities and colleges. Moreover, the system is required to be evaluated extensively on the basis of numerous performance evaluation metrics, especially those which are important for the problem domain.


### 5.1. Analysis of Different Methods for College English Teaching Evaluation

The advantage of fuzzy NN is to comprehensively analyze the evaluation results of English teaching quality, obtain accurate evaluation results of teaching quality, improve teaching effect, obtain the relationship between the evaluation results of teaching quality and English teachers, and simplify the evaluation of teaching quality. The number of English majors in universities and colleges has doubled, and the cross development of different disciplines has led to the increasing requirements of students for teaching. How to evaluate the quality of English teaching has attracted more and more attention in universities and colleges. Universities and colleges must adapt to these changes, accurately evaluate the quality of English teaching, and put forward solutions. At present, universities and colleges tend to build intelligent comprehensive analysis to solve the above problems.This paper analyzes the development trend of intelligent comprehensive analysis teaching quality examination in universities and colleges in 2019 and 2020, as shown in Figure7.Figure 7
From 2019 to 2020, universities and colleges adopt the development trend of intelligent comprehensive analysis teaching quality examination.As indicated in Figure7, universities and colleges are raising their demands for teaching effectiveness, and they place a high value on English instruction. In the college English teaching reform, using intelligent ways to evaluate teaching quality will make the evaluation of teaching quality more efficient and scientific.This work compares the efficacy of past research on evaluating the quality of English teaching with that of evaluating the quality of English teaching using FRs, as shown in Figure8.Figure 8
Comparison of efficiency of two methods in English teaching quality examination.As shown in Figure8, it is very important in the field of education to make effective use of the potential knowledge of fuzzy NN accumulated in universities and colleges to help teaching quality evaluators make constructive decisions. Universities and colleges have accumulated massive data in the evaluation of education and teaching quality for many years. If we can make full use of the potential value of English teaching quality examination data, we can not only effectively improve the accuracy of English teaching quality examination results but also get better learning quality examination feedback, which will help to improve the standardization and scientificity of teaching quality examination in universities and colleges.


### 5.2. The Teaching Effect of NN Based on the FR Model in the Research of English Teaching Quality Assessment

At present, most of the development of fuzzy NN methods is based on the previous basis, so it takes a lot of time to thoroughly and comprehensively analyze the set of candidate items and to scan the huge teaching evaluation database repeatedly.This paper compares the performance of the improved FR algorithm and the improved FR algorithm in different datasets, as shown in Tables1 and 2.Table 1
English Teaching quality examination data before and after implementation of FRs algorithm.
AlgorithmDataset sizeMining efficiencyOperation hoursNumber of rulesAfter improvement321.74178.26113.04269.57295.65343.48195.65152.17130.43121.74343.48191.30330.43243.48156.52130.43230.43234.78191.30239.13Table 2
English Teaching quality examination data before and after implementation of FRs algorithm.
AlgorithmDataset sizeMining efficiencyOperation hoursNumber of rulesBefore improvement200.00186.96191.30152.1782.6165.2282.61213.0491.30143.48143.48143.48173.9195.6565.2295.6573.91104.35113.04117.39Tables1 and 2 show that the size of data in different datasets of the modified FR method ranges from 230.43 to 200.00 M, the number of rules ranges from 95.65 to 213.04 seconds, and the calculation time ranges from 82.61 to 343.48 seconds. In diverse datasets, the modified FR algorithm has data sizes ranging from 230.43 to 321.74 M, a number of rules ranging from 130.43 to 269.57 seconds, and a computation time ranging from 113.04 to 195.65. You can see that the improved FRs’ teaching effect is around 1% higher than that of the improved FRs, implying that different datasets have minimal impact on the improved FRs.To verify the reliability of this case, the performance of the improved FR algorithm and the improved FR algorithm under different fuzzy thresholds is also compared, as shown in Tables3 and 4.Table 3
Results of English teaching quality assessment before and after implementation of FR algorithm.
AlgorithmAccuracyErrorMaximumMinimum valueBefore improvement88.1786.0295.7096.7794.6295.7096.7794.6291.4088.1791.4090.3292.4789.2586.0293.5589.2588.1795.7096.77Table 4
Evaluation results of English teaching quality before and after the implementation of FR algorithm.
AlgorithmAccuracyErrorMaximumMinimum valueAfter improvement96.7796.7796.77100.0096.7797.85100.0097.85100.0098.9298.9296.77100.0096.77100.00100.0096.7798.9296.77100.00The data from Tables3 and 4 show that the maximum value under different fuzzy constraints of the improved FR algorithm is between 86.02 and 95.70, the accuracy is between 89.25% and 94.62 seconds, and the error rate is between 88.17 and 86.02. The maximum value of the improved FR algorithm in different datasets is 96.77–100.00, the accuracy is 96.77%–100.00 seconds, and the error rate is 96.77–98.92. It can be seen that the teaching effect of the improved FRs is about 19% higher than that of the improved FRs, so the different fuzzy thresholds have a great influence on the improved FRs.This paper compares the execution time and teaching effect of the FR algorithm before and after improvement, as shown in Figure9.Figure 9
Comparison of FR algorithms before and after improvement.By comparing the execution time with the teaching effect of the improved algorithm under the same amount of data and different degrees of ambiguity, it can be seen that the execution time of the improved FR algorithm increases with the increase of the number of data, and the execution time of the improved FR algorithm decreases with the increase of the number of data (Figure9). The improved FR algorithm takes less time to execute than the improved FR algorithm. However, as the volume of data grows, the improved FR algorithm’s teaching effect becomes less noticeable and even declines. However, with the increase in the number of data, the teaching effect of the improved FR algorithm is significantly improved.Intelligent and comprehensive analysis of teaching quality examination and evaluation has a great impact on college English teaching. The main impacts are as follows:(1)
It builds an effective platform, strengthens communication, and promotes communication. The most effective way to rationally implement the evaluation of teaching quality in universities and colleges is to communicate so as to establish a communication channel and mechanism that can correctly convey the opinions of English teachers and staff.(2)
It saves time and reduces costs. With the intelligent comprehensive analysis model, the steps can be effectively reduced. On the other hand, the intermediate link of teaching quality examination and evaluation based on intelligently comprehensive analysis can be sorted out in the shortest time to save time effectively.


## 5.1. Analysis of Different Methods for College English Teaching Evaluation

The advantage of fuzzy NN is to comprehensively analyze the evaluation results of English teaching quality, obtain accurate evaluation results of teaching quality, improve teaching effect, obtain the relationship between the evaluation results of teaching quality and English teachers, and simplify the evaluation of teaching quality. The number of English majors in universities and colleges has doubled, and the cross development of different disciplines has led to the increasing requirements of students for teaching. How to evaluate the quality of English teaching has attracted more and more attention in universities and colleges. Universities and colleges must adapt to these changes, accurately evaluate the quality of English teaching, and put forward solutions. At present, universities and colleges tend to build intelligent comprehensive analysis to solve the above problems.This paper analyzes the development trend of intelligent comprehensive analysis teaching quality examination in universities and colleges in 2019 and 2020, as shown in Figure7.Figure 7
From 2019 to 2020, universities and colleges adopt the development trend of intelligent comprehensive analysis teaching quality examination.As indicated in Figure7, universities and colleges are raising their demands for teaching effectiveness, and they place a high value on English instruction. In the college English teaching reform, using intelligent ways to evaluate teaching quality will make the evaluation of teaching quality more efficient and scientific.This work compares the efficacy of past research on evaluating the quality of English teaching with that of evaluating the quality of English teaching using FRs, as shown in Figure8.Figure 8
Comparison of efficiency of two methods in English teaching quality examination.As shown in Figure8, it is very important in the field of education to make effective use of the potential knowledge of fuzzy NN accumulated in universities and colleges to help teaching quality evaluators make constructive decisions. Universities and colleges have accumulated massive data in the evaluation of education and teaching quality for many years. If we can make full use of the potential value of English teaching quality examination data, we can not only effectively improve the accuracy of English teaching quality examination results but also get better learning quality examination feedback, which will help to improve the standardization and scientificity of teaching quality examination in universities and colleges.


## 5.2. The Teaching Effect of NN Based on the FR Model in the Research of English Teaching Quality Assessment

At present, most of the development of fuzzy NN methods is based on the previous basis, so it takes a lot of time to thoroughly and comprehensively analyze the set of candidate items and to scan the huge teaching evaluation database repeatedly.This paper compares the performance of the improved FR algorithm and the improved FR algorithm in different datasets, as shown in Tables1 and 2.Table 1
English Teaching quality examination data before and after implementation of FRs algorithm.
AlgorithmDataset sizeMining efficiencyOperation hoursNumber of rulesAfter improvement321.74178.26113.04269.57295.65343.48195.65152.17130.43121.74343.48191.30330.43243.48156.52130.43230.43234.78191.30239.13Table 2
English Teaching quality examination data before and after implementation of FRs algorithm.
AlgorithmDataset sizeMining efficiencyOperation hoursNumber of rulesBefore improvement200.00186.96191.30152.1782.6165.2282.61213.0491.30143.48143.48143.48173.9195.6565.2295.6573.91104.35113.04117.39Tables1 and 2 show that the size of data in different datasets of the modified FR method ranges from 230.43 to 200.00 M, the number of rules ranges from 95.65 to 213.04 seconds, and the calculation time ranges from 82.61 to 343.48 seconds. In diverse datasets, the modified FR algorithm has data sizes ranging from 230.43 to 321.74 M, a number of rules ranging from 130.43 to 269.57 seconds, and a computation time ranging from 113.04 to 195.65. You can see that the improved FRs’ teaching effect is around 1% higher than that of the improved FRs, implying that different datasets have minimal impact on the improved FRs.To verify the reliability of this case, the performance of the improved FR algorithm and the improved FR algorithm under different fuzzy thresholds is also compared, as shown in Tables3 and 4.Table 3
Results of English teaching quality assessment before and after implementation of FR algorithm.
AlgorithmAccuracyErrorMaximumMinimum valueBefore improvement88.1786.0295.7096.7794.6295.7096.7794.6291.4088.1791.4090.3292.4789.2586.0293.5589.2588.1795.7096.77Table 4
Evaluation results of English teaching quality before and after the implementation of FR algorithm.
AlgorithmAccuracyErrorMaximumMinimum valueAfter improvement96.7796.7796.77100.0096.7797.85100.0097.85100.0098.9298.9296.77100.0096.77100.00100.0096.7798.9296.77100.00The data from Tables3 and 4 show that the maximum value under different fuzzy constraints of the improved FR algorithm is between 86.02 and 95.70, the accuracy is between 89.25% and 94.62 seconds, and the error rate is between 88.17 and 86.02. The maximum value of the improved FR algorithm in different datasets is 96.77–100.00, the accuracy is 96.77%–100.00 seconds, and the error rate is 96.77–98.92. It can be seen that the teaching effect of the improved FRs is about 19% higher than that of the improved FRs, so the different fuzzy thresholds have a great influence on the improved FRs.This paper compares the execution time and teaching effect of the FR algorithm before and after improvement, as shown in Figure9.Figure 9
Comparison of FR algorithms before and after improvement.By comparing the execution time with the teaching effect of the improved algorithm under the same amount of data and different degrees of ambiguity, it can be seen that the execution time of the improved FR algorithm increases with the increase of the number of data, and the execution time of the improved FR algorithm decreases with the increase of the number of data (Figure9). The improved FR algorithm takes less time to execute than the improved FR algorithm. However, as the volume of data grows, the improved FR algorithm’s teaching effect becomes less noticeable and even declines. However, with the increase in the number of data, the teaching effect of the improved FR algorithm is significantly improved.Intelligent and comprehensive analysis of teaching quality examination and evaluation has a great impact on college English teaching. The main impacts are as follows:(1)
It builds an effective platform, strengthens communication, and promotes communication. The most effective way to rationally implement the evaluation of teaching quality in universities and colleges is to communicate so as to establish a communication channel and mechanism that can correctly convey the opinions of English teachers and staff.(2)
It saves time and reduces costs. With the intelligent comprehensive analysis model, the steps can be effectively reduced. On the other hand, the intermediate link of teaching quality examination and evaluation based on intelligently comprehensive analysis can be sorted out in the shortest time to save time effectively.


## 6. Conclusion

This paper mainly focuses on the study of fuzzy NN and English teaching quality examination, extends from FRs to fuzzy NN, and elaborates on the theoretical knowledge of fuzzy NN and its role in college English teaching. In the method section, this paper studies the FRs and the multiangle fuzzy NN algorithm and uses the algorithm for a case. In the case section, the author investigates the development trend of current research on English teaching quality assessment. The findings reveal that current research on English teaching quality assessment is gradually expanding, indicating that English teaching quality assessment research has a significant impact on universities and colleges. Previous college English teaching methods have a low teaching effect and waste a lot of time. The use of a fuzzy NN to evaluate the quality of English education makes the research process easier and improves the efficiency of college English instruction. As a result, in-depth comprehensive analysis based on association is critical in the analysis of English teaching quality examination research.



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